prediction/4-predictr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit the final model and make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "library(magrittr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "blacklist = readr::read_tsv('features/blacklist.tsv')$feature\n",
    "feature_df = readr::read_tsv('features/transformed-features.tsv.bz2') %>%\n",
    "  dplyr::select(-one_of(blacklist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>compound_id</th><th scope=col>compound_name</th><th scope=col>disease_id</th><th scope=col>disease_name</th><th scope=col>category</th><th scope=col>status</th><th scope=col>prior_prob</th><th scope=col>prior_logit</th><th scope=col>degree_CbG</th><th scope=col>degree_CcSE</th><th scope=col>ellip.h</th><th scope=col>dwpc_CuG<rGiGaD</th><th scope=col>dwpc_CuGaDuGaD</th><th scope=col>dwpc_CuGcG<rGaD</th><th scope=col>dwpc_CuGcGiGaD</th><th scope=col>dwpc_CuGdDpSpD</th><th scope=col>dwpc_CuGr>GbCtD</th><th scope=col>dwpc_CuGuCtD</th><th scope=col>dwpc_CuGuCtDrD</th><th scope=col>dwpc_CuGuCuGaD</th><th scope=col>dwpc_CuGuDuGaD</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>DB01048</td><td>Abacavir</td><td>DOID:10652</td><td>Alzheimer's disease</td><td>NA</td><td>0</td><td>0.004753</td><td>-5.344</td><td>-0.2955</td><td>0.8146</td><td>⋯</td><td>-0.6116</td><td>-0.5028</td><td>-0.4211</td><td>-0.4581</td><td>-0.5399</td><td>-0.357</td><td>-0.375</td><td>-0.5642</td><td>-0.5555</td><td>-0.4952</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>DB05812</td><td>Abiraterone</td><td>DOID:10652</td><td>Alzheimer's disease</td><td>NA</td><td>0</td><td>0.004753</td><td>-5.344</td><td>0.5495</td><td>0.5554</td><td>⋯</td><td>-0.6116</td><td>-0.5028</td><td>-0.4211</td><td>-0.4581</td><td>-0.5399</td><td>-0.357</td><td>-0.375</td><td>-0.5642</td><td>-0.5555</td><td>-0.4952</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       "  & compound_id & compound_name & disease_id & disease_name & category & status & prior_prob & prior_logit & degree_CbG & degree_CcSE & ellip.h & dwpc_CuG<rGiGaD & dwpc_CuGaDuGaD & dwpc_CuGcG<rGaD & dwpc_CuGcGiGaD & dwpc_CuGdDpSpD & dwpc_CuGr>GbCtD & dwpc_CuGuCtD & dwpc_CuGuCtDrD & dwpc_CuGuCuGaD & dwpc_CuGuDuGaD\\\\\n",
       "\\hline\n",
       "\t1 & DB01048 & Abacavir & DOID:10652 & Alzheimer's disease & NA & 0 & 0.004753 & -5.344 & -0.2955 & 0.8146 & ⋯ & -0.6116 & -0.5028 & -0.4211 & -0.4581 & -0.5399 & -0.357 & -0.375 & -0.5642 & -0.5555 & -0.4952\\\\\n",
       "\t2 & DB05812 & Abiraterone & DOID:10652 & Alzheimer's disease & NA & 0 & 0.004753 & -5.344 & 0.5495 & 0.5554 & ⋯ & -0.6116 & -0.5028 & -0.4211 & -0.4581 & -0.5399 & -0.357 & -0.375 & -0.5642 & -0.5555 & -0.4952\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2 x 145]\n",
       "\n",
       "  compound_id compound_name disease_id        disease_name category status\n",
       "        (chr)         (chr)      (chr)               (chr)    (chr)  (int)\n",
       "1     DB01048      Abacavir DOID:10652 Alzheimer's disease       NA      0\n",
       "2     DB05812   Abiraterone DOID:10652 Alzheimer's disease       NA      0\n",
       "Variables not shown: prior_prob (dbl), prior_logit (dbl), degree_CbG (dbl),\n",
       "  degree_CcSE (dbl), degree_CdG (dbl), degree_CiPC (dbl), degree_CpD (dbl),\n",
       "  degree_CrC (dbl), degree_CuG (dbl), degree_DaG (dbl), degree_DdG (dbl),\n",
       "  degree_DlA (dbl), degree_DpC (dbl), degree_DpS (dbl), degree_DrD (dbl),\n",
       "  degree_DuG (dbl), dwpc_CbG<rG<rGaD (dbl), dwpc_CbG<rGaD (dbl),\n",
       "  dwpc_CbG<rGaDrD (dbl), dwpc_CbG<rGcGaD (dbl), dwpc_CbG<rGeAlD (dbl),\n",
       "  dwpc_CbG<rGiGaD (dbl), dwpc_CbG<rGr>GaD (dbl), dwpc_CbGaD (dbl),\n",
       "  dwpc_CbGaDaGaD (dbl), dwpc_CbGaDlAlD (dbl), dwpc_CbGaDpSpD (dbl),\n",
       "  dwpc_CbGaDrD (dbl), dwpc_CbGaDrDrD (dbl), dwpc_CbGbCbGaD (dbl),\n",
       "  dwpc_CbGbCdGaD (dbl), dwpc_CbGbCrCtD (dbl), dwpc_CbGbCtD (dbl),\n",
       "  dwpc_CbGbCtDrD (dbl), dwpc_CbGbCuGaD (dbl), dwpc_CbGdAdGaD (dbl),\n",
       "  dwpc_CbGdCdGaD (dbl), dwpc_CbGdCrCtD (dbl), dwpc_CbGdCtDrD (dbl),\n",
       "  dwpc_CbGdCuGaD (dbl), dwpc_CbGdDdGaD (dbl), dwpc_CbGdDuGaD (dbl),\n",
       "  dwpc_CbGeAeGaD (dbl), dwpc_CbGeAlD (dbl), dwpc_CbGeAlDrD (dbl),\n",
       "  dwpc_CbGeAuGaD (dbl), dwpc_CbGiGaD (dbl), dwpc_CbGiGaDrD (dbl),\n",
       "  dwpc_CbGiGbCtD (dbl), dwpc_CbGiGiGaD (dbl), dwpc_CbGiGuAlD (dbl),\n",
       "  dwpc_CbGpBPpGaD (dbl), dwpc_CbGpMFpGaD (dbl), dwpc_CbGpPWpGaD (dbl),\n",
       "  dwpc_CbGr>G<rGaD (dbl), dwpc_CbGr>G<rGdD (dbl), dwpc_CbGr>GaD (dbl),\n",
       "  dwpc_CbGr>GaDrD (dbl), dwpc_CbGr>GeAlD (dbl), dwpc_CbGr>GiGaD (dbl),\n",
       "  dwpc_CbGr>Gr>GaD (dbl), dwpc_CbGuAeGaD (dbl), dwpc_CbGuAlD (dbl),\n",
       "  dwpc_CbGuAlDrD (dbl), dwpc_CbGuAuGaD (dbl), dwpc_CbGuCdGaD (dbl),\n",
       "  dwpc_CbGuCtDrD (dbl), dwpc_CbGuCuGaD (dbl), dwpc_CbGuDaGaD (dbl),\n",
       "  dwpc_CbGuDpSpD (dbl), dwpc_CbGuDrDrD (dbl), dwpc_CbGuDuGaD (dbl),\n",
       "  dwpc_CcSEcCbGaD (dbl), dwpc_CcSEcCtD (dbl), dwpc_CcSEcCtDrD (dbl),\n",
       "  dwpc_CcSEcCuGaD (dbl), dwpc_CdGaDdGaD (dbl), dwpc_CdGaDuGaD (dbl),\n",
       "  dwpc_CdGbCdGaD (dbl), dwpc_CdGbCrCtD (dbl), dwpc_CdGbCtD (dbl),\n",
       "  dwpc_CdGcG<rGaD (dbl), dwpc_CdGcGiGaD (dbl), dwpc_CdGdCtD (dbl),\n",
       "  dwpc_CdGdCtDrD (dbl), dwpc_CdGdCuGaD (dbl), dwpc_CdGiG<rGaD (dbl),\n",
       "  dwpc_CdGiGaD (dbl), dwpc_CdGiGiGaD (dbl), dwpc_CdGuDaGaD (dbl),\n",
       "  dwpc_CdGuDpSpD (dbl), dwpc_CiPCiCbGaD (dbl), dwpc_CiPCiCdGaD (dbl),\n",
       "  dwpc_CiPCiCdGuD (dbl), dwpc_CiPCiCrCtD (dbl), dwpc_CiPCiCtD (dbl),\n",
       "  dwpc_CiPCiCuGaD (dbl), dwpc_CpDpCtD (dbl), dwpc_CpDpCtDrD (dbl),\n",
       "  dwpc_CpDrDlAlD (dbl), dwpc_CpDrDpSpD (dbl), dwpc_CrCbGaD (dbl),\n",
       "  dwpc_CrCbGaDrD (dbl), dwpc_CrCbGbCtD (dbl), dwpc_CrCbGeAlD (dbl),\n",
       "  dwpc_CrCbGiGaD (dbl), dwpc_CrCbGuAlD (dbl), dwpc_CrCdGaD (dbl),\n",
       "  dwpc_CrCdGaDrD (dbl), dwpc_CrCdGbCtD (dbl), dwpc_CrCdGcGaD (dbl),\n",
       "  dwpc_CrCdGeAlD (dbl), dwpc_CrCdGiGaD (dbl), dwpc_CrCiPCiCtD (dbl),\n",
       "  dwpc_CrCrCbGaD (dbl), dwpc_CrCrCdGaD (dbl), dwpc_CrCrCrCtD (dbl),\n",
       "  dwpc_CrCrCtD (dbl), dwpc_CrCtD (dbl), dwpc_CrCtDtCtD (dbl), dwpc_CrCuG<rGaD\n",
       "  (dbl), dwpc_CrCuGaD (dbl), dwpc_CrCuGaDrD (dbl), dwpc_CrCuGcGaD (dbl),\n",
       "  dwpc_CrCuGiGaD (dbl), dwpc_CtDrD (dbl), dwpc_CtDtCtD (dbl), dwpc_CuG<rG<rGaD\n",
       "  (dbl), dwpc_CuG<rGaD (dbl), dwpc_CuG<rGiGaD (dbl), dwpc_CuGaDuGaD (dbl),\n",
       "  dwpc_CuGcG<rGaD (dbl), dwpc_CuGcGiGaD (dbl), dwpc_CuGdDpSpD (dbl),\n",
       "  dwpc_CuGr>GbCtD (dbl), dwpc_CuGuCtD (dbl), dwpc_CuGuCtDrD (dbl),\n",
       "  dwpc_CuGuCuGaD (dbl), dwpc_CuGuDuGaD (dbl)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head(feature_df, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>29799</li>\n",
       "\t<li>138</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 29799\n",
       "\\item 138\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 29799\n",
       "2. 138\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 29799   138"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = feature_df %>%\n",
    "  dplyr::filter(prior_prob > 0)\n",
    "\n",
    "X_train = train_df %>%\n",
    "  dplyr::select(prior_logit, starts_with('degree_'), starts_with('dwpc_')) %>%\n",
    "  as.matrix()\n",
    "dim(X_train)\n",
    "\n",
    "y_train = train_df$status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lambda = 'lambda.1se'\n",
    "penalty = ifelse(colnames(X_train) == 'prior_logit', 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: Matrix\n",
      "Loading required package: foreach\n",
      "Loaded glmnet 2.0-5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit = hetior::glmnet_train(X = X_train, y = y_train, alpha = 0.2, s = lambda, cores = 10, seed = 0,\n",
    "  penalty.factor=penalty, lambda.min.ratio=1e-8, nlambda=150, standardize=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "0.00592772586596359"
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       "[1] 0.005927726"
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    {
     "data": {
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      ],
      "text/plain": [
       "plot without title"
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     },
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      "image/svg+xml": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow = c(2,1), oma = c(5,4,0,0) + 0.1, mar = c(0,0,1,1) + 0.1)\n",
    "plot(fit$cv_model$glmnet.fit, xvar = \"lambda\", label = TRUE)\n",
    "plot(fit$cv_model)\n",
    "fit$cv_model[[lambda]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "0.701731617396781"
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       "[1] 0.7017316"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The coefficient for prior_logit should be around 1 for our approach to work well\n",
    "dplyr::filter(fit$coef_df, feature == 'prior_logit')$coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       ".\n",
       "-1  1 \n",
       "18 13 "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of negative and positive coefficients\n",
    "fit$coef_df %>%\n",
    "  dplyr::filter(feature != 'intercept', zcoef != 0) %>%\n",
    "  .[['zcoef']] %>% sign %>% table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "754.990275"
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       "754.990275"
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      "text/markdown": [
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      "text/plain": [
       "[1] 754.9903"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "'0.361%'"
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       "'0.361%'"
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      "text/plain": [
       "[1] \"0.361%\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum of prior (should sum to the number of treatments)\n",
    "sum(feature_df$prior_prob)\n",
    "\n",
    "# Prevalence of positives as percent\n",
    "scales::percent(mean(feature_df$status))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'0.286%'"
      ],
      "text/latex": [
       "'0.286%'"
      ],
      "text/markdown": [
       "'0.286%'"
      ],
      "text/plain": [
       "[1] \"0.286%\""
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_all = feature_df %>%\n",
    "  dplyr::mutate(prior_logit = boot::logit(mean(status))) %>%\n",
    "  dplyr::select(one_of(colnames(X_train))) %>%\n",
    "  as.matrix()\n",
    "\n",
    "y_predicted = hetior::glmnet_predict(fit$cv_model, X = X_all, s = lambda)\n",
    "y_predicted %>% mean() %>% scales::percent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining by: c(\"compound_id\", \"disease_id\")\n",
      "Joining by: c(\"compound_id\", \"compound_name\", \"disease_id\", \"disease_name\")\n"
     ]
    }
   ],
   "source": [
    "train_pred_df = train_df %>%\n",
    "  dplyr::select(compound_id, disease_id) %>%\n",
    "  dplyr::bind_cols(dplyr::data_frame(training_prediction = fit$y_pred))\n",
    "\n",
    "predict_df = feature_df %>%\n",
    "  dplyr::select(compound_id:prior_prob, starts_with('status')) %>%\n",
    "  dplyr::mutate(prediction = y_predicted) %>%\n",
    "  dplyr::left_join(train_pred_df) %>%\n",
    "  dplyr::group_by(compound_id) %>%\n",
    "  dplyr::mutate(compound_percentile = dplyr::cume_dist(prediction)) %>%\n",
    "  dplyr::ungroup() %>%\n",
    "  dplyr::group_by(disease_id) %>%\n",
    "  dplyr::mutate(disease_percentile = dplyr::cume_dist(prediction)) %>%\n",
    "  dplyr::ungroup() %>%\n",
    "  dplyr::left_join(readr::read_tsv('../validate/validation-statuses.tsv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>compound_id</th><th scope=col>compound_name</th><th scope=col>disease_id</th><th scope=col>disease_name</th><th scope=col>category</th><th scope=col>status</th><th scope=col>prior_prob</th><th scope=col>prediction</th><th scope=col>training_prediction</th><th scope=col>compound_percentile</th><th scope=col>disease_percentile</th><th scope=col>n_trials</th><th scope=col>status_trials</th><th scope=col>status_drugcentral</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>DB00282</td><td>Pamidronate</td><td>DOID:11476</td><td>osteoporosis</td><td>DM</td><td>1</td><td>0.03893</td><td>0.8868986</td><td>0.9771082</td><td>1</td><td>1</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>DB00630</td><td>Alendronate</td><td>DOID:11476</td><td>osteoporosis</td><td>DM</td><td>1</td><td>0.03893</td><td>0.8849911</td><td>0.9766821</td><td>1</td><td>0.9993498</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & compound_id & compound_name & disease_id & disease_name & category & status & prior_prob & prediction & training_prediction & compound_percentile & disease_percentile & n_trials & status_trials & status_drugcentral\\\\\n",
       "\\hline\n",
       "\t1 & DB00282 & Pamidronate & DOID:11476 & osteoporosis & DM & 1 & 0.03893 & 0.8868986 & 0.9771082 & 1 & 1 & NA & NA & NA\\\\\n",
       "\t2 & DB00630 & Alendronate & DOID:11476 & osteoporosis & DM & 1 & 0.03893 & 0.8849911 & 0.9766821 & 1 & 0.9993498 & NA & NA & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2 x 14]\n",
       "\n",
       "  compound_id compound_name disease_id disease_name category status prior_prob\n",
       "        (chr)         (chr)      (chr)        (chr)    (chr)  (int)      (dbl)\n",
       "1     DB00282   Pamidronate DOID:11476 osteoporosis       DM      1    0.03893\n",
       "2     DB00630   Alendronate DOID:11476 osteoporosis       DM      1    0.03893\n",
       "Variables not shown: prediction (dbl), training_prediction (dbl),\n",
       "  compound_percentile (dbl), disease_percentile (dbl), n_trials (int),\n",
       "  status_trials (int), status_drugcentral (int)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_df %>%\n",
    "  dplyr::arrange(desc(prediction)) %>%\n",
    "  head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>y_true_var</th><th scope=col>y_pred_var</th><th scope=col>n_pos</th><th scope=col>n_neg</th><th scope=col>auroc</th><th scope=col>auprc</th><th scope=col>tjur</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>status</td><td>compound_percentile</td><td>755</td><td>208413</td><td>0.9362958</td><td>0.03706945</td><td>0.4357167</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>status_drugcentral</td><td>compound_percentile</td><td>208</td><td>207572</td><td>0.8518688</td><td>0.006409814</td><td>0.3513426</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>status_trials</td><td>compound_percentile</td><td>5594</td><td>202186</td><td>0.6625427</td><td>0.0515353</td><td>0.1619315</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>status</td><td>disease_percentile</td><td>755</td><td>208413</td><td>0.9499424</td><td>0.1037369</td><td>0.4502484</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>status_drugcentral</td><td>disease_percentile</td><td>208</td><td>207572</td><td>0.8073453</td><td>0.01014896</td><td>0.3074722</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>status_trials</td><td>disease_percentile</td><td>5594</td><td>202186</td><td>0.6586254</td><td>0.06944733</td><td>0.1584726</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>status</td><td>prediction</td><td>755</td><td>208413</td><td>0.974478</td><td>0.381718</td><td>0.1247884</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>status_drugcentral</td><td>prediction</td><td>208</td><td>207572</td><td>0.8550991</td><td>0.05563322</td><td>0.05479972</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>status_trials</td><td>prediction</td><td>5594</td><td>202186</td><td>0.6997335</td><td>0.09291836</td><td>0.005629416</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>status</td><td>prior_prob</td><td>755</td><td>208413</td><td>0.9791663</td><td>0.1628536</td><td>0.1068422</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>status_drugcentral</td><td>prior_prob</td><td>208</td><td>207572</td><td>0.5410436</td><td>0.003122691</td><td>0.01721512</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>status_trials</td><td>prior_prob</td><td>5594</td><td>202186</td><td>0.6252934</td><td>0.08657654</td><td>0.01472654</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>status</td><td>training_prediction</td><td>755</td><td>29044</td><td>0.9512072</td><td>0.5901544</td><td>0.3794469</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>status_drugcentral</td><td>training_prediction</td><td>42</td><td>28819</td><td>0.8734978</td><td>0.06512722</td><td>0.230363</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>status_trials</td><td>training_prediction</td><td>2044</td><td>26817</td><td>0.7239957</td><td>0.202083</td><td>0.04183229</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       "  & y_true_var & y_pred_var & n_pos & n_neg & auroc & auprc & tjur\\\\\n",
       "\\hline\n",
       "\t1 & status & compound_percentile & 755 & 208413 & 0.9362958 & 0.03706945 & 0.4357167\\\\\n",
       "\t2 & status_drugcentral & compound_percentile & 208 & 207572 & 0.8518688 & 0.006409814 & 0.3513426\\\\\n",
       "\t3 & status_trials & compound_percentile & 5594 & 202186 & 0.6625427 & 0.0515353 & 0.1619315\\\\\n",
       "\t4 & status & disease_percentile & 755 & 208413 & 0.9499424 & 0.1037369 & 0.4502484\\\\\n",
       "\t5 & status_drugcentral & disease_percentile & 208 & 207572 & 0.8073453 & 0.01014896 & 0.3074722\\\\\n",
       "\t6 & status_trials & disease_percentile & 5594 & 202186 & 0.6586254 & 0.06944733 & 0.1584726\\\\\n",
       "\t7 & status & prediction & 755 & 208413 & 0.974478 & 0.381718 & 0.1247884\\\\\n",
       "\t8 & status_drugcentral & prediction & 208 & 207572 & 0.8550991 & 0.05563322 & 0.05479972\\\\\n",
       "\t9 & status_trials & prediction & 5594 & 202186 & 0.6997335 & 0.09291836 & 0.005629416\\\\\n",
       "\t10 & status & prior_prob & 755 & 208413 & 0.9791663 & 0.1628536 & 0.1068422\\\\\n",
       "\t11 & status_drugcentral & prior_prob & 208 & 207572 & 0.5410436 & 0.003122691 & 0.01721512\\\\\n",
       "\t12 & status_trials & prior_prob & 5594 & 202186 & 0.6252934 & 0.08657654 & 0.01472654\\\\\n",
       "\t13 & status & training_prediction & 755 & 29044 & 0.9512072 & 0.5901544 & 0.3794469\\\\\n",
       "\t14 & status_drugcentral & training_prediction & 42 & 28819 & 0.8734978 & 0.06512722 & 0.230363\\\\\n",
       "\t15 & status_trials & training_prediction & 2044 & 26817 & 0.7239957 & 0.202083 & 0.04183229\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [15 x 7]\n",
       "Groups: <by row>\n",
       "\n",
       "           y_true_var          y_pred_var n_pos  n_neg     auroc       auprc\n",
       "                (chr)               (chr) (int)  (int)     (dbl)       (dbl)\n",
       "1              status compound_percentile   755 208413 0.9362958 0.037069453\n",
       "2  status_drugcentral compound_percentile   208 207572 0.8518688 0.006409814\n",
       "3       status_trials compound_percentile  5594 202186 0.6625427 0.051535296\n",
       "4              status  disease_percentile   755 208413 0.9499424 0.103736942\n",
       "5  status_drugcentral  disease_percentile   208 207572 0.8073453 0.010148964\n",
       "6       status_trials  disease_percentile  5594 202186 0.6586254 0.069447326\n",
       "7              status          prediction   755 208413 0.9744780 0.381717968\n",
       "8  status_drugcentral          prediction   208 207572 0.8550991 0.055633215\n",
       "9       status_trials          prediction  5594 202186 0.6997335 0.092918358\n",
       "10             status          prior_prob   755 208413 0.9791663 0.162853635\n",
       "11 status_drugcentral          prior_prob   208 207572 0.5410436 0.003122691\n",
       "12      status_trials          prior_prob  5594 202186 0.6252934 0.086576541\n",
       "13             status training_prediction   755  29044 0.9512072 0.590154435\n",
       "14 status_drugcentral training_prediction    42  28819 0.8734978 0.065127224\n",
       "15      status_trials training_prediction  2044  26817 0.7239957 0.202083010\n",
       "Variables not shown: tjur (dbl)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_metrics = function(params) {\n",
    "  y_true_var = params$y_true_var\n",
    "  y_pred_var = params$y_pred_var\n",
    "  vtm_df = predict_df[, c(y_true_var, y_pred_var)] %>% na.omit()\n",
    "  y_true = vtm_df[[1]]\n",
    "  vtm = hetior::calc_vtms(y_true = y_true, y_pred = vtm_df[[2]], is_prob = T)\n",
    "  dplyr::data_frame(\n",
    "      y_true_var = y_true_var, y_pred_var = y_pred_var,\n",
    "      n_pos = sum(y_true == 1), n_neg = sum(y_true == 0),\n",
    "      auroc = vtm$auroc, auprc = vtm$auprc, tjur = vtm$tjur\n",
    "  ) %>% return()\n",
    "}\n",
    "\n",
    "perf_df = tidyr::crossing(\n",
    "  y_pred_var = c('prediction', 'compound_percentile', 'disease_percentile', 'training_prediction', 'prior_prob'),\n",
    "  y_true_var = c('status', 'status_drugcentral', 'status_trials')\n",
    ") %>%\n",
    "  dplyr::rowwise() %>%\n",
    "  dplyr::do(get_metrics(.))\n",
    "\n",
    "perf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write results to TSVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "perf_df %>% readr::write_tsv('predictions/performance.tsv')\n",
    "fit$coef_df %>% readr::write_tsv('model/coefficient.tsv')\n",
    "predict_df %>% readr::write_tsv('predictions/probabilities.tsv', na = '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write an xlsx with top predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining by: c(\"compound_id\", \"disease_id\")\n",
      "Warning message:\n",
      ": Overwriting existing cell data.Warning message:\n",
      ": Overwriting existing cell data."
     ]
    }
   ],
   "source": [
    "# Read trials\n",
    "url = 'https://github.com/dhimmel/clintrials/blob/7c65dec7b69322ca2f8ba2b170c1b3dbd92ebff8/data/DrugBank-DO-slim.tsv?raw=true'\n",
    "trial_df = readr::read_tsv(url) %>%\n",
    "  dplyr::group_by(compound_id, disease_id) %>%\n",
    "  dplyr::summarize(n_trials = n()) %>%\n",
    "  dplyr::ungroup()\n",
    "\n",
    "# Extract most pertinent columns and top predictions\n",
    "top_df = predict_df %>%\n",
    "  dplyr::arrange(desc(prediction)) %>%\n",
    "  dplyr::select(compound_id, disease_id, compound_name, disease_name, category, prediction, compound_percentile, disease_percentile, prior_prob) %>%\n",
    "  dplyr::filter((prediction > 0.01)) %>%\n",
    "  dplyr::left_join(trial_df) %>%\n",
    "  dplyr::mutate(n_trials = ifelse(is.na(n_trials), 0, n_trials))\n",
    "\n",
    "compound_hyperlink = setNames(paste0('http://www.drugbank.ca/drugs/', top_df$compound_id), top_df$compound_name)\n",
    "disease_hyperlink = setNames(paste0('http://www.disease-ontology.org/?id=', top_df$disease_id), top_df$disease_name)\n",
    "class(compound_hyperlink) = 'hyperlink'\n",
    "class(disease_hyperlink) = 'hyperlink'\n",
    "top_df %<>% dplyr::select(-ends_with('_id'))\n",
    "\n",
    "# Convert columns to percent for pretty formatting\n",
    "percent_cols = c('prediction', 'compound_percentile', 'disease_percentile', 'prior_prob')\n",
    "for (percent_col in percent_cols) {\n",
    "  class(top_df[[percent_col]]) = 'percentage'\n",
    "}\n",
    "\n",
    "# Write to an excel file\n",
    "workbook = openxlsx::createWorkbook('Project Rephetio')\n",
    "sheet = 'Top Predictions'\n",
    "openxlsx::addWorksheet(workbook, sheet)\n",
    "openxlsx::writeDataTable(workbook, sheet, top_df)\n",
    "openxlsx::writeData(workbook, sheet, compound_hyperlink, startCol = 1, startRow = 2)\n",
    "openxlsx::writeData(workbook, sheet, disease_hyperlink, startCol = 2, startRow = 2)\n",
    "openxlsx::freezePane(workbook, sheet, firstRow = T)\n",
    "openxlsx::setColWidths(workbook, sheet, cols=1:7, widths='auto')\n",
    "openxlsx::saveWorkbook(workbook, './predictions/top-predictions.xlsx', overwrite = T)"
   ]
  }
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