notebooks/nutrition_CI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nutrition dataset case study with multiple background samples showing confidence interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# limit the number of threads used by xgboost models\n",
    "import os\n",
    "os.environ[\"OMP_NUM_THREADS\"] = \"8\"\n",
    "os.environ[\"OPENBLAS_NUM_THREADS\"] = \"8\"\n",
    "os.environ[\"MKL_NUM_THREADS\"] = \"8\"\n",
    "os.environ[\"VECLIB_MAXIMUM_THREADS\"] = \"8\"\n",
    "os.environ[\"NUMEXPR_NUM_THREADS\"] = \"8\"\n",
    "\n",
    "# standard libaries\n",
    "import sys\n",
    "import copy\n",
    "import dill\n",
    "from collections import defaultdict\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import shap\n",
    "\n",
    "# flow specific packages\n",
    "if '../' not in sys.path:\n",
    "    sys.path = ['../'] + sys.path\n",
    "from flow import Node, CreditFlow, Graph, get_source_nodes, topo_sort, flatten_graph, eval_graph, boundary_graph, single_source_graph, viz_graph, save_graph, hcluster_graph\n",
    "from flow import ParallelCreditFlow, GraphExplainer, translator\n",
    "from flow import group_nodes, build_feature_graph\n",
    "from flow import CausalLinks, create_xgboost_f\n",
    "from flow import edge_credits2edge_credit, node_dict2str_dict\n",
    "from on_manifold import OnManifoldExplainer, IndExplainer\n",
    "\n",
    "np.random.seed(42) # fix for reproducibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9932, 18) (9932, 18)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Diastolic BP</th>\n",
       "      <th>Poverty index</th>\n",
       "      <th>Race</th>\n",
       "      <th>Red blood cells</th>\n",
       "      <th>Sedimentation rate</th>\n",
       "      <th>Serum Albumin</th>\n",
       "      <th>Serum Cholesterol</th>\n",
       "      <th>Serum Iron</th>\n",
       "      <th>Serum Magnesium</th>\n",
       "      <th>Serum Protein</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Systolic BP</th>\n",
       "      <th>TIBC</th>\n",
       "      <th>TS</th>\n",
       "      <th>White blood cells</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Pulse pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>31.109434</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.38</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>26.9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>32.362572</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>25.388497</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>265.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.97</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>26.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>26.446610</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>49.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>20.354684</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Diastolic BP  Poverty index  Race  Red blood cells  \\\n",
       "0  35.0          92.0          126.0   2.0             77.7   \n",
       "1  71.0          78.0          210.0   2.0             77.7   \n",
       "2  74.0          86.0          999.0   2.0             77.7   \n",
       "3  64.0          92.0          385.0   1.0             77.7   \n",
       "4  32.0          70.0          183.0   2.0             77.7   \n",
       "\n",
       "   Sedimentation rate  Serum Albumin  Serum Cholesterol  Serum Iron  \\\n",
       "0                12.0            5.0              165.0       135.0   \n",
       "1                37.0            4.0              298.0        89.0   \n",
       "2                31.0            3.8              222.0       115.0   \n",
       "3                30.0            4.3              265.0        94.0   \n",
       "4                18.0            5.0              203.0       192.0   \n",
       "\n",
       "   Serum Magnesium  Serum Protein  Sex  Systolic BP   TIBC    TS  \\\n",
       "0             1.37            7.6  2.0        142.0  323.0  41.8   \n",
       "1             1.38            6.4  2.0        156.0  331.0  26.9   \n",
       "2             1.37            7.4  2.0        170.0  299.0  38.5   \n",
       "3             1.97            7.3  2.0        172.0  349.0  26.9   \n",
       "4             1.35            7.3  1.0        128.0  386.0  49.7   \n",
       "\n",
       "   White blood cells        BMI  Pulse pressure  \n",
       "0                5.8  31.109434            50.0  \n",
       "1                5.3  32.362572            78.0  \n",
       "2                8.1  25.388497            84.0  \n",
       "3                6.7  26.446610            80.0  \n",
       "4                8.1  20.354684            58.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,y = shap.datasets.nhanesi()\n",
    "X_display,y_display = shap.datasets.nhanesi(display=True) # human readable feature values\n",
    "# remove the first column: unnamed: 0\n",
    "X = X.iloc[:, 1:]\n",
    "X_display = X_display.iloc[:, 1:]\n",
    "\n",
    "xgb_full = xgboost.DMatrix(X, label=y)\n",
    "\n",
    "# create a train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)\n",
    "xgb_train = xgboost.DMatrix(X_train, label=y_train)\n",
    "xgb_test = xgboost.DMatrix(X_test, label=y_test)\n",
    "\n",
    "print(X.shape, X_display.shape)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explanation settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_bg = 100 # paper: 100, number of sampled background samples\n",
    "nsamples = 100 # number of fg samples to explain\n",
    "nruns = 1 # paper: 10,000, number of monte carlo samplings\n",
    "bg = X.fillna(X.mean()).sample(n_bg) # background samples\n",
    "fg = X[:nsamples] # foreground samples to explain\n",
    "sample_ind = 3 # sample to show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-cox-nloglik:7.26934\n",
      "[100]\ttest-cox-nloglik:7.04554\n",
      "[200]\ttest-cox-nloglik:6.90625\n",
      "[300]\ttest-cox-nloglik:6.80909\n",
      "[400]\ttest-cox-nloglik:6.73926\n",
      "[500]\ttest-cox-nloglik:6.68686\n",
      "[600]\ttest-cox-nloglik:6.64699\n",
      "[700]\ttest-cox-nloglik:6.61571\n",
      "[800]\ttest-cox-nloglik:6.59138\n",
      "[900]\ttest-cox-nloglik:6.57148\n",
      "[999]\ttest-cox-nloglik:6.55579\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    \"eta\": 0.002,\n",
    "    \"max_depth\": 3,\n",
    "    \"objective\": \"survival:cox\",\n",
    "    \"subsample\": 0.5\n",
    "}\n",
    "model_train = xgboost.train(params, xgb_train, 1000, evals = [(xgb_test, \"test\")], verbose_eval=100)\n",
    "\n",
    "# workaround from https://github.com/slundberg/shap/issues/1215\n",
    "mybooster = model_train\n",
    "\n",
    "model_bytearray = mybooster.save_raw()[4:]\n",
    "def myfun(self=None):\n",
    "    return model_bytearray\n",
    "mybooster.save_raw = myfun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-cox-nloglik:8.88081\n",
      "[999]\ttest-cox-nloglik:8.16748\n"
     ]
    }
   ],
   "source": [
    "# train final model on the full data set\n",
    "params = {\n",
    "    \"eta\": 0.002,\n",
    "    \"max_depth\": 3, \n",
    "    \"objective\": \"survival:cox\",\n",
    "    \"subsample\": 0.5\n",
    "}\n",
    "model = xgboost.train(params, xgb_full, 1000, evals = [(xgb_full, \"test\")], verbose_eval=1000)\n",
    "\n",
    "# workaround from https://github.com/slundberg/shap/issues/1215\n",
    "mybooster = model\n",
    "\n",
    "model_bytearray = mybooster.save_raw()[4:]\n",
    "def myfun(self=None):\n",
    "    return model_bytearray\n",
    "mybooster.save_raw = myfun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8289463410825477"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def c_statistic_harrell(pred, labels):\n",
    "    total = 0\n",
    "    matches = 0\n",
    "    for i in range(len(labels)):\n",
    "        for j in range(len(labels)):\n",
    "            if labels[j] > 0 and abs(labels[i]) > labels[j]:\n",
    "                total += 1\n",
    "                if pred[j] > pred[i]:\n",
    "                    matches += 1\n",
    "    return matches/total\n",
    "\n",
    "# see how well we can order people by survival\n",
    "c_statistic_harrell(model_train.predict(xgb_test, ntree_limit=5000), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ind Shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]\n"
     ]
    },
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      "text/plain": [
       "<graphviz.files.Source at 0x7f1a12fe9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "causal_links = CausalLinks()\n",
    "categorical_feature_names = ['Race', 'Sex']\n",
    "display_translator = translator(X.columns, X, X_display)\n",
    "target_name = 'predicted hazard'\n",
    "feature_names = list(X.columns)\n",
    "\n",
    "# build causal links\n",
    "causal_links.add_causes_effects(feature_names, target_name, create_xgboost_f(feature_names, model, output_margin=True)) # the target\n",
    "graph = build_feature_graph(X.fillna(X.mean()), causal_links, categorical_feature_names, display_translator, target_name)\n",
    "graph.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "bg 74/100\n",
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      "bg 79/100\n",
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    },
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     "output_type": "stream",
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      "bg 91/100\n",
      "bg 92/100\n"
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    },
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    },
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      "bg 97/100\n",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "bruteforce sampling: 100%|██████████| 1/1 [00:00<00:00, 13.15it/s]\n",
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      "\n"
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   ],
   "source": [
    "# multiple background result with individual run\n",
    "edge_credits = [] # save for each run\n",
    "for i in range(len(bg)): # average run result using linearity axiom\n",
    "    print(f'bg {i}/{len(bg)}')\n",
    "    for j in range(nruns):\n",
    "        explainer = GraphExplainer(graph, bg[i:i+1], nruns=1) \n",
    "        cf = explainer.shap_values(fg)\n",
    "        edge_credits.append(node_dict2str_dict(cf.edge_credit)) # use node name is more portable vs. node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "<path fill=\"none\" stroke=\"#ff0051\" stroke-width=\"0.780249\" d=\"M761.659,-87.805C747.738,-76.1318 727.254,-61.2132 706.343,-54 622.217,-24.9814 369.344,-19.6734 238.592,-18.9262\"/>\n",
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       "<text text-anchor=\"middle\" x=\"783.843\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#ff0051\">(0.10, 0.11, 0.13)</text>\n",
       "</g>\n",
       "<!-- Poverty index -->\n",
       "<g id=\"node8\" class=\"node\"><title>Poverty index</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"909.343\" cy=\"-105\" rx=\"59.5901\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"909.343\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">Poverty index</text>\n",
       "</g>\n",
       "<!-- Poverty index&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge7\" class=\"edge\"><title>Poverty index&#45;&gt;predicted hazard</title>\n",
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       "<text text-anchor=\"middle\" x=\"920.343\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#008bfb\">(&#45;0.03, &#45;0.03, &#45;0.02)</text>\n",
       "</g>\n",
       "<!-- BMI -->\n",
       "<g id=\"node9\" class=\"node\"><title>BMI</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1015.34\" cy=\"-105\" rx=\"27.8951\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1015.34\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">BMI</text>\n",
       "</g>\n",
       "<!-- BMI&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge8\" class=\"edge\"><title>BMI&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"#008bfb\" stroke-width=\"0.152889\" d=\"M1008.24,-87.3752C1002.33,-75.8063 992.711,-61.2111 979.343,-54 915.276,-19.4406 431.52,-17.74 237.999,-18.471\"/>\n",
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       "<text text-anchor=\"middle\" x=\"1051.34\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#008bfb\">(&#45;0.03, &#45;0.02, &#45;0.01)</text>\n",
       "</g>\n",
       "<!-- Sex -->\n",
       "<g id=\"node10\" class=\"node\"><title>Sex</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"1115.34,-123 1061.34,-123 1061.34,-87 1115.34,-87 1115.34,-123\"/>\n",
       "<text text-anchor=\"middle\" x=\"1088.34\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">Sex</text>\n",
       "</g>\n",
       "<!-- Sex&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge9\" class=\"edge\"><title>Sex&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"#008bfb\" stroke-width=\"1.02797\" d=\"M1104.58,-86.9905C1112.72,-76.42 1119.16,-63.1496 1110.34,-54 1080.28,-22.8123 461.683,-19.2308 238.091,-18.9582\"/>\n",
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       "<text text-anchor=\"middle\" x=\"1170.34\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#008bfb\">(&#45;0.19, &#45;0.15, &#45;0.11)</text>\n",
       "</g>\n",
       "<!-- Serum Albumin -->\n",
       "<g id=\"node11\" class=\"node\"><title>Serum Albumin</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1211.34\" cy=\"-105\" rx=\"68.4888\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1211.34\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">Serum Albumin</text>\n",
       "</g>\n",
       "<!-- Serum Albumin&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge10\" class=\"edge\"><title>Serum Albumin&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"#008bfb\" stroke-width=\"0.118469\" d=\"M1225.25,-87.2838C1232.44,-76.5638 1238.07,-63.016 1229.34,-54 1195,-18.5207 480.889,-17.8178 238.226,-18.6106\"/>\n",
       "<polygon fill=\"#008bfb\" stroke=\"#008bfb\" stroke-width=\"0.118469\" points=\"238.052,-15.111 228.064,-18.6455 238.076,-22.111 238.052,-15.111\"/>\n",
       "<text text-anchor=\"middle\" x=\"1288.34\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#008bfb\">(&#45;0.02, &#45;0.01, &#45;0.00)</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f1a0f0a9f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AGraph <Swig Object of type 'Agraph_t *' at 0x7f1a12ed4900>>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf.show_CI = True # 95% confidence interval from https://link.springer.com/chapter/10.1007/978-3-030-57321-8_2 Equation 12.\n",
    "cf.draw(sample_ind, max_display=10, edge_credit=edge_credits2edge_credit(edge_credits, cf.graph), show_fg_val=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# On manifold SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples:   1%|          | 1/100 [00:00<00:13,  7.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 0/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 14.40it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:05, 17.55it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "manifold bg samples: 100%|██████████| 100/100 [00:07<00:00, 14.06it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 14.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 2/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "manifold bg samples: 100%|██████████| 100/100 [00:07<00:00, 13.70it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 15.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 3/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:07<00:00, 14.24it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:07, 13.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 4/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 5/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "text": [
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    },
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     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
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    },
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     "name": "stderr",
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    {
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     "name": "stderr",
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    {
     "name": "stdout",
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    },
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      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 14.51it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 16.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 86/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 14.87it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 16.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 87/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 14.86it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 15.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 88/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 14.56it/s]\n",
      "manifold bg samples:   0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 89/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 15.18it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 15.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 90/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 14.70it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 16.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 91/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 14.90it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 14.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 92/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 15.59it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 15.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 93/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 15.07it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:07, 12.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 94/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 15.73it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:05, 16.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 95/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 15.03it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 16.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 96/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:07<00:00, 14.21it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:05, 16.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 97/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 15.40it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:07, 13.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 98/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 15.33it/s]\n",
      "manifold bg samples:   2%|▏         | 2/100 [00:00<00:06, 15.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 99/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "manifold bg samples: 100%|██████████| 100/100 [00:06<00:00, 15.08it/s]\n"
     ]
    }
   ],
   "source": [
    "feature_names = X.columns\n",
    "f = lambda x: model.predict(xgboost.DMatrix(pd.DataFrame(x, columns=feature_names)), output_margin=True) # wrapper for the model used by on manifold explainer\n",
    "\n",
    "# multiple background result with individual run\n",
    "manifold_edge_credits = [] # save for each run\n",
    "bg_on_manifold = X.fillna(X.mean()).sample(100) # used to learn conditional dependence\n",
    "for i in range(len(bg)): # average run result using linearity axiom \n",
    "    print(f'bg {i}/{len(bg)}')\n",
    "    for j in range(nruns):\n",
    "        explainer = OnManifoldExplainer(f, pd.concat([bg[i:i+1], bg_on_manifold]), nruns=1)\n",
    "        cf_on_manifold = explainer.shap_values(fg)\n",
    "        manifold_edge_credits.append(cf_on_manifold) # use node name is more portable vs. node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On manifold\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('On manifold')\n",
    "sum(manifold_edge_credits).draw(sample_ind, show=False, max_display=10)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shapley Flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Serum Albumin:   0%|          | 0/14 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:3.87407\n",
      "[100]\ttest-rmse:3.17810\n",
      "[200]\ttest-rmse:2.60949\n",
      "[300]\ttest-rmse:2.14547\n",
      "[400]\ttest-rmse:1.76748\n",
      "[499]\ttest-rmse:1.46278\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Red blood cells:   7%|▋         | 1/14 [00:00<00:06,  1.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:56.62017\n",
      "[100]\ttest-rmse:47.20332\n",
      "[200]\ttest-rmse:39.64856\n",
      "[300]\ttest-rmse:33.63278\n",
      "[400]\ttest-rmse:28.88807\n",
      "[499]\ttest-rmse:25.23218\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for White blood cells:  14%|█▍        | 2/14 [00:01<00:06,  1.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:7.24529\n",
      "[100]\ttest-rmse:6.04540\n",
      "[200]\ttest-rmse:5.08526\n",
      "[300]\ttest-rmse:4.32187\n",
      "[400]\ttest-rmse:3.72444\n",
      "[499]\ttest-rmse:3.26567\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Serum Protein:  21%|██▏       | 3/14 [00:01<00:05,  1.87it/s]    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:6.61101\n",
      "[100]\ttest-rmse:5.42027\n",
      "[200]\ttest-rmse:4.44728\n",
      "[300]\ttest-rmse:3.65282\n",
      "[400]\ttest-rmse:3.00507\n",
      "[499]\ttest-rmse:2.48216\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Systolic BP:  29%|██▊       | 4/14 [00:02<00:05,  1.97it/s]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:136.18751\n",
      "[100]\ttest-rmse:112.07594\n",
      "[200]\ttest-rmse:92.46082\n",
      "[300]\ttest-rmse:76.55484\n",
      "[400]\ttest-rmse:63.71541\n",
      "[499]\ttest-rmse:53.49734\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for BMI:  36%|███▌      | 5/14 [00:02<00:04,  1.87it/s]        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:25.68026\n",
      "[100]\ttest-rmse:21.22700\n",
      "[200]\ttest-rmse:17.61862\n",
      "[300]\ttest-rmse:14.70853\n",
      "[400]\ttest-rmse:12.38153\n",
      "[499]\ttest-rmse:10.55001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Serum Cholesterol:  43%|████▎     | 6/14 [00:03<00:04,  1.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:226.94263\n",
      "[100]\ttest-rmse:187.87550\n",
      "[200]\ttest-rmse:156.24817\n",
      "[300]\ttest-rmse:130.78793\n",
      "[400]\ttest-rmse:110.44482\n",
      "[499]\ttest-rmse:94.44800\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Serum Magnesium:  50%|█████     | 7/14 [00:03<00:03,  1.80it/s]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:1.18907\n",
      "[100]\ttest-rmse:0.97714\n",
      "[200]\ttest-rmse:0.80443\n",
      "[300]\ttest-rmse:0.66402\n",
      "[400]\ttest-rmse:0.55023\n",
      "[499]\ttest-rmse:0.45920\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Diastolic BP:  57%|█████▋    | 8/14 [00:04<00:03,  1.85it/s]   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:83.71987\n",
      "[100]\ttest-rmse:68.93323\n",
      "[200]\ttest-rmse:56.90493\n",
      "[300]\ttest-rmse:47.15287\n",
      "[400]\ttest-rmse:39.27799\n",
      "[499]\ttest-rmse:33.00833\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Sedimentation rate:  64%|██████▍   | 9/14 [00:04<00:02,  1.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:19.38454\n",
      "[100]\ttest-rmse:16.95487\n",
      "[200]\ttest-rmse:15.09857\n",
      "[300]\ttest-rmse:13.70687\n",
      "[400]\ttest-rmse:12.67748\n",
      "[499]\ttest-rmse:11.94172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for TS:  71%|███████▏  | 10/14 [00:05<00:02,  1.73it/s]                "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:29.79472\n",
      "[100]\ttest-rmse:25.10775\n",
      "[200]\ttest-rmse:21.40968\n",
      "[300]\ttest-rmse:18.53344\n",
      "[400]\ttest-rmse:16.33232\n",
      "[499]\ttest-rmse:14.69491\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Poverty index:  79%|███████▊  | 11/14 [00:06<00:01,  1.69it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:365.16959\n",
      "[100]\ttest-rmse:325.23489\n",
      "[200]\ttest-rmse:295.42532\n",
      "[300]\ttest-rmse:273.60980\n",
      "[400]\ttest-rmse:257.95938\n",
      "[499]\ttest-rmse:247.04848\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for TIBC:  86%|████████▌ | 12/14 [00:06<00:01,  1.73it/s]         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:365.08758\n",
      "[100]\ttest-rmse:300.40860\n",
      "[200]\ttest-rmse:247.82376\n",
      "[300]\ttest-rmse:205.21208\n",
      "[400]\ttest-rmse:170.85916\n",
      "[499]\ttest-rmse:143.55504\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Serum Iron:  93%|█████████▎| 13/14 [00:07<00:00,  1.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-rmse:105.80270\n",
      "[100]\ttest-rmse:88.86579\n",
      "[200]\ttest-rmse:75.45215\n",
      "[300]\ttest-rmse:64.97789\n",
      "[400]\ttest-rmse:56.92674\n",
      "[499]\ttest-rmse:50.90213\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "learning dependency for Serum Iron: 100%|██████████| 14/14 [00:07<00:00,  1.78it/s]\n"
     ]
    }
   ],
   "source": [
    "causal_links = CausalLinks()\n",
    "categorical_feature_names = ['Race', 'Sex']\n",
    "display_translator = translator(X.columns, X, X_display)\n",
    "target_name = 'predicted hazard'\n",
    "feature_names = list(X.columns)\n",
    "\n",
    "# build causal links\n",
    "causal_links.add_causes_effects(feature_names, target_name, create_xgboost_f(feature_names, model, output_margin=True)) # the target uses log(HR) instead of HR\n",
    "A = ['Diastolic BP', 'Systolic BP']\n",
    "D = ['Pulse pressure'] # Systolic BP - Diastolic BP\n",
    "causal_links.add_causes_effects(A, D, lambda dbp, sdp: sdp-dbp)\n",
    "A = ['Age', 'Sex', 'Poverty index', 'Race']\n",
    "D = list(set(feature_names) - set(A) - set(['Pulse pressure']))\n",
    "causal_links.add_causes_effects(A, D)\n",
    "A = ['Age', 'Sex', 'Race']\n",
    "D = ['Poverty index']\n",
    "causal_links.add_causes_effects(A, D)\n",
    "\n",
    "causal_graph = build_feature_graph(X.fillna(X.mean()), causal_links, categorical_feature_names, display_translator, target_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "<g id=\"edge12\" class=\"edge\"><title>Iron&#45;&gt;TS</title>\n",
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       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1290\" cy=\"-162\" rx=\"73.387\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1290\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">White blood cells</text>\n",
       "</g>\n",
       "<!-- Inflamation&#45;&gt;White blood cells -->\n",
       "<g id=\"edge17\" class=\"edge\"><title>Inflamation&#45;&gt;White blood cells</title>\n",
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       "<!-- Sedimentation rate -->\n",
       "<g id=\"node19\" class=\"node\"><title>Sedimentation rate</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1121\" cy=\"-162\" rx=\"77.1866\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1121\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">Sedimentation rate</text>\n",
       "</g>\n",
       "<!-- Inflamation&#45;&gt;Sedimentation rate -->\n",
       "<g id=\"edge18\" class=\"edge\"><title>Inflamation&#45;&gt;Sedimentation rate</title>\n",
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       "</g>\n",
       "<!-- Serum Magnesium&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge19\" class=\"edge\"><title>Serum Magnesium&#45;&gt;predicted hazard</title>\n",
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       "<!-- Serum Cholesterol&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge20\" class=\"edge\"><title>Serum Cholesterol&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M682.505,-216.578C730.375,-182.647 847.017,-105.206 958,-72 1056.06,-42.6592 1350.53,-27.5707 1493.04,-21.8202\"/>\n",
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       "<!-- BMI&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge21\" class=\"edge\"><title>BMI&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M895.971,-216.413C909.144,-182.523 944.2,-105.7 1002,-72 1083.2,-24.6593 1355.56,-18.341 1492.29,-18.2698\"/>\n",
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       "<!-- Red blood cells&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge22\" class=\"edge\"><title>Red blood cells&#45;&gt;predicted hazard</title>\n",
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       "</g>\n",
       "<!-- Serum Albumin&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge54\" class=\"edge\"><title>Serum Albumin&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1729.28,-75.1703C1698.1,-64.0371 1654.11,-48.3263 1620.58,-36.3501\"/>\n",
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       "<!-- Serum Protein&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge55\" class=\"edge\"><title>Serum Protein&#45;&gt;predicted hazard</title>\n",
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       "<!-- TS&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge57\" class=\"edge\"><title>TS&#45;&gt;predicted hazard</title>\n",
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       "<!-- TIBC&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge58\" class=\"edge\"><title>TIBC&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2007.49,-78.7753C2000.64,-76.2363 1993.11,-73.7452 1986,-72 1870.65,-43.6907 1733.29,-29.9053 1649.39,-23.6507\"/>\n",
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       "<!-- Serum Iron&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge59\" class=\"edge\"><title>Serum Iron&#45;&gt;predicted hazard</title>\n",
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       "<!-- Systolic BP&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge61\" class=\"edge\"><title>Systolic BP&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1577.45,-144.638C1585.19,-134.767 1593.94,-121.517 1598,-108 1602.6,-92.6764 1601.62,-87.5858 1598,-72 1595.85,-62.7152 1591.82,-53.1819 1587.58,-44.8058\"/>\n",
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       "<!-- Pulse pressure -->\n",
       "<g id=\"node26\" class=\"node\"><title>Pulse pressure</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1527\" cy=\"-90\" rx=\"61.99\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1527\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">Pulse pressure</text>\n",
       "</g>\n",
       "<!-- Systolic BP&#45;&gt;Pulse pressure -->\n",
       "<g id=\"edge62\" class=\"edge\"><title>Systolic BP&#45;&gt;Pulse pressure</title>\n",
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       "<!-- Diastolic BP&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge63\" class=\"edge\"><title>Diastolic BP&#45;&gt;predicted hazard</title>\n",
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       "<!-- Diastolic BP&#45;&gt;Pulse pressure -->\n",
       "<g id=\"edge64\" class=\"edge\"><title>Diastolic BP&#45;&gt;Pulse pressure</title>\n",
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       "<!-- White blood cells&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge67\" class=\"edge\"><title>White blood cells&#45;&gt;predicted hazard</title>\n",
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       "<!-- Sedimentation rate&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge68\" class=\"edge\"><title>Sedimentation rate&#45;&gt;predicted hazard</title>\n",
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       "<!-- Age -->\n",
       "<g id=\"node20\" class=\"node\"><title>Age</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"890\" cy=\"-378\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"890\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">Age</text>\n",
       "</g>\n",
       "<!-- Age&#45;&gt;Poverty index -->\n",
       "<g id=\"edge23\" class=\"edge\"><title>Age&#45;&gt;Poverty index</title>\n",
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       "<!-- Age&#45;&gt;Blood protein -->\n",
       "<g id=\"edge24\" class=\"edge\"><title>Age&#45;&gt;Blood protein</title>\n",
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       "<!-- Age&#45;&gt;Iron -->\n",
       "<g id=\"edge25\" class=\"edge\"><title>Age&#45;&gt;Iron</title>\n",
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       "<!-- Age&#45;&gt;Blood pressure -->\n",
       "<g id=\"edge26\" class=\"edge\"><title>Age&#45;&gt;Blood pressure</title>\n",
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       "<!-- Age&#45;&gt;Inflamation -->\n",
       "<g id=\"edge27\" class=\"edge\"><title>Age&#45;&gt;Inflamation</title>\n",
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       "<!-- Age&#45;&gt;Serum Magnesium -->\n",
       "<g id=\"edge28\" class=\"edge\"><title>Age&#45;&gt;Serum Magnesium</title>\n",
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       "<!-- Age&#45;&gt;Serum Cholesterol -->\n",
       "<g id=\"edge29\" class=\"edge\"><title>Age&#45;&gt;Serum Cholesterol</title>\n",
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       "<!-- Age&#45;&gt;BMI -->\n",
       "<g id=\"edge30\" class=\"edge\"><title>Age&#45;&gt;BMI</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M862.859,-377.789C823.29,-377.28 750.807,-369.714 717,-324 679.125,-272.785 790.94,-248.728 852.453,-239.61\"/>\n",
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       "<!-- Age&#45;&gt;Red blood cells -->\n",
       "<g id=\"edge31\" class=\"edge\"><title>Age&#45;&gt;Red blood cells</title>\n",
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       "<!-- Age&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge32\" class=\"edge\"><title>Age&#45;&gt;predicted hazard</title>\n",
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       "<!-- Sex -->\n",
       "<g id=\"node21\" class=\"node\"><title>Sex</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1306\" cy=\"-378\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1306\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">Sex</text>\n",
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       "<!-- Sex&#45;&gt;Poverty index -->\n",
       "<g id=\"edge33\" class=\"edge\"><title>Sex&#45;&gt;Poverty index</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1280.8,-371.01C1239.76,-361.242 1155.93,-341.242 1085,-324 1080.3,-322.858 1075.43,-321.667 1070.54,-320.469\"/>\n",
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       "<!-- Sex&#45;&gt;Blood protein -->\n",
       "<g id=\"edge34\" class=\"edge\"><title>Sex&#45;&gt;Blood protein</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1332.75,-374.93C1399.71,-369.373 1572.66,-352.579 1622,-324 1647.79,-309.06 1668.31,-281.316 1680.95,-260.761\"/>\n",
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       "<!-- Sex&#45;&gt;Iron -->\n",
       "<g id=\"edge35\" class=\"edge\"><title>Sex&#45;&gt;Iron</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1332.58,-374.27C1369.13,-370.592 1436.99,-364.043 1495,-360 1535.34,-357.188 2194.12,-353.293 2222,-324 2238.74,-306.405 2226.72,-278.632 2214.02,-258.792\"/>\n",
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       "<!-- Sex&#45;&gt;Blood pressure -->\n",
       "<g id=\"edge36\" class=\"edge\"><title>Sex&#45;&gt;Blood pressure</title>\n",
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       "<!-- Sex&#45;&gt;Inflamation -->\n",
       "<g id=\"edge37\" class=\"edge\"><title>Sex&#45;&gt;Inflamation</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1298.14,-360.423C1288.5,-341.346 1270.75,-309.842 1249,-288 1236.02,-274.97 1219.32,-263.466 1204.38,-254.534\"/>\n",
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       "<!-- Sex&#45;&gt;Serum Magnesium -->\n",
       "<g id=\"edge38\" class=\"edge\"><title>Sex&#45;&gt;Serum Magnesium</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1279.5,-374.434C1241.22,-370.8 1168.24,-364.138 1106,-360 1064.38,-357.233 388.497,-350.155 356,-324 337.929,-309.456 332.628,-282.849 331.429,-262.502\"/>\n",
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       "<!-- Sex&#45;&gt;Serum Cholesterol -->\n",
       "<g id=\"edge39\" class=\"edge\"><title>Sex&#45;&gt;Serum Cholesterol</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1279.27,-374.723C1160.22,-364.578 684,-324 684,-324 666.202,-309.124 660.864,-282.561 659.583,-262.315\"/>\n",
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       "<!-- Sex&#45;&gt;BMI -->\n",
       "<g id=\"edge40\" class=\"edge\"><title>Sex&#45;&gt;BMI</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1279.16,-375.081C1195.2,-368.848 941.504,-348.166 914,-324 896.676,-308.778 891.251,-282.493 889.818,-262.423\"/>\n",
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       "<!-- Sex&#45;&gt;Red blood cells -->\n",
       "<g id=\"edge41\" class=\"edge\"><title>Sex&#45;&gt;Red blood cells</title>\n",
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       "<!-- Sex&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge42\" class=\"edge\"><title>Sex&#45;&gt;predicted hazard</title>\n",
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       "<!-- Race -->\n",
       "<g id=\"node22\" class=\"node\"><title>Race</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1533\" cy=\"-378\" rx=\"28.6953\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1533\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">Race</text>\n",
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       "<!-- Race&#45;&gt;Poverty index -->\n",
       "<g id=\"edge43\" class=\"edge\"><title>Race&#45;&gt;Poverty index</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1504.78,-374.883C1433.82,-369.295 1242.39,-352.586 1085,-324 1079.9,-323.073 1074.61,-321.992 1069.33,-320.832\"/>\n",
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       "<!-- Race&#45;&gt;Blood protein -->\n",
       "<g id=\"edge44\" class=\"edge\"><title>Race&#45;&gt;Blood protein</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1561.73,-377.219C1637.79,-377.126 1841.48,-372.285 1883,-324 1922.67,-277.875 1828.71,-253.915 1759.82,-242.923\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1760.28,-239.451 1749.86,-241.394 1759.21,-246.37 1760.28,-239.451\"/>\n",
       "</g>\n",
       "<!-- Race&#45;&gt;Iron -->\n",
       "<g id=\"edge45\" class=\"edge\"><title>Race&#45;&gt;Iron</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1561.69,-376.365C1709.91,-372.95 2383.13,-355.723 2413,-324 2468.25,-265.319 2308.33,-244.175 2232.95,-237.602\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2233.16,-234.108 2222.9,-236.769 2232.58,-241.084 2233.16,-234.108\"/>\n",
       "</g>\n",
       "<!-- Race&#45;&gt;Blood pressure -->\n",
       "<g id=\"edge46\" class=\"edge\"><title>Race&#45;&gt;Blood pressure</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1552.51,-364.819C1565.54,-355.451 1581.59,-341.214 1589,-324 1595.33,-309.304 1598.27,-301.044 1589,-288 1570.46,-261.901 1538.08,-248.547 1507.95,-241.758\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1508.35,-238.265 1497.85,-239.698 1506.95,-245.124 1508.35,-238.265\"/>\n",
       "</g>\n",
       "<!-- Race&#45;&gt;Inflamation -->\n",
       "<g id=\"edge47\" class=\"edge\"><title>Race&#45;&gt;Inflamation</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1509.27,-367.792C1448.66,-344.276 1287.48,-281.743 1209.9,-251.645\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1211.01,-248.321 1200.42,-247.966 1208.48,-254.847 1211.01,-248.321\"/>\n",
       "</g>\n",
       "<!-- Race&#45;&gt;Serum Magnesium -->\n",
       "<g id=\"edge48\" class=\"edge\"><title>Race&#45;&gt;Serum Magnesium</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1504.88,-374.045C1467.66,-370.237 1399.98,-363.673 1342,-360 1236.25,-353.301 480.986,-376.602 389,-324 376.415,-316.803 357.648,-284.579 345.126,-260.968\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"348.228,-259.346 340.499,-252.099 342.022,-262.584 348.228,-259.346\"/>\n",
       "</g>\n",
       "<!-- Race&#45;&gt;Serum Cholesterol -->\n",
       "<g id=\"edge49\" class=\"edge\"><title>Race&#45;&gt;Serum Cholesterol</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1504.87,-374.219C1467.63,-370.602 1399.94,-364.246 1342,-360 1272.63,-354.916 777.039,-359.126 717,-324 704.542,-316.712 685.869,-284.769 673.325,-261.227\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"676.263,-259.294 668.521,-252.061 670.063,-262.543 676.263,-259.294\"/>\n",
       "</g>\n",
       "<!-- Race&#45;&gt;BMI -->\n",
       "<g id=\"edge50\" class=\"edge\"><title>Race&#45;&gt;BMI</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1504.82,-374.492C1388.08,-364.101 948.514,-324.92 947,-324 934.525,-316.425 915.543,-283.897 902.959,-260.353\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"905.909,-258.442 898.156,-251.222 899.714,-261.701 905.909,-258.442\"/>\n",
       "</g>\n",
       "<!-- Race&#45;&gt;Red blood cells -->\n",
       "<g id=\"edge51\" class=\"edge\"><title>Race&#45;&gt;Red blood cells</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1561.55,-376.915C1682.18,-376.263 2145.91,-370.716 2189,-324 2252.81,-254.827 2083.47,-238.977 1978.74,-235.628\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1978.6,-232.123 1968.5,-235.334 1978.4,-239.12 1978.6,-232.123\"/>\n",
       "</g>\n",
       "<!-- Race&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge52\" class=\"edge\"><title>Race&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1561.94,-376.635C1707.14,-374.623 2352.55,-363.506 2432,-324 2478.57,-300.84 2508,-287.015 2508,-235 2508,-235 2508,-235 2508,-161 2508,-112.793 2488.04,-95.5948 2446,-72 2377.8,-33.7204 1854.43,-22.7353 1651.78,-19.8989\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1651.56,-16.3956 1641.51,-19.7583 1651.46,-23.3949 1651.56,-16.3956\"/>\n",
       "</g>\n",
       "<!-- Poverty index noise -->\n",
       "<g id=\"node23\" class=\"node\"><title>Poverty index noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1016\" cy=\"-378\" rx=\"81.4863\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1016\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">Poverty index noise</text>\n",
       "</g>\n",
       "<!-- Poverty index noise&#45;&gt;Poverty index -->\n",
       "<g id=\"edge53\" class=\"edge\"><title>Poverty index noise&#45;&gt;Poverty index</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1016,-359.697C1016,-351.983 1016,-342.712 1016,-334.112\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1019.5,-334.104 1016,-324.104 1012.5,-334.104 1019.5,-334.104\"/>\n",
       "</g>\n",
       "<!-- Blood protein noise -->\n",
       "<g id=\"node24\" class=\"node\"><title>Blood protein noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1793\" cy=\"-306\" rx=\"80.6858\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1793\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Blood protein noise</text>\n",
       "</g>\n",
       "<!-- Blood protein noise&#45;&gt;Blood protein -->\n",
       "<g id=\"edge56\" class=\"edge\"><title>Blood protein noise&#45;&gt;Blood protein</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1769.78,-288.411C1756.56,-278.969 1739.84,-267.029 1725.57,-256.834\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1727.22,-253.713 1717.05,-250.749 1723.15,-259.409 1727.22,-253.713\"/>\n",
       "</g>\n",
       "<!-- Iron noise -->\n",
       "<g id=\"node25\" class=\"node\"><title>Iron noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2358\" cy=\"-306\" rx=\"46.2923\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2358\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Iron noise</text>\n",
       "</g>\n",
       "<!-- Iron noise&#45;&gt;Iron -->\n",
       "<g id=\"edge60\" class=\"edge\"><title>Iron noise&#45;&gt;Iron</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2328.29,-292.161C2299.73,-279.821 2256.72,-261.236 2227.56,-248.636\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"2228.81,-245.363 2218.24,-244.609 2226.03,-251.789 2228.81,-245.363\"/>\n",
       "</g>\n",
       "<!-- Pulse pressure&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge66\" class=\"edge\"><title>Pulse pressure&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1537.89,-72.055C1543.25,-63.7147 1549.83,-53.4908 1555.77,-44.2517\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1558.74,-46.0935 1561.21,-35.789 1552.86,-42.3081 1558.74,-46.0935\"/>\n",
       "</g>\n",
       "<!-- Blood pressure noise -->\n",
       "<g id=\"node27\" class=\"node\"><title>Blood pressure noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1495\" cy=\"-306\" rx=\"85.2851\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1495\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Blood pressure noise</text>\n",
       "</g>\n",
       "<!-- Blood pressure noise&#45;&gt;Blood pressure -->\n",
       "<g id=\"edge65\" class=\"edge\"><title>Blood pressure noise&#45;&gt;Blood pressure</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1480.96,-288.055C1473.91,-279.543 1465.23,-269.068 1457.45,-259.681\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1459.99,-257.256 1450.91,-251.789 1454.6,-261.722 1459.99,-257.256\"/>\n",
       "</g>\n",
       "<!-- Inflamation noise -->\n",
       "<g id=\"node28\" class=\"node\"><title>Inflamation noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1167\" cy=\"-306\" rx=\"73.387\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1167\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Inflamation noise</text>\n",
       "</g>\n",
       "<!-- Inflamation noise&#45;&gt;Inflamation -->\n",
       "<g id=\"edge69\" class=\"edge\"><title>Inflamation noise&#45;&gt;Inflamation</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1167,-287.697C1167,-279.983 1167,-270.712 1167,-262.112\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.5,-262.104 1167,-252.104 1163.5,-262.104 1170.5,-262.104\"/>\n",
       "</g>\n",
       "<!-- Serum Magnesium noise -->\n",
       "<g id=\"node29\" class=\"node\"><title>Serum Magnesium noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"166\" cy=\"-306\" rx=\"100.182\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"166\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Serum Magnesium noise</text>\n",
       "</g>\n",
       "<!-- Serum Magnesium noise&#45;&gt;Serum Magnesium -->\n",
       "<g id=\"edge70\" class=\"edge\"><title>Serum Magnesium noise&#45;&gt;Serum Magnesium</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M203.254,-289.291C227.943,-278.88 260.486,-265.157 286.676,-254.113\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"288.157,-257.287 296.011,-250.176 285.437,-250.837 288.157,-257.287\"/>\n",
       "</g>\n",
       "<!-- Serum Cholesterol noise -->\n",
       "<g id=\"node30\" class=\"node\"><title>Serum Cholesterol noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"496\" cy=\"-306\" rx=\"98.2828\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"496\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Serum Cholesterol noise</text>\n",
       "</g>\n",
       "<!-- Serum Cholesterol noise&#45;&gt;Serum Cholesterol -->\n",
       "<g id=\"edge71\" class=\"edge\"><title>Serum Cholesterol noise&#45;&gt;Serum Cholesterol</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M532.805,-289.291C557.281,-278.844 589.569,-265.062 615.489,-253.999\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"616.901,-257.201 624.724,-250.057 614.153,-250.763 616.901,-257.201\"/>\n",
       "</g>\n",
       "<!-- BMI noise -->\n",
       "<g id=\"node31\" class=\"node\"><title>BMI noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"775\" cy=\"-306\" rx=\"48.9926\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"775\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">BMI noise</text>\n",
       "</g>\n",
       "<!-- BMI noise&#45;&gt;BMI -->\n",
       "<g id=\"edge72\" class=\"edge\"><title>BMI noise&#45;&gt;BMI</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M799.108,-290.326C817.302,-279.251 842.323,-264.021 861.507,-252.343\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"863.41,-255.282 870.133,-247.093 859.771,-249.303 863.41,-255.282\"/>\n",
       "</g>\n",
       "<!-- Red blood cells noise -->\n",
       "<g id=\"node32\" class=\"node\"><title>Red blood cells noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2093\" cy=\"-306\" rx=\"87.1846\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2093\" y=\"-302.3\" font-family=\"Times,serif\" font-size=\"14.00\">Red blood cells noise</text>\n",
       "</g>\n",
       "<!-- Red blood cells noise&#45;&gt;Red blood cells -->\n",
       "<g id=\"edge73\" class=\"edge\"><title>Red blood cells noise&#45;&gt;Red blood cells</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2052.24,-289.983C2022.2,-278.916 1981.28,-263.839 1949.88,-252.272\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1951.06,-248.977 1940.47,-248.804 1948.64,-255.545 1951.06,-248.977\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f1a1304e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = GraphExplainer(causal_graph, bg[:1]) # an example\n",
    "E.prepare_graph(fg) # add noise node\n",
    "G = copy.deepcopy(E.graph) # group happens after noise node b/c the grouped nodes combines individual noise node\n",
    "G = group_nodes(G, [n for n in G if n.name in ['White blood cells', 'Sedimentation rate']], 'Inflamation')\n",
    "G = group_nodes(G, [n for n in G if n.name in ['Systolic BP', 'Diastolic BP']], 'Blood pressure')\n",
    "G = group_nodes(G, [n for n in G if n.name in ['TS', 'TIBC', 'Serum Iron']], 'Iron')\n",
    "G = group_nodes(G, [n for n in G if n.name in ['Serum Protein', 'Serum Albumin']], 'Blood protein')\n",
    "G.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bg 0/100\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
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      "bg 1/100\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
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      "bg 99/100\n"
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    },
    {
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     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# multiple background result with individual run\n",
    "causal_edge_credits = [] # save for each run\n",
    "for i in range(len(bg)): # average run result using linearity axiom\n",
    "    print(f'bg {i}/{len(bg)}')\n",
    "    for j in range(nruns):\n",
    "        E = GraphExplainer(causal_graph, bg[i:i+1])\n",
    "        E.prepare_graph(fg) # add noise node\n",
    "        G = copy.deepcopy(E.graph) # group happens after noise node b/c the grouped nodes combines individual noise node\n",
    "        G = group_nodes(G, [n for n in G if n.name in ['White blood cells', 'Sedimentation rate']], 'Inflamation')\n",
    "        G = group_nodes(G, [n for n in G if n.name in ['Systolic BP', 'Diastolic BP']], 'Blood pressure')\n",
    "        G = group_nodes(G, [n for n in G if n.name in ['TS', 'TIBC', 'Serum Iron']], 'Iron')\n",
    "        G = group_nodes(G, [n for n in G if n.name in ['Serum Protein', 'Serum Albumin']], 'Blood protein')\n",
    "\n",
    "        explainer = GraphExplainer(G, bg[i:i+1], nruns=1) \n",
    "        cf_c = explainer.shap_values(fg, skip_prepare=True)        \n",
    "        causal_edge_credits.append(node_dict2str_dict(cf_c.edge_credit)) # use node name is more portable vs. node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
       " -->\n",
       "<!-- Title: %3 Pages: 1 -->\n",
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       "<title>%3</title>\n",
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       "<!-- Poverty index -->\n",
       "<g id=\"node1\" class=\"node\"><title>Poverty index</title>\n",
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       "<g id=\"edge1\" class=\"edge\"><title>Poverty index&#45;&gt;Blood protein</title>\n",
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       "<!-- Serum Magnesium -->\n",
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       "<!-- Poverty index&#45;&gt;Serum Cholesterol -->\n",
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       "<!-- Poverty index&#45;&gt;BMI -->\n",
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       "<!-- predicted hazard -->\n",
       "<g id=\"node10\" class=\"node\"><title>predicted hazard</title>\n",
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       "<!-- Poverty index&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge9\" class=\"edge\"><title>Poverty index&#45;&gt;predicted hazard</title>\n",
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       "<!-- Serum Albumin -->\n",
       "<g id=\"node11\" class=\"node\"><title>Serum Albumin</title>\n",
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       "<text text-anchor=\"middle\" x=\"1768\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">Serum Albumin</text>\n",
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       "<!-- Blood protein&#45;&gt;Serum Albumin -->\n",
       "<g id=\"edge10\" class=\"edge\"><title>Blood protein&#45;&gt;Serum Albumin</title>\n",
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       "<!-- Serum Protein -->\n",
       "<g id=\"node12\" class=\"node\"><title>Serum Protein</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1695\" cy=\"-162\" rx=\"61.99\" ry=\"18\"/>\n",
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       "<!-- Blood protein&#45;&gt;Serum Protein -->\n",
       "<g id=\"edge11\" class=\"edge\"><title>Blood protein&#45;&gt;Serum Protein</title>\n",
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       "<polygon fill=\"black\" stroke=\"black\" points=\"1698.5,-190.104 1695,-180.104 1691.5,-190.104 1698.5,-190.104\"/>\n",
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       "<!-- TS -->\n",
       "<g id=\"node13\" class=\"node\"><title>TS</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1950\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1950\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">TS</text>\n",
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       "<!-- Iron&#45;&gt;TS -->\n",
       "<g id=\"edge12\" class=\"edge\"><title>Iron&#45;&gt;TS</title>\n",
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       "<!-- TIBC -->\n",
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       "<text text-anchor=\"middle\" x=\"2032\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">TIBC</text>\n",
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       "<!-- Iron&#45;&gt;TIBC -->\n",
       "<g id=\"edge13\" class=\"edge\"><title>Iron&#45;&gt;TIBC</title>\n",
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       "<!-- Serum Iron -->\n",
       "<g id=\"node15\" class=\"node\"><title>Serum Iron</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2135\" cy=\"-90\" rx=\"51.1914\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"2135\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">Serum Iron</text>\n",
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       "<!-- Iron&#45;&gt;Serum Iron -->\n",
       "<g id=\"edge14\" class=\"edge\"><title>Iron&#45;&gt;Serum Iron</title>\n",
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       "<!-- Systolic BP -->\n",
       "<g id=\"node16\" class=\"node\"><title>Systolic BP</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1563\" cy=\"-162\" rx=\"51.9908\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1563\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">Systolic BP</text>\n",
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       "<!-- Blood pressure&#45;&gt;Systolic BP -->\n",
       "<g id=\"edge15\" class=\"edge\"><title>Blood pressure&#45;&gt;Systolic BP</title>\n",
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       "<!-- Diastolic BP -->\n",
       "<g id=\"node17\" class=\"node\"><title>Diastolic BP</title>\n",
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       "<text text-anchor=\"middle\" x=\"1437\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">Diastolic BP</text>\n",
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       "<!-- Blood pressure&#45;&gt;Diastolic BP -->\n",
       "<g id=\"edge16\" class=\"edge\"><title>Blood pressure&#45;&gt;Diastolic BP</title>\n",
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       "<!-- White blood cells -->\n",
       "<g id=\"node18\" class=\"node\"><title>White blood cells</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1290\" cy=\"-162\" rx=\"73.387\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"1290\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">White blood cells</text>\n",
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       "<!-- Inflamation&#45;&gt;White blood cells -->\n",
       "<g id=\"edge17\" class=\"edge\"><title>Inflamation&#45;&gt;White blood cells</title>\n",
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       "<!-- Sedimentation rate -->\n",
       "<g id=\"node19\" class=\"node\"><title>Sedimentation rate</title>\n",
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       "<text text-anchor=\"middle\" x=\"1121\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">Sedimentation rate</text>\n",
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       "<!-- Inflamation&#45;&gt;Sedimentation rate -->\n",
       "<g id=\"edge18\" class=\"edge\"><title>Inflamation&#45;&gt;Sedimentation rate</title>\n",
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       "<!-- Serum Magnesium&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge19\" class=\"edge\"><title>Serum Magnesium&#45;&gt;predicted hazard</title>\n",
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       "<!-- Serum Cholesterol&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge20\" class=\"edge\"><title>Serum Cholesterol&#45;&gt;predicted hazard</title>\n",
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       "<!-- BMI&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge21\" class=\"edge\"><title>BMI&#45;&gt;predicted hazard</title>\n",
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       "<!-- Red blood cells&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge22\" class=\"edge\"><title>Red blood cells&#45;&gt;predicted hazard</title>\n",
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       "<!-- Serum Albumin&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge54\" class=\"edge\"><title>Serum Albumin&#45;&gt;predicted hazard</title>\n",
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       "<!-- Serum Protein&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge55\" class=\"edge\"><title>Serum Protein&#45;&gt;predicted hazard</title>\n",
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       "<!-- TS&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge57\" class=\"edge\"><title>TS&#45;&gt;predicted hazard</title>\n",
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       "<!-- TIBC&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge58\" class=\"edge\"><title>TIBC&#45;&gt;predicted hazard</title>\n",
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       "<!-- Serum Iron&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge59\" class=\"edge\"><title>Serum Iron&#45;&gt;predicted hazard</title>\n",
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       "<!-- Systolic BP&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge61\" class=\"edge\"><title>Systolic BP&#45;&gt;predicted hazard</title>\n",
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       "<!-- Pulse pressure -->\n",
       "<g id=\"node26\" class=\"node\"><title>Pulse pressure</title>\n",
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       "<!-- Systolic BP&#45;&gt;Pulse pressure -->\n",
       "<g id=\"edge62\" class=\"edge\"><title>Systolic BP&#45;&gt;Pulse pressure</title>\n",
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       "<!-- Diastolic BP&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge63\" class=\"edge\"><title>Diastolic BP&#45;&gt;predicted hazard</title>\n",
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       "<!-- Diastolic BP&#45;&gt;Pulse pressure -->\n",
       "<g id=\"edge64\" class=\"edge\"><title>Diastolic BP&#45;&gt;Pulse pressure</title>\n",
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       "<!-- White blood cells&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge67\" class=\"edge\"><title>White blood cells&#45;&gt;predicted hazard</title>\n",
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       "<!-- Sedimentation rate&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge68\" class=\"edge\"><title>Sedimentation rate&#45;&gt;predicted hazard</title>\n",
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       "<!-- Age -->\n",
       "<g id=\"node20\" class=\"node\"><title>Age</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"890\" cy=\"-378\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"890\" y=\"-374.3\" font-family=\"Times,serif\" font-size=\"14.00\">Age</text>\n",
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       "<!-- Age&#45;&gt;Poverty index -->\n",
       "<g id=\"edge23\" class=\"edge\"><title>Age&#45;&gt;Poverty index</title>\n",
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       "<!-- Age&#45;&gt;Blood protein -->\n",
       "<g id=\"edge24\" class=\"edge\"><title>Age&#45;&gt;Blood protein</title>\n",
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       "<!-- Age&#45;&gt;Iron -->\n",
       "<g id=\"edge25\" class=\"edge\"><title>Age&#45;&gt;Iron</title>\n",
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       "<!-- Age&#45;&gt;Blood pressure -->\n",
       "<g id=\"edge26\" class=\"edge\"><title>Age&#45;&gt;Blood pressure</title>\n",
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       "<!-- Age&#45;&gt;Inflamation -->\n",
       "<g id=\"edge27\" class=\"edge\"><title>Age&#45;&gt;Inflamation</title>\n",
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       "<!-- Age&#45;&gt;Serum Magnesium -->\n",
       "<g id=\"edge28\" class=\"edge\"><title>Age&#45;&gt;Serum Magnesium</title>\n",
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       "<!-- Age&#45;&gt;Serum Cholesterol -->\n",
       "<g id=\"edge29\" class=\"edge\"><title>Age&#45;&gt;Serum Cholesterol</title>\n",
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       "<!-- Age&#45;&gt;BMI -->\n",
       "<g id=\"edge30\" class=\"edge\"><title>Age&#45;&gt;BMI</title>\n",
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       "<!-- Age&#45;&gt;Red blood cells -->\n",
       "<g id=\"edge31\" class=\"edge\"><title>Age&#45;&gt;Red blood cells</title>\n",
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       "<!-- Age&#45;&gt;predicted hazard -->\n",
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       "<g id=\"edge65\" class=\"edge\"><title>Blood pressure noise&#45;&gt;Blood pressure</title>\n",
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       "<g id=\"edge71\" class=\"edge\"><title>Serum Cholesterol noise&#45;&gt;Serum Cholesterol</title>\n",
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       "<g id=\"edge72\" class=\"edge\"><title>BMI noise&#45;&gt;BMI</title>\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x7f1aa4efa290>"
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   ],
   "source": [
    "cf_c.graph.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "</svg>\n"
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      "text/plain": [
       "<graphviz.files.Source at 0x7f1aa4ea29d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AGraph <Swig Object of type 'Agraph_t *' at 0x7f1aa4e18120>>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_c.show_CI = True\n",
    "cf_c.draw(sample_ind, max_display=10, show_fg_val=False, edge_credit=edge_credits2edge_credit(causal_edge_credits, cf_c.graph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methods comparison: flow, independent, on-manifold, ASV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "</g>\n",
       "<!-- Age -->\n",
       "<g id=\"node10\" class=\"node\"><title>Age</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"301.995\" cy=\"-213\" rx=\"27\" ry=\"18\"/>\n",
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       "</g>\n",
       "<!-- Age&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge10\" class=\"edge\"><title>Age&#45;&gt;predicted hazard</title>\n",
       "<path fill=\"none\" stroke=\"#008bfb\" stroke-width=\"5\" d=\"M328.574,-209.414C394.471,-202.882 560.687,-185.631 568.995,-177 607.034,-137.487 614.673,-95.6568 578.995,-54 560.316,-32.1902 490.377,-23.8625 433.373,-20.7387\"/>\n",
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       "</g>\n",
       "</svg>\n"
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      "text/plain": [
       "<graphviz.files.Source at 0x7f1aa4c095d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_ind = 99\n",
    "cf_c.fold_noise = True # False\n",
    "cf_c.show_CI = True\n",
    "g = cf_c.draw(sample_ind, max_display=10, show_fg_val=False, edge_credit=edge_credits2edge_credit(causal_edge_credits, cf_c.graph))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<AGraph <Swig Object of type 'Agraph_t *' at 0x7f1a12fa6e70>>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf.show_CI = True\n",
    "cf.draw(sample_ind, max_display=10, show_fg_val=False, edge_credit=edge_credits2edge_credit(edge_credits, cf.graph))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cf_on_manifold.draw(sample_ind, max_display=10)\n",
    "sum(manifold_edge_credits).draw(sample_ind, show=False, max_display=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
       " -->\n",
       "<!-- Title: %3 Pages: 1 -->\n",
       "<svg width=\"1419pt\" height=\"131pt\"\n",
       " viewBox=\"0.00 0.00 1419.14 131.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 127)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-127 1415.14,-127 1415.14,4 -4,4\"/>\n",
       "<!-- Blood pressure noise -->\n",
       "<g id=\"node1\" class=\"node\"><title>Blood pressure noise</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"85.1424\" cy=\"-105\" rx=\"85.2851\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"85.1424\" y=\"-101.3\" font-family=\"Times,serif\" font-size=\"14.00\">Blood pressure noise</text>\n",
       "</g>\n",
       "<!-- predicted hazard -->\n",
       "<g id=\"node2\" class=\"node\"><title>predicted hazard</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"151.142\" cy=\"-18\" rx=\"69.5877\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"151.142\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">predicted hazard</text>\n",
       "</g>\n",
       "<!-- Blood pressure noise&#45;&gt;predicted hazard -->\n",
       "<g id=\"edge1\" class=\"edge\"><title>Blood pressure noise&#45;&gt;predicted hazard</title>\n",
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       "</g>\n",
       "<!-- Serum Magnesium noise -->\n",
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       "</g>\n",
       "<!-- Inflamation noise -->\n",
       "<g id=\"node8\" class=\"node\"><title>Inflamation noise</title>\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x7f1aa7427410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_c.fold_noise = False\n",
    "cf_c.show_CI = True\n",
    "g = cf_c.draw_asv(sample_ind, max_display=10, show_fg_val=False, flow_credit=edge_credits2edge_credit(causal_edge_credits, cf_c.graph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigate the background and foreground samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Age</th>\n",
       "      <th>Diastolic BP</th>\n",
       "      <th>Poverty index</th>\n",
       "      <th>Race</th>\n",
       "      <th>Red blood cells</th>\n",
       "      <th>Sedimentation rate</th>\n",
       "      <th>Serum Albumin</th>\n",
       "      <th>Serum Cholesterol</th>\n",
       "      <th>Serum Iron</th>\n",
       "      <th>Serum Magnesium</th>\n",
       "      <th>Serum Protein</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Systolic BP</th>\n",
       "      <th>TIBC</th>\n",
       "      <th>TS</th>\n",
       "      <th>White blood cells</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Pulse pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean background samples</th>\n",
       "      <td>50.84</td>\n",
       "      <td>82.87</td>\n",
       "      <td>334.88</td>\n",
       "      <td>1.11</td>\n",
       "      <td>56.761</td>\n",
       "      <td>17.0022</td>\n",
       "      <td>4.372</td>\n",
       "      <td>224.42</td>\n",
       "      <td>101.81</td>\n",
       "      <td>1.6936</td>\n",
       "      <td>7.116</td>\n",
       "      <td>1.64</td>\n",
       "      <td>134.92</td>\n",
       "      <td>366.71</td>\n",
       "      <td>28.069</td>\n",
       "      <td>7.42383</td>\n",
       "      <td>25.6405</td>\n",
       "      <td>52.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foreground sample</th>\n",
       "      <td>51</td>\n",
       "      <td>74</td>\n",
       "      <td>824</td>\n",
       "      <td>1</td>\n",
       "      <td>77.7</td>\n",
       "      <td>9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>267</td>\n",
       "      <td>63</td>\n",
       "      <td>1.61</td>\n",
       "      <td>6.8</td>\n",
       "      <td>Female</td>\n",
       "      <td>118</td>\n",
       "      <td>503</td>\n",
       "      <td>12.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>23.589</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Age Diastolic BP Poverty index  Race  \\\n",
       "mean background samples  50.84        82.87        334.88  1.11   \n",
       "foreground sample           51           74           824     1   \n",
       "\n",
       "                        Red blood cells Sedimentation rate Serum Albumin  \\\n",
       "mean background samples          56.761            17.0022         4.372   \n",
       "foreground sample                  77.7                  9           4.3   \n",
       "\n",
       "                        Serum Cholesterol Serum Iron Serum Magnesium  \\\n",
       "mean background samples            224.42     101.81          1.6936   \n",
       "foreground sample                     267         63            1.61   \n",
       "\n",
       "                        Serum Protein     Sex Systolic BP    TIBC      TS  \\\n",
       "mean background samples         7.116    1.64      134.92  366.71  28.069   \n",
       "foreground sample                 6.8  Female         118     503    12.5   \n",
       "\n",
       "                        White blood cells      BMI Pulse pressure  \n",
       "mean background samples           7.42383  25.6405          52.05  \n",
       "foreground sample                     4.4   23.589             44  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([bg.mean(0), X_display.iloc[sample_ind]], 1).rename(columns={0: \"mean background samples\", sample_ind: 'foreground sample'}).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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"
    }
   ],
   "source": [
    "o = f(np.array(fg))\n",
    "\n",
    "xlabel = 'Age'\n",
    "ylabel = 'Output'\n",
    "\n",
    "sns.regplot(fg[xlabel], o, x_bins=1, order=1, ci=None, label='regression line', color='y')\n",
    "plt.scatter(fg[xlabel], o, alpha=0.3, label='samples')\n",
    "plt.xlabel(xlabel, fontsize=15)\n",
    "plt.ylabel(ylabel, fontsize=15)\n",
    "plt.scatter(bg[xlabel].mean(), f(np.array(bg)).mean(), c=['red'], s=100, marker='s', label='background (mean)')\n",
    "plt.scatter(X[xlabel][[sample_ind]], o[[sample_ind]], c=['black'], s=100, marker='s', label='foreground')\n",
    "plt.legend(fontsize=15)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Increasing the age, increases log hazard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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"
    }
   ],
   "source": [
    "o = f(np.array(fg))\n",
    "\n",
    "def normalize(o): # normalize to 0-1\n",
    "    return (o - o.min()) / (o.max() - o.min())\n",
    "\n",
    "ylabel = 'Age'\n",
    "xlabel = 'Systolic BP' #'Serum Magnesium'\n",
    "\n",
    "plt.scatter(fg[xlabel], fg[ylabel],\n",
    "            alpha=0.3, label='samples')\n",
    "sns.regplot(fg[xlabel], fg[ylabel], x_bins=1, order=1, ci=None, label='regression line', color='y')\n",
    "plt.xlabel(xlabel, fontsize=15)\n",
    "plt.ylabel(ylabel, fontsize=15)\n",
    "\n",
    "plt.scatter(bg[xlabel].mean(), bg[ylabel].mean(), c=['red'], s=100, marker='s', label='background (mean)')\n",
    "plt.scatter(X[xlabel][[sample_ind]], X[ylabel][[sample_ind]], c=['black'], s=100, marker='s', label='foreground')\n",
    "plt.legend(fontsize=15, loc=\"upper left\")\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'figures/manifold_multiple_explain_age_{xlabel}_{sample_ind}.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updating Systolic BP before Age would cause the expeted age to decrease, thus greating inflating the risk of Age for this sample"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}