notebooks/stat_scm.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stationary SCM and DAG (STAT.) from figure 1 in paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from dcbo.examples.example_setups import setup_stat_scm\n",
    "from dcbo.utils.sequential_sampling import sequentially_sample_model\n",
    "from dcbo.utils.sem_utils.toy_sems import StationaryDependentSEM as StatSEM\n",
    "from dcbo.utils.plotting import plot_outcome\n",
    "from dcbo.utils.sem_utils.sem_estimate import build_sem_hat\n",
    "\n",
    "from numpy.random import seed\n",
    "\n",
    "# Models\n",
    "from dcbo.methods.bo import BO\n",
    "from dcbo.methods.cbo import CBO\n",
    "from dcbo.methods.abo import ABO\n",
    "from dcbo.methods.dcbo import DCBO\n",
    "\n",
    "seed(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample SEM to get observational samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
    "T = 3 # In this example, as in the paper, we consider three time-slices\n",
    "init_sem, sem, dag_view, G, exploration_sets, intervention_domain, true_objective_values, optimal_interventions, all_causal_effects  = setup_stat_scm(T=T)\n",
    "# This is just a snapshot of the DAG at hand\n",
    "dag_view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above DAG is the graphical structure we will be working with and is faithful to the one used in figure one in the paper. `dag` is a networkx object i.e."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "networkx.classes.multidigraph.MultiDiGraph"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and is the one we will use for exploring the optimization methods further down. `dag_view` is simply a convenient way to visualise the structure at hand and is just a string that we visulise using `pygraphviz`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digraph { rankdir=LR; X_0 -> Z_0; Z_0 -> Y_0; X_1 -> Z_1; Z_1 -> Y_1; X_2 -> Z_2; Z_2 -> Y_2;  X_0 -> X_1; Z_0 -> Z_1; Y_0 -> Y_1; X_1 -> X_2; Z_1 -> Z_2; Y_1 -> Y_2;  { rank=same; X_0 Z_0 Y_0 } { rank=same; X_1 Z_1 Y_1 } { rank=same; X_2 Z_2 Y_2 }  }\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(dag_view)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we make use of two types of structural equations models: `init_sem` and `sem`. The latter concerns interactions with the first time-slice in the DBN, which has no incoming edges from the previous time-slices, and is only active at $t=0$. For all other time-slices i.e. when $t>0$ the `sem` model is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of independent samples (samples here are the time-series on the horizontal) per time-index\n",
    "D_O = sequentially_sample_model(init_sem,\n",
    "                                sem,\n",
    "                                total_timesteps=T,\n",
    "                                sample_count=20, # How many samples we take per node in each time-slice\n",
    "                                epsilon=None) # If we employ a noise model or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate some interventional data\n",
    "\n",
    "from src.utils.utilities import powerset, get_monte_carlo_expectation\n",
    "from src.utils.sequential_intervention_functions import make_sequential_intervention_dict\n",
    "from numpy import zeros\n",
    "\n",
    "D_I = {k: None for k in powerset([\"X\", \"Z\"])}\n",
    "\"\"\"\n",
    "do(Z_0)\n",
    "\"\"\"\n",
    "interv = make_sequential_intervention_dict(G,T)\n",
    "# Univariate intervention at time 0\n",
    "interv[\"Z\"][0] = 1.0\n",
    "static_noise_model = {k: zeros(T) for k in [\"X\", \"Z\", \"Y\"]}\n",
    "# Sample this model with one intervention, and no noise\n",
    "intervention_samples = sequentially_sample_model(\n",
    "    init_sem,\n",
    "    sem,\n",
    "    total_timesteps=T,\n",
    "    interventions=interv,\n",
    "    sample_count=1,\n",
    "    epsilon=static_noise_model\n",
    ")\n",
    "\n",
    "# Note: we obvs don't need to take MC samples if noise-model is zero\n",
    "D_I[(\"Z\",)] = get_monte_carlo_expectation(intervention_samples)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above observation samples contained in `D_obs` are sequential in the sense that the sample function generates each column per variable, conditional on the column samples at $t-1$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other setup parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exploration sets: [('X',), ('Z',), ('X', 'Z')]\n",
      "Intervention domains: {'X': [-4, 1], 'Z': [-3, 3]}\n",
      "True optimal outcome values: ['y^*_0 = -2.152', 'y^*_1 = -4.304', 'y^*_2 = -6.455']\n"
     ]
    }
   ],
   "source": [
    "# Contains the exploration sets we will be investigating\n",
    "print(\"Exploration sets:\", exploration_sets)\n",
    "# The intervention domains for the manipulative variables\n",
    "print(\"Intervention domains:\", intervention_domain) \n",
    "# The true outcome values of Y given an optimal intervention on the three time-slices\n",
    "print(\"True optimal outcome values:\", [r\"y^*_{} = {}\".format(t,val.round(3)) for t,val in enumerate(true_objective_values)])\n",
    "# Number of trials\n",
    "N = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore optimization methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In all these examples we do not employ any interventional data, just observational."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vanilla Bayesian Optimization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Time index:   0%|          | 0/3 [00:00<?, ?it/s]reconstraining parameters GP_regression.rbf.variance\n",
      "Time index:  33%|███▎      | 1/3 [00:00<00:00,  3.73it/s]reconstraining parameters GP_regression.rbf.variance\n",
      "Time index:  67%|██████▋   | 2/3 [00:00<00:00,  3.65it/s]reconstraining parameters GP_regression.rbf.variance\n",
      "Time index: 100%|██████████| 3/3 [00:00<00:00,  3.94it/s]\n"
     ]
    }
   ],
   "source": [
    "# Setup the input parameters to be used with the Bayesian optimization\n",
    "BO_input_params = {\n",
    "    \"G\":G,\n",
    "    \"sem\": StatSEM,\n",
    "    \"base_target_variable\": \"Y\",\n",
    "    \"observation_samples\": D_O, # Observational samples\n",
    "    \"intervention_domain\": intervention_domain,\n",
    "    \"intervention_samples\":None,   \n",
    "    \"number_of_trials\": N,\n",
    "    \"optimal_assigned_blankets\": None,\n",
    "    \"sample_anchor_points\": True,\n",
    "    \"seed_anchor_points\": 1,\n",
    "    \"change_points\": None\n",
    "}\n",
    "# Run Bayesian optimization\n",
    "bo = BO(**BO_input_params)\n",
    "bo.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X': [-1.5005874958722, -3.9271251875729014, -1.0247396789687988],\n",
       " 'Y': [-2.111148824950811, -4.26018454126298, -6.103437908573766],\n",
       " 'Z': [-2.8506046346979117, -2.9864445988889186, 2.941345628240498]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bo.assigned_blanket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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u/kt3v8fdH3F33SIkaVFckMtnF01l7qRiOrp6+N36g6zac1SdO0UywFCPaL4N3GdmPw/GERMZNrnZWXxk3iTeO6sMd3h1RyPPbjpMZ3dP2NFEZABDKjTuvtbda4CngGfN7HtmpusyMmzMjCUzJ/DxKyaTl5PFtsMt/MvqA7ScVudOkaga8jWaYI6XWuAnwNeBHWb2xVQHExnIrIoi/nRxNePG5FLfHBsB+tCJU2HHEpEEhnqN5j+BOuBeYAqx/jVLgSVm9kCqw4kMpKwon5uXTGNq6Rja2rv59eoDbDnYHHYsEeljqINqLgO2+LuvwH7dzLamKJNI0sbkZfPpBVP5/fZYX5vnNh+mqa2d915YRlZIM3eKyDsN9RrN5gRFptdHU5BHZMiys4xrL6rk2osqyDJj9Z5j/Hb9QU53docdTURI4ejNwdD9583MPmtmm82sx8wSzkNtZnPNbF3co9nMbg/W3WVmdXHrbkxFLom+K6rH8+kFUxiTl83bjW38atV+jrV1hB1LZNSL4jQBm4BPE5uWOSF3r3X3+e4+H1gInOSdc9jc27ve3Z9JuBEZkaonjOXmxdMoK8rjaFsHj63ax96mtrBjiYxqkSs0wZA3tUN4y3XALnffm65MklnGjc3lTxZXc0F5Ie2dPfzbWwd5a98xde4UCUnkCs05+BzvHsjzNjPbYGYPmllpGKEkXPk52XziislcNXMCPe6srD3Ci1sbNAK0SAgsjL/yzOxFYFKCVXe4+5NBm5XAt9x99QDbyQMOApe6e32wrBJoBBy4G6hy9y/38/5lxO6ko7y8fOHy5cvPeZ+GS2trK0VF0Z6DLmoZD7T0sLahi+4emDjGuGpSDvk5Frmc/VHO1FLO1KqpqVnj7gmvp/ca6u3NKeHu16doUx8B1vYWmWDbZ56b2T8RG8WgvxwPAA8AzJ0715cuXZqiWOmzcuVKop4zihk/2Hya360/SMvpLvbn5/CJ+ZPZsub1yOVMJIrfz0SUM7UyJWcyMv3U2c30OW3WZwy2TxG7uUBGucqSAj63ZBpV4wpoOd3F8lX7qWvp0XUbkWEQuUJjZp8yswPANcDTZvZcsHyymT0T164Q+CDwmz6b+KGZbTSzDUAN8M1hii4RV5Sfw2cWTuXiqhI6u503D3fxi9f28tquJpo0x41I2oRy6mwg7v4E77xVuXf5QWLz3vS+bgMmJmincdekXznZWXz40koqSvI5tH8PR9s6eH13E6/vbqKsOJ85FUXMqSymtDAv7KgiI0bkCo1IupkZC6aVcnxmLhdePoXt9a3sbGilsaWdxpZ2/rCriYqSfOZUFjOnophxY3PDjiyS0VRoZNTKMmP6xEKmTyzk2osq2Hf0JLWHW9h1pJWG5nYamtt5dUcjk8YVMKeymNmVRZQUqOiIDJUKjQix8dJmlhUys6yQru4e9jSdZEd9C7sb2zh84jSHT5zmle1HmDJ+DLMri5hdWUxRvv77iCRD/1NE+sjJzmJWRRGzKoro7O5hT2MbtfUt7Glso+74KeqOn+L3QdGZO6mYWRVFjM3TfyWR/uh/h8gAcrOzmF1ZzOzKYjq6etjd2Mr2+lb2NLZx4NgpDhw7xYptR6ieMIY5lbGiU5CbHXZskUhRoRFJUl5OFhdNKuGiSSWc7uxm95E2tte3sLfp5JnHS1sbmD5xLLMri7iwXEVHBFRoRM5JQW42l0wu4ZLJsaKzs6GV7fUt7D96ircb23i7sY3srFjRmTupmJllheTnqOjI6KRCI3KeCnKzmTdlHPOmjONkR1dQdFo5cOwku4+0sftIGzlZxszyQuZUxopObnbk+kqLpI0KjUgKjc3L4fKp47l86nja2rvY0dDK9sMt1B0/xY76VnbUt5KXk8XMsljRmTFxLDkqOjLCqdCIpElhfg7zq8czv3o8Lac72V7fyo76Fg6dOE3t4RZqD7eQl5PFheVFzKksYvrEQrKzLOzYIimnQiMyDIoLclk4vZSF00s5caqTHfUt1Na30NDcztZDzWw91ExBbjYXBqfXqieMVdGREUOFRmSYjRuTy6IZE1g0YwLH2jrYXt/C9mAInM0Hm9l8sJkxednMKi9i7qRiejTCtGQ4FRqREJUW5nHVBRO56oKJNLW2s70+dvfa0bYONtadYGPdCfbt7WRTz46wow6q/kAnBbubuLiqhHFjNFSPnKVCIxIRE4vyuaYon6svmEBja3CkU9/CHicjpqBu7XBe29XEa7uaqJ4wlouripldUUxejm52GO1UaEQixswoL86nvDif984q4+WOPfzRH80OO9aA3J1/ffYAJZOK2dnQyv6jJ9l/9CQra48wq6KIS6pKmFo6BjNddxqNIldozOzvgY8DHcAu4L+4+/EE7W4A/l8gG/ipu98TLJ8JPE5srpo1wBfdvWN40oukXpZZBtwYYFQWZrH0sipOd3azo76VrYeaqTt+ii0Hm9lysJmSMblcXFXMJVUljB+r+X5Gkyge074AzHP3y4HtwHf7NjCzbODHwEeAS4CbzeySYPXfAfe6+yzgGPBfhyW1iACxDqyXTR3Hnyyu5tb3zOCqmRMoLsih+VQnb+w+ys//cw/LV+1nU90JTnd2hx1XhkHkjmjc/fm4l68Dn0nQbAmw0913A5jZ48BNZrYVuBb4fNDuYeAu4CdpCywi/SotzOM9s8q45sKJHDh2is0Hm9nZ0HJmFOwV2xpip9Yml1BdOpasyB+5ybkwj/Ctk2b2O+BX7v7LPss/A9zg7l8JXn8RuIpYUXk9OJrBzKqBf3f3ef1sfxmwDKC8vHzh8uXL07UrKdPa2kpRUVHYMQaUCRlBOVMt2ZydPc7B1h72NffQeOrs758xOUZ1cRbTSrIozktfwRlp38+w1dTUrHH3RQO1CeWIxsxeBCYlWHWHuz8ZtLkD6AIeSVcOd38AeABg7ty5vnTp0nR9VMqsXLmSqOfMhIygnKl2LjlPnOxk6+HYNZwTpzrpAHYCVWMLuLiqhLmTilM+AvZI/n5GVSiFxt2vH2i9md0KfAy4zhMfctUB1XGvpwbLmoDxZpbj7l1xy0UkgsaNzeXqCyZy1cwJ1B0/xdZDsVu6D504zaETp/n99iNcWF7ExVXFzJhYqFNrGSpy12iCu8n+Gvgjdz/ZT7NVwOzgDrM64HPA593dzWwFses6jwO3AE8OQ2wROQ9mxtTSsUwtHcvSueXsbIjdtbbv6Mkz/YkK87O5aFIJF1eVUF6cH3ZkGYLIFRrgfiAfeCG45/51d//vZjaZ2G3MN7p7l5ndBjxH7PbmB919c/D+bwOPm9nfAm8BPxv+XRCRc5WbncXFVbGC0ny6k22HWth6qJmjbR2s2XuMNXuPUVGSzyVVsUnoxuRpnp+oi1yh6b2Qn2D5QeDGuNfPAM8kaLeb2F1pIpLhSgpyWTJzAotnlHK4+TRbDjafGYy0ofkIr2xvZGZ5IZdUlTCzTKNfR1XkCo2ISF9mRtW4MVSNG8MfzSlnd2MbWw42s6epjV0NrexqaGVMXjZzJxVzaXBqTaMQRIcKjYhklJzsLOZUFjOnspjW9i5qg7vWGls7WLfvOOv2HaesKI9LJsdOrRXm69dc2PQvICIZqyg/h4XTJ7BgWikNLe1sOdRM7eEWGls7eGV7I6/uaGJG2VgurirhgrJCzWYaEhUaEcl4ZkZlSQGVJQV8YHY5bze2suVQC28faWN38MjPzeKiScXsO9HNproTYUce1J4MyZkMFRoRGVGys4xZFcXMqijmZEcX2w7H7lpraG5n/f4T7Gno5tiW+rBjDipTciZDhUZERqyxeTksmFbKgmmlHGlpp/ZwC350H5dOLgk72qD8aFZG5EyGCo2IjAq9c/x01eWw9NJEI2BFS96RbRmRMxm6MiYiImmlI5pAbnMzfPzjZxfce2/s6ze/eXbZzTfD5z8Pt9wCR4/Gll14Idx3H9x/Pzz33Nm2Dz8MO3fC3XefXfa1r8ENN7zzcxYvhjvvhO9/H1atOrv8d7+DZ5+FH//47LK/+Rty+ub88Ifhttvg9tth167YsgkTYp//6KPw2GPDvk/Ty8pg6dKk94lZs2KfP8z7NK+pCZ56Ki3/Tqncp3lNTbH3hvyzN9g+zWtqgtmzQ/3ZS2af5jU1wcSJof7sJbNPZ3KG+LOX1D4lIdLTBAynuXPnem1tbdgxBpUJI7pmQkZQzlRTztTKlJxmNug0ATp1JiIiaaVCIyIiaaVCIyIiaaWbAQL19fUsWjTgacZIaGtro7CwMOwYA8qEjKCcqaacqZUpOYEFgzVQoQlUVlayevXqsGMMKhMuEGZCRlDOVFPO1MqUnGZ2arA2OnUmIiJppUIjIiJppUITON0ddgIRkYx0ZLAGKjSB9i6nq7sn7BgiIpmmcbAGkSs0Zna3mW0ws3Vm9ryZTU7QZr6ZvWZmm4O2fxq37iEzezt4/zozm5/M5zpQW9+Suh0REREggoUG+Ht3v9zd5wNPAXcmaHMS+JK7XwrcANxnZuPj1v+Vu88PHuuS/eC1+46jIXlERFIrcoXG3ZvjXhYSO9jo22a7u+8Inh8EGoDy8/lcAxpb2tl/dNA79UREZAgiOaimmf0A+BJwAqhx934vNpnZEuBh4FJ37zGzh4BrgHbgJeA77t7ez3uXAcsAJlRULfzC3z5EZWEW75kc3e5Fra2tFBUVhR1jQJmQEZQz1ZQztTIlZ01NzaCDauLuw/4AXgQ2JXjc1Kfdd4H/NcB2qoBa4Oo+ywzIJ1aA7kwm0+w5c/wfX9ruP3q+1htbTntUrVixIuwIg8qEjO7KmWrKmVqZkhNY7YP8fg3l1Jm7X+/u8xI8nuzT9BHgjxNtw8xKgKeBO9z99bhtHwr2vx34ObAkmUwGXFwVmzb1rX3Hh7xPIiKSWOSu0ZjZ7LiXNwHbErTJA54AfuHuv+6zrir4asAniR0pJeXKaaUAbD3UzMmOrqFGFxGRBCJXaIB7zGyTmW0APgR8A8DMFpnZT4M2fwJ8ALg1wW3Mj5jZRmAjUAb8bbIfPKEwjwvKC+nqcTYcOJGq/RERGdUid9Xb3ROeKnP31cBXgue/BH7ZT7trz+fzF0wrZfeRNtbvP86i6aXkZEexFouIZA79Fu1jaukYyovzOdnRzbbD6sApInK+VGj6MDMWBNdq3tp3TB04RUTOkwpNAnMnFVOUn0Njawf7jp4MO46ISEZToUkgO8u4ono8AGv3HQs3jIhIhlOh6cdlU8aRm23saTxJY2vCgQVERCQJKjT9GJOXzSWT1YFTROR8qdAM4Mrq2E0B29SBU0TknKnQDKA0rgPn+v3qwCkici5UaAbRe6vzhgPHNQOniMg5UKEZxNTSMVSUqAOniMi5UqEZhDpwioicHxWaJMypVAdOEZFzpUKTBHXgFBE5d5ErNGZ2t5ltCIb+f97MJvfTrjtuioDfxi2faWZvmNlOM/tVMHfNebt8qjpwioici8gVGuDv3f1yd58PPAXc2U+7U+4+P3h8Im753wH3uvss4BjwX1MRqiBXHThFRM5F5AqNuzfHvSwEkr76HsyqeS3QO+vmw8Rm2UyJK6tLMVMHThGRoYhcoQEwsx+Y2X7gz+j/iKbAzFab2etm9slg2UTguLv3VoEDwJRU5SotzGNmmTpwiogMhYVxu66ZvQhMSrDqDnd/Mq7dd4ECd/9egm1Mcfc6M7sAeBm4DjgBvB6cNsPMqoF/d/d5/eRYBiwDKC8vX7h8+fJBsx852cOrdV3k58CHp+eSnWWDvieVWltbKSoqGtbPHKpMyAjKmWrKmVqZkrOmpmaNuy8asJG7R/YBTAM2JdHuIeAzgAGNQE6w/BrguWQ+a86cOZ6Mnp4e/+Xre/xHz9f6xgPHk3pPKq1YsWLYP3OoMiGju3KmmnKmVqbkBFb7IL9fI3fqzMxmx728CdiWoE2pmeUHz8uA9wJbgp1eQazoANwCPNn3/eeZ70wHzrXqwCkiMqjIFRrgHjPbZGYbgA8B3wAws0Vm9tOgzcXAajNbT6yw3OPuW4J13wb+wsx2Ertm87NUB+ztwNnU2sHeJnXgFBEZSE7YAfpy9z/uZ/lq4CvB8z8Al/XTbjewJG0BiXXgnD9tPK/uaGTtvmPMKCtM58eJiGS0KB7RZITeGTj3NqkDp4jIQFRozlFBbjaXTh4HwNq9GpZGRKQ/KjTnYX71eMyg9nALbe3qwCkikogKzXmIzcBZRFePs+GAOnCKiCSiQnOeFkwbD8Rm4OzUDJwiIu+iQnOepowfQ2VJASc7uqnVDJwiIu+iQnOezIwrg6MadeAUEXk3FZoUmFNZTHGBOnCKiCSiQpMCmoFTRKR/KjQpEt+B80iLOnCKiPRSoUmR+A6cb+moRkTkDBWaFLpyWqwD5zZ14BQROUOFJoXGj4114OzucdYfOB52HBGRSBi00JhZYfA1+lO9RcDZDpwn1IFTRITkjmhKzew24H3pDgNgZneb2QYzW2dmz5vZ5ARtaoL1vY/TZvbJYN1DZvZ23Lr5w5G7V28HzlMd3Ww7pA6cIiLJFJrrgFuBC8ysYqCGZvbtFGT6e3e/3N3nA08Bd/Zt4O4r3H1+0OZa4CTwfFyTv+pd7+7rUpApaWbGgunjAXhrvzpwiogkM/HZm8CXgWp3b4hfYWbL418C84G/O59A7t4c97IQGOw39WeAf3f3yPSUnF1RzKsFjTS1drCn6SQzNTGaiIxidj5/cZvZT939K3Gvf+LuXz3vUGY/AL4EnABq3P3IAG1fBn7k7k8Frx8CrgHagZeA77h7wo4tZrYMWAZQXl6+cPny5YmanZMdx7rZ1NhNxVjjvVNyU7bd1tZWioqifbksEzKCcqaacqZWpuSsqalZ4+6LBmzk7uf8AGb2eT0hyfe9CGxK8LipT7vvAv9rgO1UAUeA3D7LDMgHHgbuTCbTnDlzPJVOdXT5/S/v8B89X+sNzadTtt0VK1akbFvpkgkZ3ZUz1ZQztTIlJ7DaB/n9OqTbm83sBTO7Iq5Ivd2naB1NZjvufr27z0vweLJP00eAPx5gU38CPOHunXHbPhTsfzvwc2BJMplSrSA3m0smlwDqwCkio9tQ+9F8G7jPzH5uZlXpCGRms+Ne3gRsG6D5zcBjfd5fFXw14JPEjpRCcWW1OnCKiAyp0Lj7WnevIXY32LNm9j0zG5PiTPeY2SYz2wB8CPgGgJktMrOf9jYysxlANfD7Pu9/xMw2AhuBMuBvU5wvaePH5nGhOnCKyCiXzF1n7xAcKdQCPyH2S/zPzey77v7PqQjk7glPlbn7auArca/3AFMStLs2FTlSZcH0UnY2tLLhwAkWz5hAbrYGYxCR0WWo12j+E6gD7iX2S/5WYCmwxMweSHW4kWDyuAJ14BSRUW2oRzTLgC3BnQbxvm5mW1OUaUTp7cD57xsPs3bfMeZNKSF2UCgiMjoM9RrN5gRFptdHU5BnRJpdEZuB82hbrAOniMhokrILBu6+O1XbGmmys4z5vTNw7tWtziIyuujK9DCZN2UceTlZ7DuqGThFZHRRoRkm8R0416oDp4iMIio0w2hBdSlmUHu4hVZ14BSRUUKFZhiNG5t7pgPnhv3Hw44jIjIsVGiG2YLppQBsqNMMnCIyOqjQDLPJ4wqYNC7WgXProebB3yAikuFUaIaZmbFgWuyo5q19xzUDp4iMeCo0IZhVUXSmA+fbjW1hxxERSSsVmhBkZxlXThsPxI5qRERGssgWGjP7SzNzMyvrZ/0tZrYjeNwSt3yhmW00s51m9r8togOLXTr5bAfOhpbTYccREUmbSBYaM6smNhfNvn7WTwC+B1xFbAbN75lZabD6J8CfA7ODxw1pD3wOCnKzufTMDJzHww0jIpJGkSw0xKYh+GugvyvlHwZecPej7n4MeAG4IZhds8TdXw8G//wFsVk2I+lKdeAUkVEgcoXGzG4C6tx9/QDNpgD7414fCJZNCZ73XR5J48bmMqtCHThFZGQb8gybqWBmLwKTEqy6A/ifxE6bDUeOZcTm2KG8vJyVK1cOx8e+Q/upHvYc6OLg/j2c3JdLTtbAl5RaW1tDyTkUmZARlDPVlDO1MiVnMkIpNO5+faLlZnYZMBNYH1zDnwqsNbMl7n44rmkdsZk9e00FVgbLp/ZZXjdAjgeABwDmzp3rS5cu7a9p2rg7Hav2c/jEaSrmVHD51PEDtl+5ciVh5ByKTMgIyplqyplamZIzGZE6debuG929wt1nuPsMYqe+FvQpMgDPAR8ys9LgJoAPAc+5+yGg2cyuDu42+xLw5HDuw1DFd+Bcu/eYOnCKyIgTqUIzEDNbZGY/BXD3o8DdwKrg8f1gGcD/AH4K7AR2Af8eQtwhmR104Dx2slMdOEVkxAnl1FmygqOa3uerga/EvX4QeDDBe1YD84YjX6pkBR04X9neyNp9x7mgvCjsSCIiKZMxRzQjXW8Hzv3qwCkiI4wKTUTEd+Bcu/d4uGFERFJIhSZCejtwbq9XB04RGTlUaCIkvgPnenXgFJERQoUmYnpvdd5w4AQdXZqBU0QynwpNxFSNK6BqXAGnOzUDp4iMDCo0EWNmLJjeOwOnOnCKSOZToYmgWeXqwCkiI4cKTQTFOnAGw9JorhoRyXAqNBF16eSSsx04m9WBU0QylwpNRBXkZjNvyjhARzUiktlUaCJsfvX4MzNwtpzuDDuOiMg5UaGJsHFjcpldUUyPOxsOnAg7jojIOVGhibgrp40H1IFTRDJXZAuNmf2lmbmZlSVYN9/MXjOzzWa2wcz+NG7dQ2b2tpmtCx7zhzV4ik0eP0YdOEUko0Wy0JhZNbFZM/f10+Qk8CV3vxS4AbjPzMbHrf8rd58fPNalNewwUAdOEclkUZ347F7gr+lnGmZ33x73/KCZNQDlwPFhSTfMZpUXUTIml2MnOznc2E3H5r4zW0fLpvou2iOeEWI5O7fUk2WQZYYFX2OP2CgNWRbr13T2tQ3ePlhmce9Nun2f7cdmJBfJbBa1v5DN7CbgWnf/hpntARa5e+MA7ZcADwOXunuPmT0EXAO0Ay8B33H39n7euwxYBlBeXr5w+fLlKd2XVNp5vJuNR7rp6OggLy8v7DgDyoSMkBk5syyWMz//nTkteJx50e8yo88i4muXxX+1dy6Lb/uOZQm2A9DR3k5+fv4AexMN7cqZUnd96UNr3H3RQG1COaIxsxeBSQlW3QH8T2KnzZLZThXwz8At7t57pfy7wGEgD3gA+Dbw/UTvd/cHgjbMnTvXly5dmvxODLMP9Di7jrTyhzfeZOHCAf9NQ7dmzerIZ4RYzgULFtHjHjzAg6+9y/zMc4LXTk8PQ2ofWxb3fu/7/vj20NNz9jXAnj17qJ42I9xvVhL27NlD+aTpYccYlHIOv1AKjbtfn2i5mV0GzATWB6cMpgJrzWyJux/u07YEeBq4w91fj9v2oeBpu5n9HPhWGnZh2GVlGbMri6krOduRM6oad0Q/I8RyXjY1ujl7i9SKlXV84I9mBcuCdfg7Xvddd/Z53Lb6tMPfvR0P2r7zvWfb8Y527/y817oOcM3i6vPZ5WGhnKn1F0m0idQ1GnffCFT0vu7v1JmZ5QFPAL9w91/3WVfl7ocsVqk+CWxKd26RdLDgOk12lpGbHcn7dt5h4pgsJo8fE3aMQSnn8Iv+T2/AzBaZ2U+Dl38CfAC4NcFtzI+Y2UZgI1AG/O3wpxURkV6ROqLpy91nxD1fDXwleP5L4Jf9vOfac/ms3OZm+PjHzy64997Y129+8+yym2+Gz38ebrkFjh6NLbvwQrjvPrj/fnjuubNtH34Ydu6Eu+8+u+xrX4Mbbnjn5yxeDHfeCd//PqxadXb5734Hzz4LP/7x2WV/8zfk9M354Q/DbbfB7bfDrl2xZRMmxD7/0UfhsceGfZ+ml5XB0qVJ7xOzZsU+f5j3aV5TEzz1VFr+nVK5T/OammLvDflnb7B9mtfUBLNnh/qzl8w+zWtqgokTQ/3ZS2afzuQM8WcvqX1KQuTuOgvL3Llzvba2NuwYg1q5ciVRvmkBMiMjKGeqKWdqZUpOMxv0rrOMOXUmIiKZSYVGRETSSoVGRETSKtI3Awyn+vp6Fi2KfifDtrY2CgsLw44xoEzICMqZasqZWpmSE1gwWAMVmkBlZSWrV68OO8agMuECYSZkBOVMNeVMrUzJaWanBmujU2ciIpJWKjQB3eQtIpIeKjSBU10qNSIi5+DIYA1UaAId3VB3fNBTjSIi8k79TuPSS4Umzstb6+nu0ZGNiEgqqdAEsgwaWztYu+9Y2FFEREYUFZrA2JzYdIGv72rixMnOkNOIiIwcKjSBnCy4aFIxXT3Oy7X1aLBREZHUUKGJ84E55eTnZrGn8SQ7GlrDjiMiMiKo0MQpzM/h/bPKAVhZ28Dpzu6QE4mIZD4Vmj7mTSlh8vgC2tq7+cOuQe/aExGRQajQ9GFmXHtRJVlmbDhwgkMn1LdGROR8RK7QmNldZlZnZuuCx439tNtjZhuDNqvjlk8wsxfMbEfwtXSoGcqL81k4vRR3eGlrAz3qWyMics4iV2gC97r7/ODxzADtaoI28eP7fwd4yd1nAy8Fr4fsqgsmUDImlyMt7by1X31rRETOVVQLzfm4CXg4eP4w8Mlz2UhudhbXXlQBwGu7mjhxSn1rRETORVQLzW1mtsHMHhzg1JcDz5vZGjNbFre80t0PBc8PA5XnGmJmWSFzKovp7HZW1jaob42IyDmwMH55mtmLwKQEq+4AXic2SJsDdwNV7v7lBNuY4u51ZlYBvAB83d1fMbPj7j4+rt0xd09YrIICtQygvLx84fLly9/V5lSX89LeTjp74KqqHCYXhVubW1tbKSoqCjXDYDIhIyhnqilnamVKzpqamjV9Ll+8m7tH9gHMADYl0e4u4FvB81pixQmgCqhN5rPmzJnj/Xlr3zH/0fO1/k+v7PLTnV39thsOK1asCPXzk5EJGd2VM9WUM7UyJSew2gf5/Rq5U2dmVhX38lPApgRtCs2suPc58KG4dr8Fbgme3wI8eb6ZLp8yjknjCmg53cVru5rOd3MiIqNK5AoN8MPgtuUNQA3wTQAzm2xmvXegVQKvmtl64E3gaXd/Nlh3D/BBM9sBXB+8Pi9ZWcZ1F1eQZca6/cepbz59vpsUERk1csIO0Je7f7Gf5QeBG4Pnu4Er+mnXBFyX6lwVxQXMnzaetXuP8dLWBj63uJqsLEv1x4iIjDhRPKKJrGsumEhxQQ71zadZf+B42HFERDKCCs0Q5OVkURP0rfnDriZaTqtvjYjIYFRohujC8iIurCiio6uHlbVHwo4jIhJ5KjTnYOnccvJystjZ0MquI5q3RkRkICo056CkIJdrLpwIwIptDXR09YScSEQkulRoztH8qeOpKMmn5XQXr+9W3xoRkf6o0JyjrCzj+osrMYO39h2noUV9a0REElGhOQ+VJQVcUT2eHnde1rw1IiIJqdCcp/dcOJGi/BwOnTjNxroTYccREYkcFZrzlJ+TzdK55QC8urOR1vaukBOJiESLCk0KzKoo4oLyQjq6enhlu/rWiIjEU6FJATNj6dwKcrON2sMt7GlsCzuSiEhkqNCkyLgxuVx9QaxvzcvbGujsVt8aERFQoUmpK6eVUlacz4lTnbz59tGw44iIRELkCo2Z3WVmdWa2LnjcmKDN3Lj168ys2cxuT/b96ZKdZVx/cQVmsHrPMRpb24fro0VEIityhSZwr7vPDx7P9F3p7rW964GFwEngiWTfn05V48Zw2ZRxZ/rWxGY6FREZvaJaaIbiOmCXu+8NO0iv984qozA/m7rjp9hU1xx2HBGRUFnU/uI2s7uAW4FmYDXwl+5+bID2DwJr3f3+ob7fzJYBywDKy8sXLl++PFW7wYGWHlYd7iIvG66flkt+Tmpm42xtbaWoqCgl20qXTMgIyplqyplamZKzpqZmjbsvGqhNKIXGzF4EJiVYdQfwOtAIOHA3UOXuX+5nO3nAQeBSd68PllUm+/54c+fO9dra2nPYm8TcnSfeqmNv00kurirmhnlVKdnuypUrWbp0aUq2lS6ZkBGUM9WUM7UyJaeZDVpocoYrTDx3vz6Zdmb2T8BTAzT5CLGjmfq4bZ95nsT708bMuPaiCv75tb1sPdTCJVXjmDZxbBhRRERCFblrNGYW/6f/p4BNAzS/GXjsPN6fVuPH5nFV0LfmpW31dKlvjYiMQpErNMAPzWyjmW0AaoBvApjZZDM7cweZmRUCHwR+k8z7w7JweikTi/I4frKTN/eob42IjD6hnDobiLt/sZ/lB4Eb4163AROTfX9YsrOM6y6uZPmq/azec4yLJpUwoTAv7FgiIsMmikc0I86U8WOYN2Uc3T3OS1vr1bdGREYVFZph8v7ZZYzNy+bAsVNsOaS+NSIyeqjQDJOC3GzePzs2b81/7GjkVEd3yIlERIbHoIUmuOiOmUW/51DEXVxVTPWEsZzq6OY/dmjeGhEZHZI5oik1s9uA96U7zEhnZlx3UQXZWcbmg83sP3oy7EgiImmXTKG5jtiQLheYWUX8CjP7oJn9k5nND14vS3nCEaa0MI/FMyYAsXlr1LdGREa6ZArNm8CXgb3u3tBn3ZeBvwK+YGbXAvNTG29kWjyjlNKxuRxt62D13n6HcRMRGREGLTTuvtXdN7j70wlWt7j7cXf/FvAhYHHKE45AOdlZXHdxJQCr3j7KsbaOkBOJiKTP+d51dqb4uPt3gF+c5/ZGjeoJY7m4qoSuHuflbZq3RkRGriEVGjN7wcyu6H3t7k/Gr3f3f0xVsNHgA3PKKMjNZt/Rk2w73BJ2HBGRtBjqEc23gfvM7Od9Bq+UczA2L4f3zy4D4JXtRzjdqb41IjLyDKnQuPtad68hNvT+s2b2PTMbk55oo8Olk0uYUjqGkx3dvLqjMew4IiIpN+RrNGZmQC3wE+DrwA4zi9RAlpkkvm/NxroT1B0/FXYkEZGUGuo1mv8E6oB7gSnE+tcsBZaY2QOpCmVmXzezbWa22cx+2E+bG8ys1sx2mtl34pbPNLM3guW/CmbhjLSJRfksml4KwMtb6+nu0Y0BIjJyDHWagGXAFn/3LVJfN7OtqQhkZjXATcAV7t7et5No0CYb+DGx+WgOAKvM7LfuvgX4O+Bed3/czP4P8F+JHX1F2uKZE6itb6GxtYO1+46d6dQpIpLphnqNZnOCItProynIA/BV4B53bw8+s28nUYAlwE533+3uHcDjwE3Bab1rgV8H7R4GPpmiXGmVm53FtRfFauobu5s4cbIz5EQiIqmRstGb3X13ijY1B3h/cPrr92aWqBPoFGB/3OsDwbKJwHF37+qzPCNMn1jIRZOK6ex2Xq7VvDUiMjKEMsOmmb0ITEqw6g5imSYAVxMbaWC5mV0wwJHU+eRYRux0IOXl5axcuTLVHzFk1uXU7e9kzx44eWA7U4rf+bdAa2trJHIOJBMygnKmmnKmVqbkTEYohcbdr+9vnZl9FfhNUFjeNLMeoAyIH1e/DqiOez01WNYEjDeznOCopnd5fzkeAB4AmDt3ri9duvTcdijFJh84zktbG2jLz+bqa2ZQkJt9Zt3KlSuJSs7+ZEJGUM5UU87UypScyYjixGf/BtQAmNkcIA/o28FkFTA7uMMsD/gc8NugOK0APhO0uwV4kgxz2ZRxTB5fQFt7N3/Ypb41IpLZolhoHiQ2JcEmYhf5b3F3N7PJZvYMQHC0chvwHLAVWO7um4P3fxv4CzPbSeyazc+GfQ/Ok5lx7UWVZJmx4cAJDp84HXYkEZFzFsqps4EEd5F9IcHyg8CNca+fAZ5J0G43sbvSMlp5cT4Lpo9n9Z5jvLi1ns8vmUZWloUdS0RkyKJ4RCOBq2ZOpGRMLkda2nlrv+atEZHMpEITYXk5Z/vWvLariROn1LdGRDKPCk3EzSwrZHZlEZ3dzspazVsjIpknctdo5N2Wzq1gb9NJdh9po/lwF0dW7x/8TSGqPdBJQ8QzgnKmmnKmVqbkTIYKTQYoys/hfbPKeHlbA0dPO3XHoj3Cc+MppyjiGUE5U005UytTciZDhSZDXD51HFXjCnil6wBXL5oadpwBvZ4BGUE5U005UytTcv5FEm1UaDKEmVFRUkDZmCymlo4NO86AMiEjKGeqKWdqZUrOZOhmABERSSsVGhERSSsVGhERSSsVGhERSSsVGhERSSsVGhERSSsVGhERSatIFhoz+7qZbTOzzWb2wwTrq81shZltCdp8I27dXWZWZ2brgseNfd8vIiLDJ3IdNs2sBrgJuMLd282sIkGzLuAv3X2tmRUDa8zsBXffEqy/193/Ybgyi4hI/6J4RPNV4B53bwdw94a+Ddz9kLuvDZ63EJtlc8qwphQRkaRY1IadN7N1wJPADcBp4FvuvmqA9jOAV4B57t5sZncBtwLNwGpiRz4JZw0zs2XAMoDy8vKFy5cvT9l+pEtraytFRUVhxxhQJmQE5Uw15UytTMlZU1Ozxt0XDdjI3Yf9AbwIbErwuCn4+o+AEZuS+W2CgphgO0XAGuDTccsqgWxiR2s/AB5MJtOcOXM8E6xYsSLsCIPKhIzuyplqyplamZITWO2D/H4N5RqNu1/f3zoz+yrwm2AH3jSzHqAMONKnXS7wr8Aj7v6buG3Xx7X5J+CpFMcXEZEhiOI1mn8DagDMbA6QBzTGNzAzA34GbHX3H/VZVxX38lPEjpBERCQkUSw0DwIXmNkm4HHgFnd3M5tsZs8Ebd4LfBG4NsFtzD80s41mtoFYwfrmsO+BiIicEbnbm929A/hCguUHgRuD568Su4aT6P1fTGtAEREZkige0YiIyAiiQiMiImmlQiMiImmlQiMiImmlQiMiImmlQiMiImmlQiMiImmlQiMiImmlQiMiImmlQiMiImmlQiMiImmlQiMiImmlQiMiImkVudGbAczs68DXgG7gaXf/6wRt9gAtQZsuD6YSNbMJwK+AGcAe4E+8n6mc4+U2N8PHP352wb33xr5+M26WgZtvhs9/Hm65BY4ejS278EK47z64/3547rmzbR9+GHbuhLvvPrvsa1+DG2545+csXgx33gnf/z6sipux+ne/g2efhR//+Oyyv/kbcvrm/PCH4bbb4PbbYdeu2LIJE2Kf/+ij8Nhjw75P08vKYOnSpPeJWbNinz/M+zSvqQmeeiot/06p3Kd5TU2x94b8szfYPs1raoLZs0P92Utmn+Y1NcHEiaH+7CWzT2dyhvizl9Q+JWOwKTiH+0FsDpkXgfzgdUU/7fYAZQmW/xD4TvD8O8DfJfO5mso5dTIho7typppyplam5CSJqZyjeOrsq8A97t4O4O4NQ3z/TcDDwfOHgU+mLpqIiAxVFAvNHOD9ZvaGmf3ezBb3086B581sjZkti1te6e6HgueHgcp0hhURkYFZ7MhnmD/U7EVgUoJVdwA/AFYA/xewmNj1lgu8T1Azm+LudWZWAbwAfN3dXzGz4+4+Pq7dMXcv7SfHMmAZQHl5+cLly5ef/86lWWtrK0VFRWHHGFAmZATlTDXlTK1MyVlTU7PGg2vk/Qml0AzEzJ4ldl1lRfB6F3C1ux8Z4D13Aa3u/g9mVgssdfdDZlYFrHT3uUl8bgtQm5KdSK8yoDHsEIPIhIygnKmmnKmVKTnnunvxQA2ieNfZvxG7IWCFmc0B8ujzzTazQiDL3VuC5x8Cvh+s/i1wC3BP8PXJJD+3drCqHAVmtjrqOTMhIyhnqilnamVSzsHaRPEazYPABWa2CXgcuMXd3cwmm9kzQZtK4FUzWw+8SewW6GeDdfcAHzSzHcD1wWsREQlJ5I5o3L0D+EKC5QeBG4Pnu4Er+nl/E3BdOjOKiEjyonhEE5YHwg6QpEzImQkZQTlTTTlTa8TkjNzNACIiMrLoiEZERNJq1BcaM7vBzGrNbKeZfSfsPP0xswfNrCG4SSKSzKzazFaY2RYz22xm3wg7UyJmVmBmb5rZ+iDn/wo7U3/MLNvM3jKzp8LOMhAz22NmG81sXTJ3IYXBzMab2a/NbJuZbTWza8LO1JeZzQ2+h72PZjO7PexciZjZN4P/P5vM7DEzK+i37Wg+dWZm2cB24IPAAWAVcLO7bwk1WAJm9gGgFfiFu88LO08iQb+lKndfa2bFwBrgk1H7fpqZAYXu3mpmucCrwDfc/fWQo72Lmf0FsAgocfePhZ2nP8Egt4vcPbL9PszsYeA/3P2nZpYHjHX34yHH6lfw+6kOuMrd94adJ56ZTSH2/+YSdz9lZsuBZ9z9oUTtR/sRzRJgp7vvDu52e5zYWGmR4+6vAEfDzjEQdz/k7muD5y3AVmBKuKneLRgLsDV4mRs8IvcXl5lNBT4K/DTsLJnOzMYBHwB+BrG7W6NcZALXAbuiVmTi5ABjzCwHGAsc7K/haC80U4D9ca8PEMFfjJnIzGYAVwJvhBwloeCU1DqgAXjB3aOY8z7gr4GekHMko7+xB6NiJnAE+HlwKvKnQWfvKPsc8NigrULg7nXAPwD7gEPACXd/vr/2o73QSBqYWRHwr8Dt7t4cdp5E3L3b3ecDU4ElZhap05Fm9jGgwd3XhJ0lSe9z9wXAR4CvBad6oyQHWAD8xN2vBNqITSMSScGpvU8A/xJ2lkTMrJTY2Z+ZwGSg0Mze1f+x12gvNHVAddzrqcEyOUfBNY9/BR5x99+EnWcwwemTFcANIUfp673AJ4JrH48D15rZL8ON1L/gL9zeaT2eIHZaOkoOAAfijlx/TazwRNVHgLXuXh92kH5cD7zt7kfcvRP4DfCe/hqP9kKzCphtZjODvyA+R2ysNDkHwUX2nwFb3f1HYefpj5mVm9n44PkYYjeDbAs1VB/u/l13n+ruM4j9XL7s7v3+xRgmMysMbv7oHYfwQ0Ck7o5098PAfjPrHWD3OiBSN6n0cTMRPW0W2AdcbWZjg//31xG7JptQ5IagGU7u3mVmtwHPAdnAg+6+OeRYCZnZY8BSoMzMDgDfc/efhZvqXd4LfBHYGFz/APif7v5M/28JRRXwcHBXTxaw3N0jfftwxFUCT8R+35ADPBo39mCUfB14JPijcjfwX0LOk1BQrD8I/Lews/TH3d8ws18Da4Eu4C0GGCFgVN/eLCIi6TfaT52JiEiaqdCIiEhaqdCIiEhaqdCIiEhaqdCIiEhaqdCIDCMzmxg3Mu9hM6uLe50XtPnEYCOJm9mtZnb/8KQWOT+juh+NyHALphqfD2BmdwGt7v4PvevNLMfdf4s6DssIokIjEjIzewg4TWwQ0v80sw3Ehty/zcw+DvzfQB7QBPxZ32FJzOyzwPeAbmKDG0ZtnDEZ5VRoRKJhKvAed+82s1vjlr8KXO3ubmZfITaa81/2ee+dwIfdva53aB2RKFGhEYmGf3H37gTLpwK/CiaVywPeTtDmP4GHgsmnIj+QqYw+uhlAJBra+ln+j8D97n4ZsbGv3jVdrrv/d2Kn16qBNWY2MW0pRc6BCo1ItI3j7NQVtyRqYGYXuvsb7n4nscm9qhO1EwmLCo1ItN0F/IuZrQEa+2nz92a20cw2AX8A1g9XOJFkaPRmERFJKx3RiIhIWqnQiIhIWqnQiIhIWqnQiIhIWqnQiIhIWqnQiIhIWqnQiIhIWqnQiIhIWv3/muM+zFOusaIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_outcome(T,N,outcomes=[bo.optimal_outcome_values_during_trials],labels=['BO'],true_objective_values=true_objective_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaptive Bayesian Optimization\n",
    "The setup for ABO is the same as for BO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Time index:   0%|          | 0/3 [00:00<?, ?it/s]reconstraining parameters GP_regression.rbf.variance\n",
      "Time index:  33%|███▎      | 1/3 [00:00<00:00,  3.93it/s]reconstraining parameters GP_regression.rbf.variance\n",
      "Time index:  67%|██████▋   | 2/3 [00:00<00:00,  3.54it/s]reconstraining parameters GP_regression.rbf.variance\n",
      "Time index: 100%|██████████| 3/3 [00:00<00:00,  3.49it/s]\n"
     ]
    }
   ],
   "source": [
    "abo = ABO(**BO_input_params)\n",
    "abo.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_outcome(T,N,outcomes=[bo.optimal_outcome_values_during_trials,abo.optimal_outcome_values_during_trials],labels=['BO', 'ABO'],true_objective_values=true_objective_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Causal Bayesian Optimization\n",
    "CBO has one extra parameter (though there are many others which we are not demonstrating in this demo) which is the SEM estimator method `build_sem_hat`. It estimates a SEM model from the observational data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "CBO_input_params = {\n",
    "    \"G\": G,\n",
    "    \"sem\": StatSEM,\n",
    "    \"base_target_variable\": \"Y\",\n",
    "    \"make_sem_estimator\": build_sem_hat,\n",
    "    \"exploration_sets\":exploration_sets,\n",
    "    \"observation_samples\": D_O,\n",
    "    \"intervention_samples\":D_I, # Interventional data as well\n",
    "    \"intervention_domain\": intervention_domain,\n",
    "    \"intervention_samples\":None,\n",
    "    \"number_of_trials\": N,\n",
    "    \"sample_anchor_points\": True,\n",
    "    \"seed_anchor_points\": 1,\n",
    "    \"debug_mode\":False,\n",
    "    \"ground_truth\":all_causal_effects\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Time index:   0%|          | 0/3 [00:00<?, ?it/s]reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "Time index:  33%|███▎      | 1/3 [00:04<00:08,  4.49s/it]reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "Time index:  67%|██████▋   | 2/3 [00:12<00:06,  6.83s/it]reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "Time index: 100%|██████████| 3/3 [00:24<00:00,  8.09s/it]\n"
     ]
    }
   ],
   "source": [
    "cbo = CBO(**CBO_input_params)\n",
    "cbo.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_outcome(T,N,outcomes=[bo.optimal_outcome_values_during_trials, abo.optimal_outcome_values_during_trials, cbo.optimal_outcome_values_during_trials],labels=['BO', 'ABO', 'CBO'],true_objective_values=true_objective_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dynamic Causal Bayesian Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DCBO has the same input parameters as CBO. But they work very differently under the hood. DCBO takes 'horizontal' information into account i.e. is able to transfer information between time-slices. CBO has no notion of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /Users/nd/anaconda3/envs/scibo/lib/python3.7/site-packages/GPy/kern/src/stationary.py:168: RuntimeWarning:overflow encountered in true_divide\n",
      " /Users/nd/anaconda3/envs/scibo/lib/python3.7/site-packages/GPy/kern/src/rbf.py:52: RuntimeWarning:overflow encountered in square\n",
      " /Users/nd/anaconda3/envs/scibo/lib/python3.7/site-packages/GPy/kern/src/rbf.py:76: RuntimeWarning:invalid value encountered in multiply\n",
      "Time index:   0%|          | 0/3 [00:00<?, ?it/s]reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "Time index:  33%|███▎      | 1/3 [00:04<00:09,  4.71s/it]reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "Time index:  67%|██████▋   | 2/3 [00:12<00:06,  6.57s/it]reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "reconstraining parameters GP_regression.rbf.variance\n",
      "Time index: 100%|██████████| 3/3 [00:20<00:00,  6.98s/it]\n"
     ]
    }
   ],
   "source": [
    "dcbo = DCBO(**CBO_input_params)\n",
    "dcbo.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_outcome(T,N,outcomes=[bo.optimal_outcome_values_during_trials, abo.optimal_outcome_values_during_trials, cbo.optimal_outcome_values_during_trials, dcbo.optimal_outcome_values_during_trials],labels=['BO', 'ABO', 'CBO', 'DCBO'],true_objective_values=true_objective_values)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "aaa96eb265c32c2b50eaffc5e0bc3c80016276efa552bb99d04c94d0e8842f70"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 64-bit ('scibo': conda)",
   "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": "3.10.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}