ember2018_Default RF,KNN,MLPC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = \"/data/acp18fta\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import ember\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import altair as alt\n",
    "import lightgbm as lgb\n",
    "#import matplotlib.pylab as plt\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "#_ = alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: EMBER feature version 2 were computed using lief version 0.9.0-\n",
      "WARNING:   lief version 0.10.1- found instead. There may be slight inconsistencies\n",
      "WARNING:   in the feature calculations.\n"
     ]
    }
   ],
   "source": [
    "emberdf = ember.read_metadata(data_dir)\n",
    "X_train, y_train, X_test, y_test = ember.read_vectorized_features(data_dir)\n",
    "lgbm_model = lgb.Booster(model_file=os.path.join(data_dir, \"ember_model_2018.txt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
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       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
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       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http:https://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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"
     },
     "metadata": {
      "jupyter-vega": "#ecacf93a-8b24-40bc-a5c0-16f596001c47"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotdf = emberdf.copy()\n",
    "gbdf = plotdf.groupby([\"label\", \"subset\"]).count().reset_index()\n",
    "alt.Chart(gbdf).mark_bar().encode(\n",
    "    alt.X('subset:O', axis=alt.Axis(title='Subset')),\n",
    "    alt.Y('sum(sha256):Q', axis=alt.Axis(title='Number of samples')),\n",
    "    alt.Color('label:N', scale=alt.Scale(range=[\"#00b300\", \"#3333ff\", \"#ff3333\"]), legend=alt.Legend(values=[\"unlabeled\", \"benign\", \"malicious\"]))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP Accuracy: 0.499995\n",
      "MLP ROC_AUC: 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/acp18fta/.conda/envs/my-python-env/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/acp18fta/.conda/envs/my-python-env/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP classification report :               precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.00      0.00      0.00         2\n",
      "         0.0       0.00      0.00      0.00         0\n",
      "         1.0       1.00      0.50      0.67    199998\n",
      "\n",
      "    accuracy                           0.50    200000\n",
      "   macro avg       0.33      0.17      0.22    200000\n",
      "weighted avg       1.00      0.50      0.67    200000\n",
      "\n",
      "CPU times: user 18h 36min 5s, sys: 22h 26min 47s, total: 1d 17h 2min 52s\n",
      "Wall time: 2h 16min 57s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Without Hyper Parameters Tuning\n",
    "#7-7,MLP,Multi-layer Perceptron classifier\n",
    "#importing module\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "#making the instance\n",
    "model = MLPClassifier(hidden_layer_sizes=(100),max_iter=100)\n",
    "#learning\n",
    "model.fit(X_train,y_train)\n",
    "#Prediction\n",
    "prediction= model.predict(X_test)\n",
    "#importing the metrics module\n",
    "from sklearn import metrics\n",
    "#evaluation(Accuracy)\n",
    "print(\"MLP Accuracy:\",metrics.accuracy_score(prediction,y_test))\n",
    "#evaluation of AUC ROC SCORE \n",
    "print(\"MLP ROC_AUC:\",metrics.roc_auc_score(y_test,prediction))\n",
    "#evaluation(Classification_report)\n",
    "from sklearn.metrics import classification_report\n",
    "print(\"MLP classification report :\",metrics.classification_report(prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF Accuracy: 0.897375\n",
      "RF ROC_AUC: 0.9027022801000001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/acp18fta/.conda/envs/my-python-env/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF Classification report :               precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.00      0.00      0.00     12120\n",
      "         0.0       0.90      0.95      0.93     94873\n",
      "         1.0       0.89      0.96      0.93     93007\n",
      "\n",
      "    accuracy                           0.90    200000\n",
      "   macro avg       0.60      0.64      0.62    200000\n",
      "weighted avg       0.84      0.90      0.87    200000\n",
      "\n",
      "CPU times: user 56min 17s, sys: 5.01 s, total: 56min 22s\n",
      "Wall time: 57min 52s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Without Hyper Parameters Tuning\n",
    "#4-6,RF\n",
    "#importing module\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#making the instance\n",
    "model = RandomForestClassifier()\n",
    "#learning\n",
    "model.fit(X_train,y_train)\n",
    "#Prediction\n",
    "prediction=model.predict(X_test)\n",
    "#importing the metrics module\n",
    "from sklearn import metrics\n",
    "#evaluation(Accuracy)\n",
    "print(\"RF Accuracy:\",metrics.accuracy_score(prediction,y_test))\n",
    "#evaluation of AUC ROC SCORE \n",
    "print(\"RF ROC_AUC:\",metrics.roc_auc_score(y_test,prediction))\n",
    "#evaluation(Classification_report)\n",
    "from sklearn.metrics import classification_report\n",
    "print(\"RF Classification report :\" ,metrics.classification_report(prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Accuracy: 0.694065\n",
      "KNN ROC_AUC_SCORE : 0.7453493832\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/acp18fta/.conda/envs/my-python-env/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.00      0.00      0.00     32979\n",
      "         0.0       0.71      0.80      0.76     88881\n",
      "         1.0       0.67      0.86      0.76     78140\n",
      "\n",
      "    accuracy                           0.69    200000\n",
      "   macro avg       0.46      0.56      0.50    200000\n",
      "weighted avg       0.58      0.69      0.63    200000\n",
      "\n",
      "CPU times: user 3h 12min 58s, sys: 19 s, total: 3h 13min 17s\n",
      "Wall time: 3h 14min 59s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Without Hyper Parameters Tuning\n",
    "#5-6,kNearestNeighbors\n",
    "#importing module\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "#making the instance\n",
    "model = KNeighborsClassifier()\n",
    "#learning\n",
    "model.fit(X_train,y_train)\n",
    "#Prediction\n",
    "prediction=model.predict(X_test)\n",
    "#importing the metrics module\n",
    "from sklearn import metrics\n",
    "#evaluation(Accuracy)\n",
    "print(\"KNN Accuracy:\",metrics.accuracy_score(prediction,y_test))\n",
    "#evaluation of AUC ROC SCORE \n",
    "print(\"KNN ROC_AUC_SCORE :\",metrics.roc_auc_score(y_test,prediction))\n",
    "from sklearn.metrics import classification_report\n",
    "print(\"KNN Classification report:\",metrics.classification_report(prediction, y_test))"
   ]
  }
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