notebooks/demo.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook Demo for A2H Recommendation System\n",
    "\n",
    "This is a simple demonstration of how our proposed A2H recommendation system works. A web-based version of the system is currently under development. Please stay tuned for updates!\n",
    "\n",
    "## Use case scenario\n",
    "\n",
    "The proposed recommendation system aims to allow drug development scientists to find high-quality preclinical trials that are more likely to translate outcomes to the desired clinical trial experiments. This way, the scientists can optimize preclinical trials, which would be much cheaper compared to human trials, instead of prematurely entering and failing clinical trials. The proposed recommendation system would work as follows. A user would provide partial or whole clinical trial experimental settings or interest. The recommendation system will then generate the recommended preclinical experiment settings, ranked based on the translation success probability predicted by our trained ML model, along with explanations for the predictions. For each recommended preclinical trial setting, the system would also provide the range of differences between preclinical and clinical outcomes, defined by different thresholds for δ. For example, if the threshold is 10%, then the preclinical and clinical outcomes differ by no more than 10%. Lastly, the user scientist would perform the recommended preclinical trial and collect the preclinical outcome. If the preclinical outcome is 50%, the predicted clinical outcome will be 40-60% given the 10% difference range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries.\n",
    "import os\n",
    "import sys\n",
    "import pickle\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from src.utils import load_splits, load_best_pipeline, get_simple_pipeline\n",
    "from src.utils import constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/share/fzli/.conda/envs/a2h/lib/python3.11/site-packages/imblearn/over_sampling/_smote/base.py:345: FutureWarning: The parameter `n_jobs` has been deprecated in 0.10 and will be removed in 0.12. You can pass an nearest neighbors estimator where `n_jobs` is already set instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model trained successfully.\n"
     ]
    }
   ],
   "source": [
    "# Load and train the production model.\n",
    "path_data_dir = \"../outputs/data_processing/splits/cls_delta-0.5_rs-42\"\n",
    "path_cls_dir = \"../outputs/msap/cls_delta-0.5_rs-42\"\n",
    "\n",
    "data_train, data_test, _ = load_splits(path_data_dir)\n",
    "model = load_best_pipeline(path_cls_dir)\n",
    "with open(f\"{path_cls_dir}/classifiers/sfs.pkl\", 'rb') as f:\n",
    "    sfs = pickle.load(f)\n",
    "\n",
    "data = pd.concat([data_train, data_test], axis=0)\n",
    "inputs = data_train.drop('result translation', axis=1)\n",
    "labels = data_train['result translation']\n",
    "\n",
    "prep_pipeline = get_simple_pipeline(inputs, constants.FEATURES_CAT)\n",
    "prep_pipeline.fit(inputs)\n",
    "inputs = prep_pipeline.transform(inputs)\n",
    "inputs = inputs[list(sfs.k_feature_names_)]\n",
    "\n",
    "model.fit(inputs, labels)\n",
    "model = model.named_steps['cls']\n",
    "\n",
    "print(\"Model trained successfully.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommendation\n",
    "\n",
    "The following example shows the scenario where a user provides the following clinical trial settings:\n",
    "- Endpoint: Acute\n",
    "- Dosage times per day: 2\n",
    "- Age group: Not child"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create candidate inputs for the model.\n",
    "from itertools import product\n",
    "\n",
    "feature_value_ranges = {\n",
    "    'ohe_0__acute/sustained preclinical_sustained': [0, 1],\n",
    "    'ohe_1__gnotobiotic preclinical_spf': [0, 1],\n",
    "    'ohe_2__animal strain preclinical_c57bl/6j': [0, 1],\n",
    "    'scaler_4__animal age value (days) preclinical': [5, 10, 20, 40],\n",
    "    'scaler_5__animal weight value (g) preclinical': [70, 140, 280, 560],\n",
    "    'ohe_7__disease model preclinical_kanamycin,gentamicin,colistin,metronidazole,vancomycin+clindamycin+challenge': [0, 1],\n",
    "    'ohe_8__prophylactic / therapeutic preclinical_t': [0, 1],\n",
    "    'ohe_9__ribotype preclinical_1.0': [0, 1],\n",
    "    'scaler_11__c diff dose value preclinical': [100, 1000, 10000, 100000, 1000000],\n",
    "    'scaler_14__daily dosage preclinical': [None],\n",
    "    'scaler_16__dosage value preclinical': [5, 10, 20, 40, 80, 160],\n",
    "    'scaler_17__dosage times per day preclinical': [1, 2],\n",
    "    'scaler_18__dosage duration preclinical': [1, 3, 5, 7],\n",
    "    'ohe_19__acute/sustained clinical_sustained': [0],\n",
    "    'scaler_23__dosage times per day clinical': [2],\n",
    "    'ohe_26__Age Groups clinical_child': [0],\n",
    "}\n",
    "\n",
    "candidates = pd.DataFrame(\n",
    "    list(product(*feature_value_ranges.values())),\n",
    "    columns=feature_value_ranges.keys(),\n",
    ")\n",
    "candidates['scaler_14__daily dosage preclinical'] = candidates['scaler_16__dosage value preclinical'] * candidates['scaler_17__dosage times per day preclinical']\n",
    "candidates['translation_success_prob'] = model.predict_proba(candidates)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "      <td>100000</td>\n",
       "      <td>80</td>\n",
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       "      <td>560</td>\n",
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      "text/plain": [
       "       ohe_0__acute/sustained preclinical_sustained  \\\n",
       "37138                                             0   \n",
       "48658                                             0   \n",
       "42898                                             0   \n",
       "50578                                             0   \n",
       "52498                                             0   \n",
       "\n",
       "       ohe_1__gnotobiotic preclinical_spf  \\\n",
       "37138                                   0   \n",
       "48658                                   0   \n",
       "42898                                   0   \n",
       "50578                                   0   \n",
       "52498                                   0   \n",
       "\n",
       "       ohe_2__animal strain preclinical_c57bl/6j  \\\n",
       "37138                                          1   \n",
       "48658                                          1   \n",
       "42898                                          1   \n",
       "50578                                          1   \n",
       "52498                                          1   \n",
       "\n",
       "       scaler_4__animal age value (days) preclinical  \\\n",
       "37138                                              5   \n",
       "48658                                             20   \n",
       "42898                                             10   \n",
       "50578                                             20   \n",
       "52498                                             20   \n",
       "\n",
       "       scaler_5__animal weight value (g) preclinical  \\\n",
       "37138                                            560   \n",
       "48658                                            140   \n",
       "42898                                            280   \n",
       "50578                                            280   \n",
       "52498                                            560   \n",
       "\n",
       "       ohe_7__disease model preclinical_kanamycin,gentamicin,colistin,metronidazole,vancomycin+clindamycin+challenge  \\\n",
       "37138                                                  0                                                               \n",
       "48658                                                  0                                                               \n",
       "42898                                                  0                                                               \n",
       "50578                                                  0                                                               \n",
       "52498                                                  0                                                               \n",
       "\n",
       "       ohe_8__prophylactic / therapeutic preclinical_t  \\\n",
       "37138                                                1   \n",
       "48658                                                1   \n",
       "42898                                                1   \n",
       "50578                                                1   \n",
       "52498                                                1   \n",
       "\n",
       "       ohe_9__ribotype preclinical_1.0  \\\n",
       "37138                                0   \n",
       "48658                                0   \n",
       "42898                                0   \n",
       "50578                                0   \n",
       "52498                                0   \n",
       "\n",
       "       scaler_11__c diff dose value preclinical  \\\n",
       "37138                                    100000   \n",
       "48658                                    100000   \n",
       "42898                                    100000   \n",
       "50578                                    100000   \n",
       "52498                                    100000   \n",
       "\n",
       "       scaler_14__daily dosage preclinical  \\\n",
       "37138                                   80   \n",
       "48658                                   80   \n",
       "42898                                   80   \n",
       "50578                                   80   \n",
       "52498                                   80   \n",
       "\n",
       "       scaler_16__dosage value preclinical  \\\n",
       "37138                                   80   \n",
       "48658                                   80   \n",
       "42898                                   80   \n",
       "50578                                   80   \n",
       "52498                                   80   \n",
       "\n",
       "       scaler_17__dosage times per day preclinical  \\\n",
       "37138                                            1   \n",
       "48658                                            1   \n",
       "42898                                            1   \n",
       "50578                                            1   \n",
       "52498                                            1   \n",
       "\n",
       "       scaler_18__dosage duration preclinical  \\\n",
       "37138                                       5   \n",
       "48658                                       5   \n",
       "42898                                       5   \n",
       "50578                                       5   \n",
       "52498                                       5   \n",
       "\n",
       "       ohe_19__acute/sustained clinical_sustained  \\\n",
       "37138                                           0   \n",
       "48658                                           0   \n",
       "42898                                           0   \n",
       "50578                                           0   \n",
       "52498                                           0   \n",
       "\n",
       "       scaler_23__dosage times per day clinical  \\\n",
       "37138                                         2   \n",
       "48658                                         2   \n",
       "42898                                         2   \n",
       "50578                                         2   \n",
       "52498                                         2   \n",
       "\n",
       "       ohe_26__Age Groups clinical_child  translation_success_prob  \n",
       "37138                                  0                  0.923795  \n",
       "48658                                  0                  0.923795  \n",
       "42898                                  0                  0.923795  \n",
       "50578                                  0                  0.923795  \n",
       "52498                                  0                  0.923795  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# According to the model, the top 5 candidates are:\n",
    "candidates = candidates.sort_values('translation_success_prob', ascending=False)\n",
    "candidates.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
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e=null!==i?i.baseLanes|n:n,t.lanes=t.childLanes=1073741824,t.memoizedState={baseLanes:e,cachePool:null,transitions:null},t.updateQueue=null,Ca(Al,Ol),Ol|=e,null;t.memoizedState={baseLanes:0,cachePool:null,transitions:null},r=null!==i?i.baseLanes:n,Ca(Al,Ol),Ol|=r}else null!==i?(r=i.baseLanes|n,t.memoizedState=null):r=n,Ca(Al,Ol),Ol|=r;return wu(e,t,a,n),t.child}function Cu(e,t){var n=t.ref;(null===e&&null!==n||null!==e&&e.ref!==n)&&(t.flags|=512,t.flags|=2097152)}function Tu(e,t,n,r,a){var i=La(n)?Pa:Ma.current;return i=za(t,i),Si(t,a),n=So(e,t,n,r,i,a),r=Eo(),null===e||_u?(ai&&r&&ei(t),t.flags|=1,wu(e,t,n,a),t.child):(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~a,Hu(e,t,a))}function Mu(e,t,n,r,a){if(La(n)){var i=!0;Da(t)}else i=!1;if(Si(t,a),null===t.stateNode)Vu(e,t),Wi(t,n,r),Hi(t,n,r,a),r=!0;else if(null===e){var o=t.stateNode,u=t.memoizedProps;o.props=u;var l=o.context,s=n.contextType;s=\"object\"==typeof s&&null!==s?Ei(s):za(t,s=La(n)?Pa:Ma.current);var c=n.getDerivedStateFromProps,f=\"function\"==typeof c||\"function\"==typeof o.getSnapshotBeforeUpdate;f||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==r||l!==s)&&Vi(t,o,r,s),Pi=!1;var p=t.memoizedState;o.state=p,Ri(t,r,o,a),l=t.memoizedState,u!==r||p!==l||Na.current||Pi?(\"function\"==typeof c&&(Ii(t,n,c,r),l=t.memoizedState),(u=Pi||Bi(t,n,u,r,p,l,s))?(f||\"function\"!=typeof o.UNSAFE_componentWillMount&&\"function\"!=typeof o.componentWillMount||(\"function\"==typeof o.componentWillMount&&o.componentWillMount(),\"function\"==typeof o.UNSAFE_componentWillMount&&o.UNSAFE_componentWillMount()),\"function\"==typeof o.componentDidMount&&(t.flags|=4194308)):(\"function\"==typeof o.componentDidMount&&(t.flags|=4194308),t.memoizedProps=r,t.memoizedState=l),o.props=r,o.state=l,o.context=s,r=u):(\"function\"==typeof o.componentDidMount&&(t.flags|=4194308),r=!1)}else{o=t.stateNode,Li(e,t),u=t.memoizedProps,s=t.type===t.elementType?u:gi(t.type,u),o.props=s,f=t.pendingProps,p=o.context,l=\"object\"==typeof(l=n.contextType)&&null!==l?Ei(l):za(t,l=La(n)?Pa:Ma.current);var d=n.getDerivedStateFromProps;(c=\"function\"==typeof d||\"function\"==typeof o.getSnapshotBeforeUpdate)||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==f||p!==l)&&Vi(t,o,r,l),Pi=!1,p=t.memoizedState,o.state=p,Ri(t,r,o,a);var h=t.memoizedState;u!==f||p!==h||Na.current||Pi?(\"function\"==typeof d&&(Ii(t,n,d,r),h=t.memoizedState),(s=Pi||Bi(t,n,s,r,p,h,l)||!1)?(c||\"function\"!=typeof o.UNSAFE_componentWillUpdate&&\"function\"!=typeof o.componentWillUpdate||(\"function\"==typeof o.componentWillUpdate&&o.componentWillUpdate(r,h,l),\"function\"==typeof o.UNSAFE_componentWillUpdate&&o.UNSAFE_componentWillUpdate(r,h,l)),\"function\"==typeof o.componentDidUpdate&&(t.flags|=4),\"function\"==typeof o.getSnapshotBeforeUpdate&&(t.flags|=1024)):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=h),o.props=r,o.state=h,o.context=l,r=s):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),r=!1)}return Nu(e,t,n,r,i,a)}function Nu(e,t,n,r,a,i){Cu(e,t);var o=0!=(128&t.flags);if(!r&&!o)return a&&Ra(t,n,!1),Hu(e,t,i);r=t.stateNode,bu.current=t;var u=o&&\"function\"!=typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&o?(t.child=Ki(t,e.child,null,i),t.child=Ki(t,null,u,i)):wu(e,t,u,i),t.memoizedState=r.state,a&&Ra(t,n,!0),t.child}function Pu(e){var t=e.stateNode;t.pendingContext?Aa(0,t.pendingContext,t.pendingContext!==t.context):t.context&&Aa(0,t.context,!1),ro(e,t.containerInfo)}function zu(e,t,n,r,a){return di(),hi(a),t.flags|=256,wu(e,t,n,r),t.child}var Lu,Ou,Au,Fu,Du={dehydrated:null,treeContext:null,retryLane:0};function Ru(e){return{baseLanes:e,cachePool:null,transitions:null}}function ju(e,t,n){var r,a=t.pendingProps,o=uo.current,u=!1,l=0!=(128&t.flags);if((r=l)||(r=(null===e||null!==e.memoizedState)&&0!=(2&o)),r?(u=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(o|=1),Ca(uo,1&o),null===e)return si(t),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated)?(0==(1&t.mode)?t.lanes=1:\"$!\"===e.data?t.lanes=8:t.lanes=1073741824,null):(l=a.children,e=a.fallback,u?(a=t.mode,u=t.child,l={mode:\"hidden\",children:l},0==(1&a)&&null!==u?(u.childLanes=0,u.pendingProps=l):u=Rs(l,a,0,null),e=Ds(e,a,n,null),u.return=t,e.return=t,u.sibling=e,t.child=u,t.child.memoizedState=Ru(n),t.memoizedState=Du,e):Uu(t,l));if(null!==(o=e.memoizedState)&&null!==(r=o.dehydrated))return function(e,t,n,r,a,o,u){if(n)return 256&t.flags?(t.flags&=-257,Iu(e,t,u,r=fu(Error(i(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(o=r.fallback,a=t.mode,r=Rs({mode:\"visible\",children:r.children},a,0,null),(o=Ds(o,a,u,null)).flags|=2,r.return=t,o.return=t,r.sibling=o,t.child=r,0!=(1&t.mode)&&Ki(t,e.child,null,u),t.child.memoizedState=Ru(u),t.memoizedState=Du,o);if(0==(1&t.mode))return Iu(e,t,u,null);if(\"$!\"===a.data){if(r=a.nextSibling&&a.nextSibling.dataset)var l=r.dgst;return r=l,Iu(e,t,u,r=fu(o=Error(i(419)),r,void 0))}if(l=0!=(u&e.childLanes),_u||l){if(null!==(r=Pl)){switch(u&-u){case 4:a=2;break;case 16:a=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:a=32;break;case 536870912:a=268435456;break;default:a=0}0!==(a=0!=(a&(r.suspendedLanes|u))?0:a)&&a!==o.retryLane&&(o.retryLane=a,Ni(e,a),rs(r,e,a,-1))}return gs(),Iu(e,t,u,r=fu(Error(i(421))))}return\"$?\"===a.data?(t.flags|=128,t.child=e.child,t=Ms.bind(null,e),a._reactRetry=t,null):(e=o.treeContext,ri=sa(a.nextSibling),ni=t,ai=!0,ii=null,null!==e&&(Qa[Ya++]=Ka,Qa[Ya++]=Za,Qa[Ya++]=Ga,Ka=e.id,Za=e.overflow,Ga=t),(t=Uu(t,r.children)).flags|=4096,t)}(e,t,l,a,r,o,n);if(u){u=a.fallback,l=t.mode,r=(o=e.child).sibling;var s={mode:\"hidden\",children:a.children};return 0==(1&l)&&t.child!==o?((a=t.child).childLanes=0,a.pendingProps=s,t.deletions=null):(a=As(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=As(r,u):(u=Ds(u,l,n,null)).flags|=2,u.return=t,a.return=t,a.sibling=u,t.child=a,a=u,u=t.child,l=null===(l=e.child.memoizedState)?Ru(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},u.memoizedState=l,u.childLanes=e.childLanes&~n,t.memoizedState=Du,a}return e=(u=e.child).sibling,a=As(u,{mode:\"visible\",children:a.children}),0==(1&t.mode)&&(a.lanes=n),a.return=t,a.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=a,t.memoizedState=null,a}function Uu(e,t){return(t=Rs({mode:\"visible\",children:t},e.mode,0,null)).return=e,e.child=t}function Iu(e,t,n,r){return null!==r&&hi(r),Ki(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function $u(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ki(e.return,t,n)}function Bu(e,t,n,r,a){var i=e.memoizedState;null===i?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=a)}function Wu(e,t,n){var r=t.pendingProps,a=r.revealOrder,i=r.tail;if(wu(e,t,r.children,n),0!=(2&(r=uo.current)))r=1&r|2,t.flags|=128;else{if(null!==e&&0!=(128&e.flags))e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&$u(e,n,t);else if(19===e.tag)$u(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(Ca(uo,r),0==(1&t.mode))t.memoizedState=null;else switch(a){case\"forwards\":for(n=t.child,a=null;null!==n;)null!==(e=n.alternate)&&null===lo(e)&&(a=n),n=n.sibling;null===(n=a)?(a=t.child,t.child=null):(a=n.sibling,n.sibling=null),Bu(t,!1,a,n,i);break;case\"backwards\":for(n=null,a=t.child,t.child=null;null!==a;){if(null!==(e=a.alternate)&&null===lo(e)){t.child=a;break}e=a.sibling,a.sibling=n,n=a,a=e}Bu(t,!0,n,null,i);break;case\"together\":Bu(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Vu(e,t){0==(1&t.mode)&&null!==e&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Hu(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),Rl|=t.lanes,0==(n&t.childLanes))return null;if(null!==e&&t.child!==e.child)throw Error(i(153));if(null!==t.child){for(n=As(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=As(e,e.pendingProps)).return=t;n.sibling=null}return t.child}function qu(e,t){if(!ai)switch(e.tailMode){case\"hidden\":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case\"collapsed\":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function Qu(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=14680064&a.subtreeFlags,r|=14680064&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Yu(e,t,n){var r=t.pendingProps;switch(ti(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Qu(t),null;case 1:case 17:return La(t.type)&&Oa(),Qu(t),null;case 3:return r=t.stateNode,ao(),Ea(Na),Ea(Ma),co(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),null!==e&&null!==e.child||(fi(t)?t.flags|=4:null===e||e.memoizedState.isDehydrated&&0==(256&t.flags)||(t.flags|=1024,null!==ii&&(us(ii),ii=null))),Ou(e,t),Qu(t),null;case 5:oo(t);var a=no(to.current);if(n=t.type,null!==e&&null!=t.stateNode)Au(e,t,n,r,a),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(null===t.stateNode)throw Error(i(166));return Qu(t),null}if(e=no(Ji.current),fi(t)){r=t.stateNode,n=t.type;var o=t.memoizedProps;switch(r[pa]=t,r[da]=o,e=0!=(1&t.mode),n){case\"dialog\":Ur(\"cancel\",r),Ur(\"close\",r);break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",r);break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],r);break;case\"source\":Ur(\"error\",r);break;case\"img\":case\"image\":case\"link\":Ur(\"error\",r),Ur(\"load\",r);break;case\"details\":Ur(\"toggle\",r);break;case\"input\":K(r,o),Ur(\"invalid\",r);break;case\"select\":r._wrapperState={wasMultiple:!!o.multiple},Ur(\"invalid\",r);break;case\"textarea\":ae(r,o),Ur(\"invalid\",r)}for(var l in me(n,o),a=null,o)if(o.hasOwnProperty(l)){var s=o[l];\"children\"===l?\"string\"==typeof s?r.textContent!==s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",s]):\"number\"==typeof s&&r.textContent!==\"\"+s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",\"\"+s]):u.hasOwnProperty(l)&&null!=s&&\"onScroll\"===l&&Ur(\"scroll\",r)}switch(n){case\"input\":q(r),J(r,o,!0);break;case\"textarea\":q(r),oe(r);break;case\"select\":case\"option\":break;default:\"function\"==typeof o.onClick&&(r.onclick=Jr)}r=a,t.updateQueue=r,null!==r&&(t.flags|=4)}else{l=9===a.nodeType?a:a.ownerDocument,\"http://www.w3.org/1999/xhtml\"===e&&(e=ue(n)),\"http://www.w3.org/1999/xhtml\"===e?\"script\"===n?((e=l.createElement(\"div\")).innerHTML=\"<script><\\/script>\",e=e.removeChild(e.firstChild)):\"string\"==typeof r.is?e=l.createElement(n,{is:r.is}):(e=l.createElement(n),\"select\"===n&&(l=e,r.multiple?l.multiple=!0:r.size&&(l.size=r.size))):e=l.createElementNS(e,n),e[pa]=t,e[da]=r,Lu(e,t,!1,!1),t.stateNode=e;e:{switch(l=be(n,r),n){case\"dialog\":Ur(\"cancel\",e),Ur(\"close\",e),a=r;break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",e),a=r;break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],e);a=r;break;case\"source\":Ur(\"error\",e),a=r;break;case\"img\":case\"image\":case\"link\":Ur(\"error\",e),Ur(\"load\",e),a=r;break;case\"details\":Ur(\"toggle\",e),a=r;break;case\"input\":K(e,r),a=G(e,r),Ur(\"invalid\",e);break;case\"option\":default:a=r;break;case\"select\":e._wrapperState={wasMultiple:!!r.multiple},a=R({},r,{value:void 0}),Ur(\"invalid\",e);break;case\"textarea\":ae(e,r),a=re(e,r),Ur(\"invalid\",e)}for(o in me(n,a),s=a)if(s.hasOwnProperty(o)){var c=s[o];\"style\"===o?ge(e,c):\"dangerouslySetInnerHTML\"===o?null!=(c=c?c.__html:void 0)&&fe(e,c):\"children\"===o?\"string\"==typeof c?(\"textarea\"!==n||\"\"!==c)&&pe(e,c):\"number\"==typeof c&&pe(e,\"\"+c):\"suppressContentEditableWarning\"!==o&&\"suppressHydrationWarning\"!==o&&\"autoFocus\"!==o&&(u.hasOwnProperty(o)?null!=c&&\"onScroll\"===o&&Ur(\"scroll\",e):null!=c&&b(e,o,c,l))}switch(n){case\"input\":q(e),J(e,r,!1);break;case\"textarea\":q(e),oe(e);break;case\"option\":null!=r.value&&e.setAttribute(\"value\",\"\"+V(r.value));break;case\"select\":e.multiple=!!r.multiple,null!=(o=r.value)?ne(e,!!r.multiple,o,!1):null!=r.defaultValue&&ne(e,!!r.multiple,r.defaultValue,!0);break;default:\"function\"==typeof a.onClick&&(e.onclick=Jr)}switch(n){case\"button\":case\"input\":case\"select\":case\"textarea\":r=!!r.autoFocus;break e;case\"img\":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}null!==t.ref&&(t.flags|=512,t.flags|=2097152)}return Qu(t),null;case 6:if(e&&null!=t.stateNode)Fu(e,t,e.memoizedProps,r);else{if(\"string\"!=typeof r&&null===t.stateNode)throw 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0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!=(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&uo.current)?0===Fl&&(Fl=3):gs())),null!==t.updateQueue&&(t.flags|=4),Qu(t),null);case 4:return ao(),Ou(e,t),null===e&&Br(t.stateNode.containerInfo),Qu(t),null;case 10:return xi(t.type._context),Qu(t),null;case 19:if(Ea(uo),null===(o=t.memoizedState))return 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",
      "text/plain": [
       "<Figure size 800x650 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import shap\n",
    "\n",
    "np.random.seed(42)\n",
    "shap.initjs()\n",
    "\n",
    "explainer = shap.Explainer(model)\n",
    "shap_values = explainer(\n",
    "    candidates.iloc[\n",
    "        [0] + list(np.random.choice(\n",
    "            list(range(1, len(candidates) - 1)),\n",
    "            1000,\n",
    "            replace=False,\n",
    "        )) + [len(candidates) - 1]\n",
    "    ]\n",
    ")\n",
    "\n",
    "shap.plots.waterfall(shap_values[0, :, 1])\n",
    "\n",
    "# And here is the SHAP explanation for the top candidate. According to SHAP, the\n",
    "# sustained decreases the translation success probability the most, which is consistent\n",
    "# with our finding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do you use this results?\n",
    "\n",
    "Next, you will perform the recommended preclinical trial and collect the preclinical outcome. The current model uses $\\delta < 0.5\\sigma$ as the threshold, corresponding to ~20%. If the preclinical outcome, i.e., the survival rate, is 80%, the model predicted, with 92.4% of the proabability, that the clinical outcome, i.e., recovery rate, would be 60-100%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ace",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}