3_ddc_jaccard_parallel_search.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DDC-J Parallel Parameter Search\n",
    "\n",
    "We'll start with our standard set of initial imports\n",
    "\n",
    "Including the JobLib for Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "import outliers\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from outliers import ddc_outlier\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "import multiprocessing\n",
    "import tempfile\n",
    "import os\n",
    "from joblib import Parallel, delayed\n",
    "from joblib.pool import has_shareable_memory\n",
    "cpuN = multiprocessing.cpu_count()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "prescription = pd.read_csv('data/prescriptions_sample.csv.gz', compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medications = outliers.getOverdoseMedications(prescription)\n",
    "medications.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parallel Method\n",
    "\n",
    "Here we need to define the parallel method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def computeClf(X,Y,epsilon,scores):\n",
    "    clf = ddc_outlier(alpha=epsilon,metric='jaccard')\n",
    "    clf.fit(X)\n",
    "    y_pred = clf.predict(X)\n",
    "\n",
    "    y_pred[y_pred == 1] = 0\n",
    "    y_pred[y_pred == -1] = 1\n",
    "\n",
    "    f = f1_score(y_pred, Y)\n",
    "    scores[int(epsilon*100)] = f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parallel Search\n",
    "\n",
    "For each medication we call the method in parallel, considering the number of cores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALOPURINOL:  4729\n",
      "AMPICILINA:  7226\n",
      "ANLODIPINO:  10392\n",
      "BISACODIL:  9483\n",
      "CETOPROFENO:  10907\n",
      "CLORETO:  5593\n",
      "DIAZEPAM:  9030\n",
      "DICLOFENACO:  5559\n",
      "DOXAZOSINA:  6280\n",
      "ENALAPRIL:  9585\n",
      "ENOXAPARINA:  7666\n",
      "HIDRALAZINA:  6056\n",
      "LOPERAMIDA:  4957\n",
      "MORFINA:  5362\n",
      "PARACETAMOL:  10811\n",
      "POLIMIXINA:  7257\n",
      "RISPERIDONA:  5431\n",
      "SULFAMETOXAZOL:  7086\n",
      "TENOFOVIR:  4518\n",
      "VARFARINA:  6419\n",
      "VITAMINAS:  5766\n"
     ]
    }
   ],
   "source": [
    "scores_df = pd.DataFrame()\n",
    "\n",
    "folder = tempfile.mkdtemp()\n",
    "score_name = os.path.join(folder, 'score')\n",
    "\n",
    "ep_range = np.arange(0.01,1.0,0.01)\n",
    "\n",
    "for med in medications:\n",
    "\n",
    "    X, Y = outliers.getPrescriptions(prescription, med)\n",
    "    print(med + ': ',len(X))\n",
    "\n",
    "    scores = np.memmap(score_name, dtype=float, shape=(150), mode='w+')\n",
    "    Parallel(n_jobs=cpuN)(delayed(computeClf)(X,Y,epsilon,scores)\n",
    "                   for epsilon in ep_range)\n",
    "    \n",
    "    for ep in ep_range:\n",
    "        idx = int(ep*100)\n",
    "        scores_df.loc[med,ep] = scores[idx]\n",
    "    \n",
    "    epsilon = scores_df.idxmax(1).values[0]\n",
    "    f1 = scores_df.max(1).values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Plot\n",
    "\n",
    "For the first 9 medications, we print the F-Mesaure variation for each parameter\n",
    "\n",
    "Look that, usually, the best parameter is below 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f728277f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8), dpi= 300)\n",
    "colors = ['k','r','b','g','m','y','pink','brown','gray']\n",
    "i = 0\n",
    "for med in scores_df.index:\n",
    "    plt.plot(np.asarray(scores_df.columns),np.asarray(scores_df.loc[med].values), c=colors[i],label=med)\n",
    "    i += 1\n",
    "    if i > 8:\n",
    "        break\n",
    "    \n",
    "plt.legend(bbox_to_anchor=(1, 0.70), fontsize=16)\n",
    "plt.xlabel('DDC-J Threshold', fontsize=16)\n",
    "plt.ylabel('F-Measure', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Plot\n",
    "\n",
    "And here the plot with the mean F-Measure for all medications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7283fe0dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8), dpi= 300)\n",
    "plt.plot(np.asarray(scores_df.mean(0).index),np.asarray(scores_df.mean(0).values), c='k',label='mean')\n",
    "plt.legend(bbox_to_anchor=(1, 0.70), fontsize=16)\n",
    "plt.xlabel('DDC-J Threshold', fontsize=16)\n",
    "plt.ylabel('F-Measure', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}