bullseye3d_experiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "import h5py\n",
    "import pickle\n",
    "import json\n",
    "import torch\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "\n",
    "from performance import *\n",
    "from pycit import *\n",
    "from bullseye import bullseye_network, get_ci_dict\n",
    "from mapping import ModelManager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# make data with illustrated DAG structure\n",
    "num_samples = 5000 \n",
    "dim = 3\n",
    "r_data, x_data, y_data = bullseye_network(num_samples, dim, eps=0.075)\n",
    "r_data = standardize(r_data.astype(np.float16))\n",
    "x_data = standardize(x_data.astype(np.float16))\n",
    "y_data = standardize(y_data.astype(np.float16))\n",
    "\n",
    "np.save(\"data/bullseye3dY.npy\", y_data)\n",
    "np.save(\"data/bullseye3dX.npy\", x_data)\n",
    "\n",
    "# plot dag structure\n",
    "plt.figure()\n",
    "plt.imshow(mpimg.imread('assets/dag7.png'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## learn feature mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Validation performance didn't improve for 50 epochs. Training stops.\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "config_path = \"config/bullseye3d.json\"\n",
    "manager = ModelManager(config_path, make_plots=True)\n",
    "manager.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load learned feature mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "manager.load_model(checkpoint_file=\"model_best.pth\")\n",
    "mu,lv,z = manager.process_numpy(x_data)\n",
    "z_data = standardize(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## set cit settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CONFIDENCE_LEVEL = 0.95\n",
    "K_KNN = 5\n",
    "K_PERM = 5\n",
    "SUBSAMPLE_SIZE = None\n",
    "N_TRIALS = 250\n",
    "N_JOBS = 4\n",
    "\n",
    "cit_funcs = {\n",
    "    'it_args': {\n",
    "        'statistic_args': {\n",
    "            'k': K_KNN\n",
    "        },\n",
    "        'test_args': {\n",
    "            'statistic': 'mixed_mi',\n",
    "            'k_perm': K_PERM,\n",
    "            'subsample_size': SUBSAMPLE_SIZE,\n",
    "            'n_trials': N_TRIALS,\n",
    "            'n_jobs': N_JOBS\n",
    "        }\n",
    "    },\n",
    "    'cit_args': {\n",
    "        'statistic_args': {\n",
    "            'k': K_KNN\n",
    "        },\n",
    "        'test_args': {\n",
    "            'statistic': 'mixed_cmi',\n",
    "            'k_perm': K_PERM,\n",
    "            'subsample_size': SUBSAMPLE_SIZE,\n",
    "            'n_trials': N_TRIALS,\n",
    "            'n_jobs': N_JOBS\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## find Markov blanket using original X data\n",
    "\n",
    "* The algorithm fails to find the correct Markov Blanket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "==========Finding Adjacents...==========\nTesting X_{6}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{4}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{1}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{2}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{3}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{5}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{6}\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\nTesting X_{4}\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.000\nTesting X_{1}\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.032\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\nTesting X_{2}\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.004\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\nTesting X_{3}\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\nTesting X_{5}\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\nTesting X_{6}\n  Cond. set: ['X_{1}', 'X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{3}']\n    Is CI: False, pval: 0.000\nTesting X_{4}\n  Cond. set: ['X_{2}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{5}']\n    Is CI: False, pval: 0.000\nTesting X_{1}\n  Cond. set: ['X_{4}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{5}']\n    Is CI: False, pval: 0.008\n  Cond. set: ['X_{3}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{3}']\n    Is CI: False, pval: 0.000\nTesting X_{2}\n  Cond. set: ['X_{1}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{4}']\n    Is CI: False, pval: 0.000\nTesting X_{3}\n  Cond. set: ['X_{4}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{6}']\n    Is CI: False, pval: 0.000\nTesting X_{5}\n  Cond. set: ['X_{1}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{4}']\n    Is CI: False, pval: 0.000\nTesting X_{6}\n  Cond. set: ['X_{1}', 'X_{2}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{4}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{4}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{4}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{3}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{2}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{3}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{3}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{3}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{2}', 'X_{5}']\n    Is CI: False, pval: 0.000\nTesting X_{4}\n  Cond. set: ['X_{1}', 'X_{2}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{2}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{3}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{3}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{2}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.036\n  Cond. set: ['X_{1}', 'X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{5}', 'X_{6}']\n    Is CI: True, pval: 0.216\nTesting X_{1}\n  Cond. set: ['X_{2}', 'X_{3}', 'X_{5}']\n    Is CI: True, pval: 0.436\nTesting X_{2}\n  Cond. set: ['X_{3}', 'X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.004\nTesting X_{3}\n  Cond. set: ['X_{2}', 'X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.004\nTesting X_{5}\n  Cond. set: ['X_{2}', 'X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.000\nTesting X_{6}\nTesting X_{2}\nTesting X_{3}\nTesting X_{5}\nTesting X_{6}\nTesting X_{2}\nTesting X_{3}\nTesting X_{5}\nAdjacents found: ['X_{2}', 'X_{3}', 'X_{5}', 'X_{6}']\n==========Finding Coparents...==========\nTesting X_{4}\n    Is dependent: False, pval: 1.000\nTesting X_{1}\n    Is dependent: False, pval: 1.000\nDiscovered Markov blanket: ['X_{2}', 'X_{3}', 'X_{5}', 'X_{6}']\n"
    }
   ],
   "source": [
    "mb = MarkovBlanket(x_data, y_data, cit_funcs)\n",
    "selected = mb.find_markov_blanket(confidence=CONFIDENCE_LEVEL, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## find Markov blanket using mapped features\n",
    "\n",
    "* The algorithm finds the correct Markov Blanket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "==========Finding Adjacents...==========\nTesting X_{5}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{3}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{2}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{6}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{1}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{4}\n  Cond. set: []\n    Is CI: False, pval: 0.000\nTesting X_{5}\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\nTesting X_{3}\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\nTesting X_{2}\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\nTesting X_{6}\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\nTesting X_{1}\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\nTesting X_{4}\n  Cond. set: ['X_{1}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{6}']\n    Is CI: False, pval: 0.000\nTesting X_{5}\n  Cond. set: ['X_{3}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{6}']\n    Is CI: False, pval: 0.000\nTesting X_{3}\n  Cond. set: ['X_{1}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{2}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{2}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{4}']\n    Is CI: False, pval: 0.000\nTesting X_{2}\n  Cond. set: ['X_{1}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{5}']\n    Is CI: True, pval: 0.136\nTesting X_{6}\n  Cond. set: ['X_{3}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{3}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{4}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{1}', 'X_{5}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{4}', 'X_{5}']\n    Is CI: False, pval: 0.000\nTesting X_{1}\n  Cond. set: ['X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.036\n  Cond. set: ['X_{3}', 'X_{5}']\n    Is CI: True, pval: 0.956\nTesting X_{4}\n  Cond. set: ['X_{5}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{6}']\n    Is CI: False, pval: 0.000\n  Cond. set: ['X_{3}', 'X_{5}']\n    Is CI: True, pval: 0.416\nTesting X_{5}\nTesting X_{3}\nTesting X_{6}\nTesting X_{5}\nTesting X_{3}\nTesting X_{6}\nTesting X_{5}\nTesting X_{3}\nTesting X_{6}\nAdjacents found: ['X_{3}', 'X_{5}', 'X_{6}']\n==========Finding Coparents...==========\nTesting X_{2}\n    Is dependent: False, pval: 0.480\nTesting X_{4}\n    Is dependent: True, pval: 0.004\nTesting X_{1}\n    Is dependent: False, pval: 0.500\nDiscovered Markov blanket: ['X_{3}', 'X_{4}', 'X_{5}', 'X_{6}']\n"
    }
   ],
   "source": [
    "mb = MarkovBlanket(z_data, y_data, cit_funcs)\n",
    "selected = mb.find_markov_blanket(confidence=CONFIDENCE_LEVEL, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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