IDB_drilling_monitoring_shap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mA_-eAkuEnLC"
   },
   "source": [
    "# IDB_drilling_monitoring_shap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G6Dqfh0oC9d7"
   },
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "8KJudV78mY5m",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-17 10:17:05.131493: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Nov 17 10:17:06 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA GeForce ...  On   | 00000000:98:00.0 Off |                  Off |\n",
      "| 30%   27C    P8    18W / 450W |      2MiB / 24564MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "import gc\n",
    "import IPython\n",
    "import librosa\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import scipy.io as scio\n",
    "import shap\n",
    "import soundfile\n",
    "import time\n",
    "from keras.layers import Activation, BatchNormalization, Concatenate, Conv1D, Conv2D, Dense, Dropout, Flatten\n",
    "from keras.layers import GlobalAveragePooling1D, GlobalAveragePooling2D, Input, MaxPooling1D, MaxPooling2D\n",
    "from keras.models import load_model, Model, Sequential\n",
    "from keras.utils import to_categorical\n",
    "from pyts.image import RecurrencePlot, MarkovTransitionField, GramianAngularField\n",
    "from sklearn.metrics import confusion_matrix, mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow import keras\n",
    "from tqdm import tqdm\n",
    "\n",
    "!nvidia-smi\n",
    "\n",
    "HOST_path = \"/root/autodl-tmp\"\n",
    "SETS_path = os.path.join(HOST_path, \"IDB_drilling_monitoring/datasets\")\n",
    "SAVE_path = os.path.join(HOST_path, \"IDB_drilling_monitoring/shap\")\n",
    "\n",
    "if not os.path.exists(SAVE_path):\n",
    "    os.makedirs(SAVE_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pAQJZnnvpeT0"
   },
   "source": [
    "## Import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35524, 12000)\n",
      "(35524, 12000, 1)\n",
      "(35524, 7)\n",
      "(35524, 7)\n",
      "(35524, 6)\n",
      "(35524,)\n",
      "(35524,)\n",
      "(35524,)\n",
      "(35524,)\n",
      "(35524,)\n",
      "(35524,)\n",
      "(35524,)\n"
     ]
    }
   ],
   "source": [
    "X_set = np.load(os.path.join(SETS_path, \"X_set.npy\"))\n",
    "X_set_cnn = np.load(os.path.join(SETS_path, \"X_set_cnn.npy\"))\n",
    "Y_set = np.load(os.path.join(SETS_path, \"Y_set.npy\"))\n",
    "ALL7 = np.load(os.path.join(SETS_path, \"ALL7.npy\"))\n",
    "ALL6 = np.load(os.path.join(SETS_path, \"ALL6.npy\"))\n",
    "HEIGHT = np.load(os.path.join(SETS_path, \"HEIGHT.npy\"))\n",
    "RPM = np.load(os.path.join(SETS_path, \"RPM.npy\"))\n",
    "TORQUE = np.load(os.path.join(SETS_path, \"TORQUE.npy\"))\n",
    "PUMPR = np.load(os.path.join(SETS_path, \"PUMPR.npy\"))\n",
    "PUMPP = np.load(os.path.join(SETS_path, \"PUMPP.npy\"))\n",
    "ROP = np.load(os.path.join(SETS_path, \"ROP.npy\"))\n",
    "WOB = np.load(os.path.join(SETS_path, \"WOB.npy\"))\n",
    "\n",
    "print(X_set.shape)\n",
    "print(X_set_cnn.shape)\n",
    "print(Y_set.shape)\n",
    "print(ALL7.shape)\n",
    "print(ALL6.shape)\n",
    "print(HEIGHT.shape)\n",
    "print(RPM.shape)\n",
    "print(TORQUE.shape)\n",
    "print(PUMPR.shape)\n",
    "print(PUMPP.shape)\n",
    "print(ROP.shape)\n",
    "print(WOB.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqsdaJal345yTOZV9yz3hrNWTSEZlbpLXA7uq6q3d+i8CL6+qK4faPNC1me/WH+3afGnJbe0H9nerLwGOnGTdW4EvjWw1HezLxtRKX1rpB9iXRS+sqpnldqzrSdGqOggcPNXbSTJXVbM9lDRx9mVjaqUvrfQD7Ms4xnlj0VPAeUPr27pty7ZJsgV4PoOTo5KkdTJOoN8L7EyyI8kZwF7g0JI2h4A3dcuvBz7p/Lkkra+RUy5VdTzJlcAdDF62eGNVPZjkGmCuqg4BfwB8MMlR4MsMQn8tnfK0zQZiXzamVvrSSj/Avow08qSoJGk6eHEuSWqEgS5JjZi6QE+yK8mRJEeTHJh0PUslOS/JnUkeSvJgkl/ttr8gyX9K8vnu+9nd9iR5d9ef+5NcNHRbb+rafz7Jm1a6z3Xo02lJPpXkY936jiT3dDX/UXeynCRndutHu/3bh27jqm77kSSvnVA/zkpyW5LPJXk4ySum8bgk+efd79YDSW5O8pxpOiZJbkxyrHv/yuK23o5Dkpcl+Wz3M+9OknXsx3Xd79f9Sf5DkrOG9i37eK+UaSsd01VV1dR8MTgp+yjwIuAM4DPABZOua0mN5wAXdcvPAx4BLgB+CzjQbT8AXNstXw58HAhwCXBPt/0FwGPd97O75bMn1Ke3A38IfKxbvxXY2y3fAPyTbvmfAjd0y3uBP+qWL+iO1ZnAju4YnjaBfvx74K3d8hnAWdN2XIBzgceBvzJ0LN48TccE+HHgIuCBoW29HQfgv3dt0/3sZevYj9cAW7rla4f6sezjzSqZttIxXbWm9fyD6uEBfAVwx9D6VcBVk65rRM1/wuA6OEeAc7pt5wBHuuX3AfuG2h/p9u8D3je0/Rnt1rH+bcAngJ8APtb9kXxp6Jf2u8eEwSuhXtEtb+naZelxGm63jv14PoMgzJLtU3VcGAT6k12QbemOyWun7ZgA25cEYS/Hodv3uaHtz2i31v1Ysu9ngA91y8s+3qyQaav9na32NW1TLou/zIvmu20bUvfv7YXAPcAPVdUXu11/CfxQt7xSnzZKX38X+HXgO936DwJfrarjy9T13Zq7/V/r2m+EvuwAFoB/100fvT/JDzBlx6WqngJ+G/gC8EUGj/F9TOcxGdbXcTi3W166fRLewuA/BDjxfqz2d7aiaQv0qZHkucAfA/+sqr4+vK8GT7kb/vWiSX4aOFZV9026lh5sYfDv8Xur6kLgGwz+tf+uaTgu3dzyHgZPUH8d+AFg10SL6tk0HIdRkrwDOA58aD3vd9oCfZzLEExcktMZhPmHquoj3eb/leScbv85wLFu+0p92gh9fSWwO8kTwC0Mpl1+Dzgrg0s8LK1rpUtAbIS+zAPzVXVPt34bg4CftuPyauDxqlqoqm8DH2FwnKbxmAzr6zg81S0v3b5ukrwZ+GngDd2TE5x4P55m5WO6omkL9HEuQzBR3Rn1PwAerqrfGdo1fHmENzGYW1/c/sbubP4lwNe6fz3vAF6T5OxuVPaabtu6qaqrqmpbVW1n8Fh/sqreANzJ4BIPy/VluUtAHAL2dq+42AHsZHDiat1U1V8CTyZ5SbfpJxlcAnrajssXgEuSfH/3u7bYj6k7Jkv0chy6fV9Pckn3+Lxx6LbWXAYfBvTrwO6q+ubQrpUe72UzrTtGKx3Tla3XSZAeT0JczuCVI48C75h0PcvU9yoG/y7eD3y6+7qcwZzYJ4DPA/8ZeEHXPgw+QORR4LPA7NBtvQU42n390oT7dSnfe5XLi7pfxqPAh4Ezu+3P6daPdvtfNPTz7+j6eIQ1etXBGH34MWCuOzYfZfDqiKk7LsC/AT4HPAB8kMErJ6bmmAA3M5j//zaD/5yu6PM4ALPdY/Mo8PssORG+xv04ymBOfPFv/4ZRjzcrZNpKx3S1L9/6L0mNmLYpF0nSCgx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1Ij/D3LFQA3mr0bpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def distributionOf(arr):\n",
    "    Freq = np.sum(arr, axis=0)\n",
    "    plt.bar(np.arange(arr.shape[1]), Freq)\n",
    "    plt.show()\n",
    "\n",
    "distributionOf(X_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35524, 12000)\n",
      "[[ 3.69545312 10.0336584   5.00140735 ...  3.62882899  1.9633871\n",
      "   1.55529765]\n",
      " [ 4.46275059  9.47017867 10.35649324 ...  1.92746959  1.61736184\n",
      "   3.70639905]\n",
      " [ 3.81786369 11.04322585 13.54993743 ...  2.82333739  1.30031815\n",
      "   3.25870772]\n",
      " ...\n",
      " [10.7989744  12.70249237 13.64979581 ...  0.12924805  0.30560761\n",
      "   0.07823928]\n",
      " [ 5.84249246  3.35302212  6.99121709 ...  0.23678803  0.12476213\n",
      "   0.14811056]\n",
      " [ 0.85414921  3.55850481  2.23368328 ...  0.34487924  0.45839801\n",
      "   0.18501066]]\n"
     ]
    }
   ],
   "source": [
    "print(X_set.shape)\n",
    "print(X_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_set = X_set[0:6000]\n",
    "# print(X_set.shape)\n",
    "# print(X_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = \"ROP\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA_path = os.path.join(HOST_path, \"IDB_drilling_monitoring/evaluation/\"+DATA)\n",
    "    \n",
    "# model = load_model(os.path.join(HOST_path, \"IDB_drilling_monitoring/outputs_\"+DATA+\"/best_model.hdf5\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35524, 12000)\n",
      "(35524,)\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "INPUT = X_set\n",
    "OUTPUT = eval(DATA)\n",
    "\n",
    "if OUTPUT.ndim == 1:\n",
    "    num_targets = 1\n",
    "else:\n",
    "    num_targets = OUTPUT.shape[1]\n",
    "\n",
    "print(INPUT.shape)\n",
    "print(OUTPUT.shape)\n",
    "print(num_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = pd.DataFrame(data=INPUT, columns=[f\"{i}Hz\" for i in range(1, INPUT.shape[1]+1)])\n",
    "# Y = OUTPUT\n",
    "\n",
    "# print(type(X), X.shape)\n",
    "# print(type(y), y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(X)\n",
    "# print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28419, 12000)\n",
      "(28419,)\n",
      "(7105, 12000)\n",
      "(7105,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(INPUT, OUTPUT, test_size=0.2, random_state=42, shuffle=True)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train2 = X_train.reshape(28419, 12000)\n",
    "# print(X_train2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_summary = shap.kmeans(X_train, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_accuracy(f):\n",
    "    print(\"Root mean squared test error = {}\".format(np.sqrt(np.mean((f(X_test) - y_test) ** 2))))\n",
    "    time.sleep(0.5)  # to let the print get out before any progress bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t=void 0;\"string\"==typeof this.props.plot_cmap?this.props.plot_cmap in je.colors?t=je.colors[this.props.plot_cmap]:(console.log(\"Invalid color map name, reverting to default.\"),t=je.colors.RdBu):Array.isArray(this.props.plot_cmap)&&(t=this.props.plot_cmap),this.colors=t.map((function(e){return q(e)})),this.brighterColors=[1.45,1.6].map((function(t,n){return e.colors[n].brighter(t)}));var n=ze(\",.4\");null!=this.props.ordering_keys&&null!=this.props.ordering_keys_time_format?(this.parseTime=Tr(this.props.ordering_keys_time_format),this.formatTime=Cr(this.props.ordering_keys_time_format),this.xtickFormat=function(e){return\"object\"==Za(e)?this.formatTime(e):n(e)}):(this.parseTime=null,this.formatTime=null,this.xtickFormat=n),this.xscale=De(),this.xaxis=dn().scale(this.xscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.xtickFormat(t)})).tickPadding(-18),this.ytickFormat=n,this.yscale=De(),this.yaxis=pn(an,undefined).scale(this.yscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.ytickFormat(e.invLinkFunction(t))})).tickPadding(2),this.xlabel.node().onchange=function(){return e.internalDraw()},this.ylabel.node().onchange=function(){return e.internalDraw()},this.svg.on(\"mousemove\",(function(t){return e.mouseMoved(t)})),this.svg.on(\"click\",(function(){return alert(\"This original index of the sample you clicked is \"+e.nearestExpIndex)})),this.svg.on(\"mouseout\",(function(t){return e.mouseOut(t)})),window.addEventListener(\"resize\",this.redraw),window.setTimeout(this.redraw,50)}},{key:\"componentDidUpdate\",value:function(){this.draw()}},{key:\"mouseOut\",value:function(){this.hoverLine.attr(\"display\",\"none\"),this.hoverx.attr(\"display\",\"none\"),this.hoverxOutline.attr(\"display\",\"none\"),this.hoverxTitle.attr(\"display\",\"none\"),this.hovery.attr(\"display\",\"none\"),this.hoveryOutline.attr(\"display\",\"none\"),this.hoverGroup1.attr(\"display\",\"none\"),this.hoverGroup2.attr(\"display\",\"none\")}},{key:\"mouseMoved\",value:function(e){var t,n,r=this;this.hoverLine.attr(\"display\",\"\"),this.hoverx.attr(\"display\",\"\"),this.hoverxOutline.attr(\"display\",\"\"),this.hoverxTitle.attr(\"display\",\"\"),this.hovery.attr(\"display\",\"\"),this.hoveryOutline.attr(\"display\",\"\"),this.hoverGroup1.attr(\"display\",\"\"),this.hoverGroup2.attr(\"display\",\"\");var a=function(e,t){if(e=function(e){let t;for(;t=e.sourceEvent;)e=t;return e}(e),void 0===t&&(t=e.currentTarget),t){var n=t.ownerSVGElement||t;if(n.createSVGPoint){var r=n.createSVGPoint();return r.x=e.clientX,r.y=e.clientY,[(r=r.matrixTransform(t.getScreenCTM().inverse())).x,r.y]}if(t.getBoundingClientRect){var a=t.getBoundingClientRect();return[e.clientX-a.left-t.clientLeft,e.clientY-a.top-t.clientTop]}}return[e.pageX,e.pageY]}(e,this.svg.node())[0];if(this.props.explanations){for(t=0;t<this.currExplanations.length;++t)(!n||Math.abs(n.xmapScaled-a)>Math.abs(this.currExplanations[t].xmapScaled-a))&&(n=this.currExplanations[t]);this.nearestExpIndex=n.origInd,this.hoverLine.attr(\"x1\",n.xmapScaled).attr(\"x2\",n.xmapScaled).attr(\"y1\",0+this.topOffset).attr(\"y2\",this.height),this.hoverx.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxOutline.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxTitle.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-18).text(n.count>1?n.count+\" averaged samples\":\"\"),this.hovery.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint))),this.hoveryOutline.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint)));for(var i,o,u=[],l=this.currPosOrderedFeatures.length-1;l>=0;--l){var s=this.currPosOrderedFeatures[l],c=n.features[s];o=5+(c.posyTop+c.posyBottom)/2,(!i||o-i>=15)&&c.posyTop-c.posyBottom>=6&&(u.push(c),i=o)}var f=[];i=void 0;var p,d=Ga(this.currNegOrderedFeatures);try{for(d.s();!(p=d.n()).done;){var h=p.value,v=n.features[h];o=5+(v.negyTop+v.negyBottom)/2,(!i||i-o>=15)&&v.negyTop-v.negyBottom>=6&&(f.push(v),i=o)}}catch(e){d.e(e)}finally{d.f()}var g=function(e){var t=\"\";return null!==e.value&&void 0!==e.value&&(t=\" = \"+(isNaN(e.value)?e.value:r.ytickFormat(e.value))),n.count>1?\"mean(\"+r.props.featureNames[e.ind]+\")\"+t:r.props.featureNames[e.ind]+t},y=this.hoverGroup1.selectAll(\".pos-values\").data(u);y.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(y).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),y.exit().remove();var m=this.hoverGroup2.selectAll(\".pos-values\").data(u);m.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(m).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[0]).text(g),m.exit().remove();var b=this.hoverGroup1.selectAll(\".neg-values\").data(f);b.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(b).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),b.exit().remove();var _=this.hoverGroup2.selectAll(\".neg-values\").data(f);_.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(_).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[1]).text(g),_.exit().remove()}}},{key:\"draw\",value:function(){var e=this;if(this.props.explanations&&0!==this.props.explanations.length){(0,Re.each)(this.props.explanations,(function(e,t){return e.origInd=t}));var t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var u=a.value;i.features.hasOwnProperty(u)||(i.features[u]={effect:0,value:0}),i.features[u].ind=u}}catch(e){o.e(e)}finally{o.f()}}}catch(e){r.e(e)}finally{r.f()}var l=this.xlabel.node().value,s=\"sample order by key\"===l&&null!=this.props.ordering_keys_time_format;if(this.xscale=s?Ya():De(),this.xaxis.scale(this.xscale),\"sample order by similarity\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.simIndex})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by output value\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return-e.outValue})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"original sample ordering\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.origInd})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by key\"===l)t=this.props.explanations,s?(0,Re.each)(t,(function(e,t){return e.xmap=n.parseTime(n.props.ordering_keys[t])})):(0,Re.each)(t,(function(e,t){return e.xmap=n.props.ordering_keys[t]})),t=(0,Re.sortBy)(t,(function(e){return e.xmap}));else{var c=(0,Re.findKey)(this.props.featureNames,(function(e){return e===l}));(0,Re.each)(this.props.explanations,(function(e,t){return e.xmap=e.features[c].value}));var f=(0,Re.sortBy)(this.props.explanations,(function(e){return e.xmap})),p=(0,Re.map)(f,(function(e){return e.xmap}));if(\"string\"==typeof p[0])return void alert(\"Ordering by category names is not yet supported.\");var d,h,v=(0,Re.min)(p),g=((0,Re.max)(p)-v)/100;t=[];for(var y=0;y<f.length;++y){var m=f[y];if(d&&!h&&m.xmap-d.xmap<=g||h&&m.xmap-h.xmap<=g){h||((h=(0,Re.cloneDeep)(d)).count=1);var b,_=Ga(this.usedFeatures);try{for(_.s();!(b=_.n()).done;){var w=b.value;h.features[w].effect+=m.features[w].effect,h.features[w].value+=m.features[w].value}}catch(e){_.e(e)}finally{_.f()}h.count+=1}else if(d)if(h){var x,k=Ga(this.usedFeatures);try{for(k.s();!(x=k.n()).done;){var S=x.value;h.features[S].effect/=h.count,h.features[S].value/=h.count}}catch(e){k.e(e)}finally{k.f()}t.push(h),h=void 0}else t.push(d);d=m}d.xmap-t[t.length-1].xmap>g&&t.push(d)}this.currUsedFeatures=this.usedFeatures,this.currPosOrderedFeatures=this.posOrderedFeatures,this.currNegOrderedFeatures=this.negOrderedFeatures;var E=this.ylabel.node().value;if(\"model output value\"!==E){var C=t;t=(0,Re.cloneDeep)(t);for(var T=(0,Re.findKey)(this.props.featureNames,(function(e){return e===E})),M=0;M<t.length;++M){var N=t[M].features[T];t[M].features={},t[M].features[T]=N,C[M].remapped_version=t[M]}this.currUsedFeatures=[T],this.currPosOrderedFeatures=[T],this.currNegOrderedFeatures=[T]}this.currExplanations=t,\"identity\"===this.props.link?this.invLinkFunction=function(e){return n.props.baseValue+e}:\"logit\"===this.props.link?this.invLinkFunction=function(e){return 1/(1+Math.exp(-(n.props.baseValue+e)))}:console.log(\"ERROR: Unrecognized link function: 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e.effect<0})),(function(e){return-e.effect})))||0;A=Math.max(A,2.2*Math.max(R,j))}this.yscale.domain([-A/2,A/2]).range([this.height-10,this.topOffset]),this.yaxisElement.attr(\"transform\",\"translate(\"+this.leftOffset+\",0)\").call(this.yaxis);for(var U=0;U<O;++U){var I,$=t[U].features,B=-((0,Re.sum)((0,Re.map)((0,Re.filter)($,(function(e){return e.effect<0})),(function(e){return-e.effect})))||0),W=void 0,V=Ga(this.currPosOrderedFeatures);try{for(V.s();!(I=V.n()).done;)$[W=I.value].posyTop=this.yscale(B),$[W].effect>0&&(B+=$[W].effect),$[W].posyBottom=this.yscale(B),$[W].ind=W}catch(e){V.e(e)}finally{V.f()}var H,q=B,Q=Ga(this.currNegOrderedFeatures);try{for(Q.s();!(H=Q.n()).done;)$[W=H.value].negyTop=this.yscale(B),$[W].effect<0&&(B-=$[W].effect),$[W].negyBottom=this.yscale(B)}catch(e){Q.e(e)}finally{Q.f()}t[U].joinPoint=q,t[U].joinPointy=this.yscale(q)}var Y=En().x((function(e){return e[0]})).y((function(e){return e[1]})),G=this.mainGroup.selectAll(\".force-bar-array-area-pos\").data(this.currUsedFeatures);G.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-pos\").merge(G).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[0]),G.exit().remove();var K=this.mainGroup.selectAll(\".force-bar-array-area-neg\").data(this.currUsedFeatures);K.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-neg\").merge(K).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[1]),K.exit().remove();var Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var pe=t[le].xmapScaled;for(ue=le;pe+100>t[ue].xmapScaled&&ue<O-1;)++ue;le=ue}}}catch(e){ie.e(e)}finally{ie.f()}}var de=this.onTopGroup.selectAll(\".force-bar-array-flabels\").data(ee);de.enter().append(\"text\").attr(\"class\",\"force-bar-array-flabels\").merge(de).attr(\"x\",(function(e){return e[0]})).attr(\"y\",(function(e){return e[1]+4})).text((function(e){return e[2]})),de.exit().remove()}},{key:\"componentWillUnmount\",value:function(){window.removeEventListener(\"resize\",this.redraw)}},{key:\"render\",value:function(){var t=this;return e.createElement(\"div\",{ref:function(e){return t.wrapper=Jt(e)},style:{textAlign:\"center\"}},e.createElement(\"style\",{dangerouslySetInnerHTML:{__html:\"\\n          .force-bar-array-wrapper {\\n            text-align: center;\\n          }\\n          .force-bar-array-xaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-xaxis .domain {\\n            opacity: 0;\\n          }\\n          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       "</script>"
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test R^2 Score  :  0.8072292056132844\n",
      "Train R^2 Score :  0.9414130568175361\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_regr = LinearRegression()\n",
    "lin_regr.fit(X_train, Y_train)\n",
    "\n",
    "print(\"Test R^2 Score  : \", lin_regr.score(X_test, Y_test))\n",
    "print(\"Train R^2 Score : \", lin_regr.score(X_train, Y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1Hz', '2Hz', '3Hz', '4Hz', '5Hz', '6Hz', '7Hz', '8Hz', '9Hz', '10Hz']\n",
      "['11991Hz', '11992Hz', '11993Hz', '11994Hz', '11995Hz', '11996Hz', '11997Hz', '11998Hz', '11999Hz', '12000Hz']\n"
     ]
    }
   ],
   "source": [
    "feature_names = [f\"{i}Hz\" for i in range(1, INPUT.shape[1]+1)]\n",
    "print(feature_names[:10])\n",
    "print(feature_names[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex = shap.KernelExplainer(lin_regr.predict, X_train_summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Signle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "shap_values = ex.shap_values(X_test[0, :])\n",
    "print(shap_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
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       "    document.getElementById('iGTB8R4O8N6WBINFRXTDP')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x7f68006f3130>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(ex.expected_value, shap_values, X_test[0, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Multi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_values = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_value(x):\n",
    "    SHAP_value = ex.shap_values(X_test[x, :])\n",
    "    print(SHAP_value.shape, SHAP_value)\n",
    "    return SHAP_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 1/100 [06:48<11:13:33, 408.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12000,) [0. 0. 0. ... 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(100)):\n",
    "    shap_values.append(plot_value(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_values = np.array(shap_values)\n",
    "print(shap_values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(os.path.join(HOST_path, \"IDB_drilling_monitoring/shap_values.npy\"), shap_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap.summary_plot(shap_values, X_test[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap.summary_plot(shap_values, X_test[0:10], plot_type=\"bar\", sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap.force_plot(ex.expected_value, shap_values[0], X_test[0, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap.force_plot(ex.expected_value, shap_values, X_test[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### CNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex = shap.KernelExplainer(model.predict, X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test2 = X_test.reshape(7105, 12000)\n",
    "print(X_test2.shape)\n",
    "print(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_values = ex.shap_values(X_test2[0, :])\n",
    "print(shap_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap.summary_plot(shap_values[0], X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap.summary_plot(shap_values[0], X_test, plot_type=\"bar\", sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap.decision_plot(expected_value[0], shap_values[0], X_test, ignore_warnings=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shaps = shap_values[0]\n",
    "shaps_mean = np.abs(shaps).mean(axis=0)\n",
    "feature_names = df.drop('type',axis=1).columns\n",
    "\n",
    "sorted_indices = np.argsort(shaps_mean)[::-1]\n",
    "sorted_features = feature_names[sorted_indices] \n",
    "sorted_shaps_mean = shaps_mean[sorted_indices]\n",
    "max_display = 20  \n",
    "selected_features = sorted_features[:max_display]\n",
    "selected_shaps_mean = sorted_shaps_mean[:max_display]\n",
    "fig, ax = plt.subplots()\n",
    "ax.barh(np.arange(max_display), selected_shaps_mean, align='center', color='b')\n",
    "ax.set_yticks(np.arange(max_display))\n",
    "ax.set_yticklabels(selected_features)\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel('Mean(|SHAP value|)(average impact on model output magnitude)')\n",
    "# ax.set_xlabel('Mean Absolute SHAP Value')\n",
    "# ax.set_title('Summary Plot of SHAP Values')\n",
    "plt.savefig(os.path.join(HOST_path, \"IDB_drilling_signal/shap_0_20.png\"), dpi=300)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.xlim(0,4096)\n",
    "plt.ylim(0,0.017)\n",
    "ax = plt.gca()\n",
    "# ax.axes.xaxis.set_visible(False)\n",
    "# ax.axes.yaxis.set_visible(False)\n",
    "plt.plot(shaps_mean)\n",
    "# plt.savefig(os.path.join(HOST_path, \"IDB_drilling_signal/shap_0_all.png\"), dpi=300)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.xlim(0,4096)\n",
    "plt.ylim(0,0.017)\n",
    "ax = plt.gca()\n",
    "ax.axes.xaxis.set_visible(False)\n",
    "# ax.axes.yaxis.set_visible(False)\n",
    "plt.plot(shaps_mean)\n",
    "plt.savefig(os.path.join(HOST_path, \"IDB_drilling_signal/shap_0_all.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rforest = RandomForestRegressor(n_estimators=100, max_depth=None, min_samples_split=2, random_state=42)\n",
    "rforest.fit(X_train, Y_train)\n",
    "\n",
    "print(\"Test R^2 Score  : \", lin_regr.score(X_test, Y_test))\n",
    "print(\"Train R^2 Score : \", lin_regr.score(X_train, Y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# explain all the predictions in the test set\n",
    "explainer = shap.TreeExplainer(rforest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_values = explainer.shap_values(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap.summary_plot(shap_values, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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