Sample_DDI_Prediction_RDF2Vec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import sys\n",
    "import pandas as pd\n",
    "import itertools\n",
    "import math\n",
    "import time\n",
    "\n",
    "from sklearn import svm, linear_model, neighbors\n",
    "from sklearn import tree, ensemble\n",
    "from sklearn import metrics\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "import networkx as nx\n",
    "import random\n",
    "import numbers\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from src import ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init(\"C:/Users/admin-karim/Downloads/spark-2.2.0-bin-hadoop2.7\")\n",
    "\n",
    "from pyspark import SparkConf, SparkContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<SparkContext master=local[5] appName=pyspark-shell>\n"
     ]
    }
   ],
   "source": [
    "if False: \n",
    "    sc.stop()\n",
    "\n",
    "config = SparkConf()\n",
    "config.setMaster(\"local[5]\")\n",
    "config.set(\"spark.executor.memory\", \"8g\")\n",
    "config.set('spark.driver.memory', '12g')\n",
    "config.set(\"spark.memory.offHeap.enabled\",True)\n",
    "config.set(\"spark.memory.offHeap.size\",\"20g\") \n",
    "sc = SparkContext(conf=config)\n",
    "print (sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ddiDataGenerator(ddi_list, vectors):\n",
    "    ddi_df = pd.read_csv(ddi_list, sep='\\t')\n",
    "\n",
    "    embedding_df = pd.read_csv(vectors, delimiter = '\\t') \n",
    "\n",
    "    embedding_df.Entity = embedding_df.Entity.str[-8:-1]\n",
    "    embedding_df.rename(columns = {'Entity':'Drug'}, inplace = True)\n",
    "\n",
    "    len(set(ddi_df.Drug1.unique()).union(ddi_df.Drug2.unique()) )\n",
    "    pairs, classes = ml.generatePairs(ddi_df, embedding_df)\n",
    "    \n",
    "    return pairs, classes, embedding_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drugbank v5  RDF2Vec SG "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB04868</td>\n",
       "      <td>DB09199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB01623</td>\n",
       "      <td>DB00804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DB01021</td>\n",
       "      <td>DB08931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DB01208</td>\n",
       "      <td>DB09321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DB06754</td>\n",
       "      <td>DB07477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Drug1     Drug2\n",
       "0  DB04868    DB09199\n",
       "1  DB01623    DB00804\n",
       "2  DB01021    DB08931\n",
       "3  DB01208    DB09321\n",
       "4  DB06754    DB07477"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddi_list = 'input/full_DDI.txt'\n",
    "vectors = 'vectors/RDF2Vec_cbow_300_5_5_2_500_d5_uniform.txt'\n",
    "\n",
    "ddi_df = pd.read_csv(ddi_list, sep='\\t')\n",
    "ddi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs, classes, embedding_df = ddiDataGenerator(ddi_list, vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['DB00005' 'DB00007']\n",
      " ['DB00005' 'DB00009']\n",
      " ['DB00005' 'DB00014']\n",
      " ...\n",
      " ['DB08811' 'DB08816']\n",
      " ['DB08811' 'DB08819']\n",
      " ['DB08816' 'DB08819']]\n"
     ]
    }
   ],
   "source": [
    "print(pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Drug  feature0  feature1  feature2  feature3  feature4  feature5  \\\n",
      "0  DB00001 -0.903437 -0.331749 -0.369652  0.439574  1.541132 -1.336791   \n",
      "1  DB08860  1.384768  0.363517  0.557544  2.626180  2.502104  1.148705   \n",
      "2  DB06605 -0.113727 -0.203172  1.686063  2.237446  1.583634  0.420009   \n",
      "3  DB06695  1.553845 -1.943734  0.040946  0.987024 -1.168784  0.878595   \n",
      "4  DB01254  1.605805 -0.298084  0.441323  2.509530  2.398917 -1.055412   \n",
      "\n",
      "   feature6  feature7  feature8     ...      feature290  feature291  \\\n",
      "0 -1.635298 -0.560466  0.000102     ...       -0.694317   -0.008376   \n",
      "1 -3.365903 -1.807856 -1.510181     ...        1.365525   -0.723049   \n",
      "2 -1.796523 -1.203906 -2.252901     ...        0.673704   -0.146191   \n",
      "3 -0.538334  0.557914 -0.010863     ...        0.997751   -0.345845   \n",
      "4  0.433553 -2.312896 -0.097910     ...        2.142729   -0.245621   \n",
      "\n",
      "   feature292  feature293  feature294  feature295  feature296  feature297  \\\n",
      "0    0.411577    0.432441    0.777116    0.081497    0.870548    0.428174   \n",
      "1    1.239299   -0.514180   -0.051110    0.627170    2.924956    1.248235   \n",
      "2    1.719473   -1.306523    0.696576   -1.050315    2.090180    1.084697   \n",
      "3   -0.232929   -1.119504   -0.570450   -0.270374    1.786494    0.915425   \n",
      "4    2.198764    0.150174    1.664038    0.031736    1.193334    1.351188   \n",
      "\n",
      "   feature298  feature299  \n",
      "0   -0.388969    0.991831  \n",
      "1   -3.177874   -1.141148  \n",
      "2   -1.466035    0.911414  \n",
      "3   -0.208451   -1.183845  \n",
      "4   -1.815744   -0.674143  \n",
      "\n",
      "[5 rows x 301 columns]\n"
     ]
    }
   ],
   "source": [
    "print(embedding_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN = KNeighborsClassifier(n_neighbors = 3)\n",
    "NB = GaussianNB()\n",
    "SVM = svm.SVC()\n",
    "LR = linear_model.LogisticRegression(C = 0.01)\n",
    "RF = ensemble.RandomForestClassifier(n_estimators = 5, n_jobs = -1)\n",
    "GBT = ensemble.GradientBoostingClassifier(n_estimators = 5, max_leaf_nodes = 3, max_depth = 3)\n",
    "\n",
    "clfs = [('LR',LR),('SVM',SVM),('NB',NB),('KNN',KNN),('GBT',GBT),('RF',LR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_seed =100\n",
    "n_fold =5\n",
    "n_run =2\n",
    "n_proportion = 1\n",
    "scoreDF  = ml.kfoldCV(sc, pairs, classes, embedding_df, clfs, n_run, n_fold, n_proportion, n_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>average_precision</th>\n",
       "      <th>f1</th>\n",
       "      <th>fold</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.708657</td>\n",
       "      <td>0.777289</td>\n",
       "      <td>0.708382</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.709047</td>\n",
       "      <td>0.707720</td>\n",
       "      <td>0.778547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>0.649174</td>\n",
       "      <td>0.681024</td>\n",
       "      <td>0.633555</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.663073</td>\n",
       "      <td>0.606556</td>\n",
       "      <td>0.697719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.837881</td>\n",
       "      <td>0.912630</td>\n",
       "      <td>0.842488</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.819206</td>\n",
       "      <td>0.867133</td>\n",
       "      <td>0.919453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     accuracy  average_precision        f1  fold  precision  \\\n",
       "method                                                                        \n",
       "Logistic Regression  0.708657           0.777289  0.708382   2.0   0.709047   \n",
       "Naive Bayes          0.649174           0.681024  0.633555   2.0   0.663073   \n",
       "Random Forest        0.837881           0.912630  0.842488   2.0   0.819206   \n",
       "\n",
       "                       recall   roc_auc  \n",
       "method                                   \n",
       "Logistic Regression  0.707720  0.778547  \n",
       "Naive Bayes          0.606556  0.697719  \n",
       "Random Forest        0.867133  0.919453  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoreDF .groupby(['method','run']).mean().groupby('method').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoreDF .to_csv('Results/results/traditional/DB5/DBv5_TCV_run_cbow.csv',sep=',', index=False)"
   ]
  }
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