SDAE_Code_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "XL=pd.read_csv('9_510_L_resample.csv')\n",
    "XU_train=pd.read_csv('9_510_U_resample.csv')# No resampling done in the unlabeled set, It just denotes the pair of L and U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source.IP</th>\n",
       "      <th>Source.Port</th>\n",
       "      <th>Destination.IP</th>\n",
       "      <th>Destination.Port</th>\n",
       "      <th>Protocol</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Flow.Duration</th>\n",
       "      <th>Total.Fwd.Packets</th>\n",
       "      <th>Total.Backward.Packets</th>\n",
       "      <th>Fwd.Packet.Length.Max</th>\n",
       "      <th>...</th>\n",
       "      <th>act_data_pkt_fwd</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>Active.Mean</th>\n",
       "      <th>Active.Std</th>\n",
       "      <th>Active.Max</th>\n",
       "      <th>Active.Min</th>\n",
       "      <th>Idle.Mean</th>\n",
       "      <th>Idle.Std</th>\n",
       "      <th>Idle.Max</th>\n",
       "      <th>Idle.Min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>80</td>\n",
       "      <td>180881369</td>\n",
       "      <td>52177</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494322e+09</td>\n",
       "      <td>483</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>55151</td>\n",
       "      <td>180881158</td>\n",
       "      <td>3128</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494314e+09</td>\n",
       "      <td>22692370</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>739</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>180881351</td>\n",
       "      <td>34795</td>\n",
       "      <td>3193625907</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494314e+09</td>\n",
       "      <td>245206</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3017</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3232243719</td>\n",
       "      <td>51563</td>\n",
       "      <td>180881160</td>\n",
       "      <td>3128</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494318e+09</td>\n",
       "      <td>10188</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Source.IP  Source.Port  Destination.IP  Destination.Port  Protocol  \\\n",
       "0   180881161         3128      3232243823             51203         6   \n",
       "1  3193625889           80       180881369             52177         6   \n",
       "2  3232246019        55151       180881158              3128         6   \n",
       "3   180881351        34795      3193625907                80         6   \n",
       "4  3232243719        51563       180881160              3128         6   \n",
       "\n",
       "      Timestamp  Flow.Duration  Total.Fwd.Packets  Total.Backward.Packets  \\\n",
       "0  1.494314e+09            106                  2                       0   \n",
       "1  1.494322e+09            483                  1                       2   \n",
       "2  1.494314e+09       22692370                  5                       3   \n",
       "3  1.494314e+09         245206                  3                       6   \n",
       "4  1.494318e+09          10188                  1                       1   \n",
       "\n",
       "   Fwd.Packet.Length.Max  ...  act_data_pkt_fwd  min_seg_size_forward  \\\n",
       "0                     55  ...                 1                    20   \n",
       "1                      0  ...                 0                    32   \n",
       "2                    739  ...                 4                    20   \n",
       "3                   3017  ...                 1                    32   \n",
       "4                      6  ...                 0                    20   \n",
       "\n",
       "   Active.Mean  Active.Std  Active.Max  Active.Min  Idle.Mean  Idle.Std  \\\n",
       "0          0.0         0.0         0.0         0.0        0.0       0.0   \n",
       "1          0.0         0.0         0.0         0.0        0.0       0.0   \n",
       "2          0.0         0.0         0.0         0.0        0.0       0.0   \n",
       "3          0.0         0.0         0.0         0.0        0.0       0.0   \n",
       "4          0.0         0.0         0.0         0.0        0.0       0.0   \n",
       "\n",
       "   Idle.Max  Idle.Min  \n",
       "0       0.0       0.0  \n",
       "1       0.0       0.0  \n",
       "2       0.0       0.0  \n",
       "3       0.0       0.0  \n",
       "4       0.0       0.0  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "XU_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Destination.IP</th>\n",
       "      <th>Destination.Port</th>\n",
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       "      <th>Total.Backward.Packets</th>\n",
       "      <th>Fwd.Packet.Length.Max</th>\n",
       "      <th>...</th>\n",
       "      <th>min_seg_size_forward</th>\n",
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       "      <th>Active.Std</th>\n",
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       "      <th>Active.Min</th>\n",
       "      <th>Idle.Mean</th>\n",
       "      <th>Idle.Std</th>\n",
       "      <th>Idle.Max</th>\n",
       "      <th>Idle.Min</th>\n",
       "      <th>ProtocolName</th>\n",
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       "      <td>8</td>\n",
       "      <td>284</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AMAZON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>180881346</td>\n",
       "      <td>42382</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>AMAZON</td>\n",
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       "    <tr>\n",
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       "      <td>180881370</td>\n",
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       "      <td>915964343</td>\n",
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       "      <td>1.494318e+09</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AMAZON</td>\n",
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      "text/plain": [
       "    Source.IP  Source.Port  Destination.IP  Destination.Port  Protocol  \\\n",
       "0   180881370        51010       915964343                80         6   \n",
       "1  3232256328        57128       180881160              3128         6   \n",
       "2   180881370        41620       872483344                80         6   \n",
       "3   180881346        42382       872820700               443         6   \n",
       "4   180881370        47404       915964343                80         6   \n",
       "\n",
       "      Timestamp  Flow.Duration  Total.Fwd.Packets  Total.Backward.Packets  \\\n",
       "0  1.494319e+09            233                  1                       1   \n",
       "1  1.494322e+09           1778                  3                       0   \n",
       "2  1.494318e+09         743839                 11                       8   \n",
       "3  1.494322e+09        9683828                  8                       9   \n",
       "4  1.494318e+09             24                  1                       1   \n",
       "\n",
       "   Fwd.Packet.Length.Max  ...  min_seg_size_forward  Active.Mean  Active.Std  \\\n",
       "0                      0  ...                    32          0.0         0.0   \n",
       "1                      6  ...                    20          0.0         0.0   \n",
       "2                    284  ...                    32          0.0         0.0   \n",
       "3                   1471  ...                    32          0.0         0.0   \n",
       "4                      0  ...                    32          0.0         0.0   \n",
       "\n",
       "   Active.Max  Active.Min  Idle.Mean  Idle.Std  Idle.Max  Idle.Min  \\\n",
       "0         0.0         0.0        0.0       0.0       0.0       0.0   \n",
       "1         0.0         0.0        0.0       0.0       0.0       0.0   \n",
       "2         0.0         0.0        0.0       0.0       0.0       0.0   \n",
       "3         0.0         0.0        0.0       0.0       0.0       0.0   \n",
       "4         0.0         0.0        0.0       0.0       0.0       0.0   \n",
       "\n",
       "   ProtocolName  \n",
       "0        AMAZON  \n",
       "1        AMAZON  \n",
       "2        AMAZON  \n",
       "3        AMAZON  \n",
       "4        AMAZON  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XL.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=XL.groupby('ProtocolName')['ProtocolName'].count().sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProtocolName\n",
       "YOUTUBE           6000\n",
       "SSL               6000\n",
       "GOOGLE            6000\n",
       "HTTP              6000\n",
       "HTTP_CONNECT      6000\n",
       "HTTP_PROXY        6000\n",
       "AMAZON            6000\n",
       "TEAMVIEWER        5000\n",
       "WIKIPEDIA         5000\n",
       "GOOGLE_MAPS       5000\n",
       "WINDOWS_UPDATE    5000\n",
       "GMAIL             5000\n",
       "FTP_DATA          5000\n",
       "FACEBOOK          5000\n",
       "EBAY              5000\n",
       "EASYTAXI          5000\n",
       "DROPBOX           5000\n",
       "DNS               5000\n",
       "CONTENT_FLASH     5000\n",
       "CLOUDFLARE        5000\n",
       "CITRIX_ONLINE     5000\n",
       "APPLE_ITUNES      5000\n",
       "APPLE_ICLOUD      5000\n",
       "APPLE             5000\n",
       "H323              5000\n",
       "WHATSAPP          5000\n",
       "SSL_NO_CERT       5000\n",
       "HTTP_DOWNLOAD     5000\n",
       "TOR               5000\n",
       "SPOTIFY           5000\n",
       "SKYPE             5000\n",
       "OFFICE_365        5000\n",
       "NTP               5000\n",
       "NETFLIX           5000\n",
       "MS_ONE_DRIVE      5000\n",
       "MSSQL             5000\n",
       "MSN               5000\n",
       "MQTT              5000\n",
       "MICROSOFT         5000\n",
       "LASTFM            5000\n",
       "IP_ICMP           5000\n",
       "YAHOO             5000\n",
       "TWITTER           5000\n",
       "INSTAGRAM         5000\n",
       "Name: ProtocolName, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a   # see resampled instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Function mask_noise\n",
    "\n",
    "Input: numpy array , fraction_delete(between 0 and 1)\n",
    "\n",
    "Removes a fraction_delete fraction of the input array X randomly\n",
    "\n",
    "Output: numpy array after deletions\n",
    "\n",
    "'''\n",
    "def mask_noise(X,fraction_delete=0.2):\n",
    "    mask=np.random.rand(np.shape(X)[0],np.shape(X)[1])\n",
    "    mask=1*(mask>fraction_delete)\n",
    "    return mask*X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Function Gaussian_noise\n",
    "\n",
    "Input: numpy array , sigma\n",
    "\n",
    "adds noise distributed normally with a stddev sigma \n",
    "\n",
    "Output: numpy array after adding noise\n",
    "\n",
    "'''\n",
    "def Gaussian_noise(X,sigma=0.5):\n",
    "    noise = np.random.normal(0,sigma,np.shape(X))\n",
    "    return noise+X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "import keras.backend as K\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Sparcity Autoencoder's Sparcity Activity Regularization\n",
    "\n",
    "'''\n",
    "def kl_divergence(rho, rho_hat):\n",
    "    return rho * tf.math.log(rho) - rho * tf.math.log(rho_hat+1e-12) + (1 - rho) * tf.math.log(1 - rho) - (1 - rho) * tf.math.log(1 - rho_hat+1e-12)\n",
    "\n",
    "class SparseActivityRegularizer(tf.keras.regularizers.Regularizer):\n",
    "\n",
    "    def __init__(self, p=0.1, sparsityBeta=3):\n",
    "        self.p = p\n",
    "        self.sparsityBeta = sparsityBeta\n",
    "\n",
    "    def __call__(self, x):\n",
    "        regularization = 0            \n",
    "\n",
    "        p_hat = K.mean(x, axis=0)\n",
    "        regularization += self.sparsityBeta * K.sum(kl_divergence(self.p, p_hat))\n",
    "\n",
    "        return regularization\n",
    "\n",
    "    def get_config(self):\n",
    "        return {\"name\": self.__class__.__name__} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import BatchNormalization\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import Adagrad\n",
    "from keras.optimizers import SGD\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "yL_train=XL.iloc[:,-1]\n",
    "XL_train=XL.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "yL_train=pd.get_dummies(yL_train)#One-hot_encoding\n",
    "\n",
    "# Split into train,test and val\n",
    "\n",
    "#randomly split into train(80%) and val(20%)\n",
    "XL_train,XL_val,yL_train,yL_val=train_test_split(XL_train,yL_train,random_state=0,test_size=0.2)\n",
    "\n",
    "#randomly split val into val(10%) and test(10%)\n",
    "XL_val,XL_test,yL_val,yL_test=train_test_split(XL_val,yL_val,random_state=0,test_size=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Reversing the one hot encoding for each set to be used for ML methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "yL_train2=yL_train[yL_train==1].stack().reset_index().drop(0,1)['level_1'] # reversing the one hot encoding for each set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "yL_val2=yL_val[yL_val==1].stack().reset_index().drop(0,1)['level_1']# reversing the one hot encoding for each set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "yL_test2=yL_test[yL_test==1].stack().reset_index().drop(0,1)['level_1']# reversing the one hot encoding for each set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "#scaling and normalization\n",
    "scaler=StandardScaler()\n",
    "XU_train=scaler.fit_transform(XU_train)\n",
    "XL_train=scaler.fit_transform(XL_train)\n",
    "XL_val=scaler.transform(XL_val)\n",
    "XL_test=scaler.transform(XL_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Training the SDAE\n",
    "#### Training the Encoder parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras_sequential_ascii import keras2ascii\n",
    "from tensorflow.keras import regularizers\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Function SDAE\n",
    "Inputs: numpy array X, list layers,list activations,float rho,int n_epochs,string denoise,float fraction_delete\n",
    "\n",
    "denoise is the mode of adding noise\n",
    "rho is the sparcity constraint parameter\n",
    "layers is a list of no. of hidden nodes in each hidden layer\n",
    "activations is a list of activations used in each encoding layer\n",
    "\n",
    "trains each autoencoder layer and outputs all the encoder-layer models.\n",
    "\n",
    "Output: a list of keras models\n",
    "\n",
    "'''\n",
    "def SDAE(X,layers,activations,rho,n_epochs,denoise,fraction_delete=0.2):\n",
    "    layers.insert(0,len(X[0]))\n",
    "    #layers.insert(0,len(X_train.iloc[0]))\n",
    "    ec=[]\n",
    "    for i in range(len(layers)-1):\n",
    "        encoding_dim = layers[i+1]\n",
    "\n",
    "    # this is our input placeholder\n",
    "        input_ = Input(shape=(layers[i],))\n",
    "    # define regulariser\n",
    "        if rho[i] != None: \n",
    "            regulariser = SparseActivityRegularizer(rho[i],3)\n",
    "        else:\n",
    "            regulariser=None\n",
    "\n",
    "        # \"encoded\" is the encoded representation of the input\n",
    "        encoded = Dense(encoding_dim, activation=activations[i],bias_initializer='random_normal',kernel_initializer='he_uniform',activity_regularizer=regulariser,name='encoder'+str(i+1))(input_)\n",
    "        \n",
    "        # \"decoded\" is the lossy reconstruction of the input\n",
    "        decoded = Dense(layers[i],activation=None,name='decoder'+str(i+1))(encoded)\n",
    "\n",
    "        # this model maps an input to its reconstruction\n",
    "        autoencoder = Model(input_, decoded)\n",
    "\n",
    "        # this model maps an input to its encoded representation\n",
    "        encoder = Model(input_, encoded)\n",
    "\n",
    "        # create a placeholder for an encoded (32-dimensional) input\n",
    "        encoded_input = Input(shape=(encoding_dim,))\n",
    "\n",
    "        autoencoder.compile(optimizer=RMSprop(learning_rate=0.001,clipnorm=0.1), loss='mse')\n",
    "        if denoise =='Gauss':\n",
    "            X_noisy=Gaussian_noise(X,fraction_delete)\n",
    "        else:\n",
    "            X_noisy=mask_noise(X,fraction_delete)\n",
    "        \n",
    "        print('Training Layer ' + str(i+1)+' : ')\n",
    "        autoencoder.fit(X_noisy,X,\n",
    "                epochs=n_epochs,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(X,X))\n",
    "        ec.append(encoder)\n",
    "        X=encoder.predict(X)\n",
    "    print('All Layers Done!')    \n",
    "    return ec       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Layer 1 : \n",
      "Train on 121392 samples, validate on 121392 samples\n",
      "Epoch 1/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 6.1544 - val_loss: 0.7211\n",
      "Epoch 2/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.6228 - val_loss: 0.5587\n",
      "Epoch 3/20\n",
      "121392/121392 [==============================] - 2s 17us/step - loss: 0.5030 - val_loss: 0.4641\n",
      "Epoch 4/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.4478 - val_loss: 0.4137\n",
      "Epoch 5/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.4126 - val_loss: 0.3772\n",
      "Epoch 6/20\n",
      "121392/121392 [==============================] - 2s 13us/step - loss: 0.3841 - val_loss: 0.3361\n",
      "Epoch 7/20\n",
      "121392/121392 [==============================] - 2s 13us/step - loss: 0.3614 - val_loss: 0.3300\n",
      "Epoch 8/20\n",
      "121392/121392 [==============================] - 2s 13us/step - loss: 0.3431 - val_loss: 0.3143\n",
      "Epoch 9/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.3307 - val_loss: 0.2926\n",
      "Epoch 10/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.3189 - val_loss: 0.2986\n",
      "Epoch 11/20\n",
      "121392/121392 [==============================] - 2s 13us/step - loss: 0.3071 - val_loss: 0.3055\n",
      "Epoch 12/20\n",
      "121392/121392 [==============================] - 2s 19us/step - loss: 0.2928 - val_loss: 0.2569\n",
      "Epoch 13/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2849 - val_loss: 0.2587\n",
      "Epoch 14/20\n",
      "121392/121392 [==============================] - 2s 12us/step - loss: 0.2775 - val_loss: 0.2467\n",
      "Epoch 15/20\n",
      "121392/121392 [==============================] - 2s 18us/step - loss: 0.2658 - val_loss: 0.2571\n",
      "Epoch 16/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.2549 - val_loss: 0.2442\n",
      "Epoch 17/20\n",
      "121392/121392 [==============================] - 3s 27us/step - loss: 0.2509 - val_loss: 0.2176\n",
      "Epoch 18/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.2403 - val_loss: 0.2258\n",
      "Epoch 19/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2320 - val_loss: 0.2269\n",
      "Epoch 20/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.2287 - val_loss: 0.2085\n",
      "Training Layer 2 : \n",
      "Train on 121392 samples, validate on 121392 samples\n",
      "Epoch 1/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.6479 - val_loss: 0.3749\n",
      "Epoch 2/20\n",
      "121392/121392 [==============================] - 2s 17us/step - loss: 0.2985 - val_loss: 0.3467\n",
      "Epoch 3/20\n",
      "121392/121392 [==============================] - 2s 17us/step - loss: 0.2920 - val_loss: 0.4253\n",
      "Epoch 4/20\n",
      "121392/121392 [==============================] - 3s 21us/step - loss: 0.2743 - val_loss: 0.3632\n",
      "Epoch 5/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.2608 - val_loss: 0.3175\n",
      "Epoch 6/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2524 - val_loss: 0.2931\n",
      "Epoch 7/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2456 - val_loss: 0.3164\n",
      "Epoch 8/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.2391 - val_loss: 0.2940\n",
      "Epoch 9/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2338 - val_loss: 0.2787\n",
      "Epoch 10/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2330 - val_loss: 0.3093\n",
      "Epoch 11/20\n",
      "121392/121392 [==============================] - 2s 16us/step - loss: 0.2258 - val_loss: 0.2846\n",
      "Epoch 12/20\n",
      "121392/121392 [==============================] - 2s 16us/step - loss: 0.2241 - val_loss: 0.3010\n",
      "Epoch 13/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2198 - val_loss: 0.2302\n",
      "Epoch 14/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2160 - val_loss: 0.3140\n",
      "Epoch 15/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.2103 - val_loss: 0.3235\n",
      "Epoch 16/20\n",
      "121392/121392 [==============================] - 2s 14us/step - loss: 0.2106 - val_loss: 0.3082\n",
      "Epoch 17/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2094 - val_loss: 0.3205\n",
      "Epoch 18/20\n",
      "121392/121392 [==============================] - 3s 21us/step - loss: 0.2090 - val_loss: 0.2962\n",
      "Epoch 19/20\n",
      "121392/121392 [==============================] - 2s 19us/step - loss: 0.2050 - val_loss: 0.2250\n",
      "Epoch 20/20\n",
      "121392/121392 [==============================] - 2s 15us/step - loss: 0.2059 - val_loss: 0.2704\n",
      "Training Layer 3 : \n",
      "Train on 121392 samples, validate on 121392 samples\n",
      "Epoch 1/20\n",
      "121392/121392 [==============================] - 4s 32us/step - loss: 3.3800 - val_loss: 2.3793\n",
      "Epoch 2/20\n",
      "121392/121392 [==============================] - 3s 26us/step - loss: 0.7379 - val_loss: 1.1514\n",
      "Epoch 3/20\n",
      "121392/121392 [==============================] - 3s 26us/step - loss: 0.7299 - val_loss: 1.1146\n",
      "Epoch 4/20\n",
      "121392/121392 [==============================] - 4s 31us/step - loss: 0.7143 - val_loss: 1.0029\n",
      "Epoch 5/20\n",
      "121392/121392 [==============================] - 3s 28us/step - loss: 0.7105 - val_loss: 1.0420\n",
      "Epoch 6/20\n",
      "121392/121392 [==============================] - 3s 28us/step - loss: 0.7350 - val_loss: 0.6235\n",
      "Epoch 7/20\n",
      "121392/121392 [==============================] - 3s 27us/step - loss: 0.7153 - val_loss: 0.9101\n",
      "Epoch 8/20\n",
      "121392/121392 [==============================] - 3s 26us/step - loss: 0.6862 - val_loss: 0.8048\n",
      "Epoch 9/20\n",
      "121392/121392 [==============================] - 3s 28us/step - loss: 0.6859 - val_loss: 1.1837\n",
      "Epoch 10/20\n",
      "121392/121392 [==============================] - 3s 28us/step - loss: 0.7042 - val_loss: 0.8120\n",
      "Epoch 11/20\n",
      "121392/121392 [==============================] - 4s 37us/step - loss: 0.7101 - val_loss: 1.1621\n",
      "Epoch 12/20\n",
      "121392/121392 [==============================] - 4s 35us/step - loss: 0.6878 - val_loss: 0.9333\n",
      "Epoch 13/20\n",
      "121392/121392 [==============================] - 7s 54us/step - loss: 0.7012 - val_loss: 1.3063\n",
      "Epoch 14/20\n",
      "121392/121392 [==============================] - 5s 42us/step - loss: 0.7058 - val_loss: 1.1035\n",
      "Epoch 15/20\n",
      "121392/121392 [==============================] - 5s 41us/step - loss: 0.6669 - val_loss: 1.2193\n",
      "Epoch 16/20\n",
      "121392/121392 [==============================] - 3s 28us/step - loss: 0.6852 - val_loss: 0.6485\n",
      "Epoch 17/20\n",
      "121392/121392 [==============================] - 3s 27us/step - loss: 0.6918 - val_loss: 1.0398\n",
      "Epoch 18/20\n",
      "121392/121392 [==============================] - 3s 27us/step - loss: 0.6957 - val_loss: 0.6928\n",
      "Epoch 19/20\n",
      "121392/121392 [==============================] - 4s 30us/step - loss: 0.6966 - val_loss: 0.9915\n",
      "Epoch 20/20\n",
      "121392/121392 [==============================] - 3s 28us/step - loss: 0.6995 - val_loss: 1.1113\n",
      "Training Layer 4 : \n",
      "Train on 121392 samples, validate on 121392 samples\n",
      "Epoch 1/20\n",
      "121392/121392 [==============================] - 3s 24us/step - loss: 0.2063 - val_loss: 0.2770\n",
      "Epoch 2/20\n",
      "121392/121392 [==============================] - 2s 21us/step - loss: 0.1624 - val_loss: 0.2345\n",
      "Epoch 3/20\n",
      "121392/121392 [==============================] - 3s 25us/step - loss: 0.1503 - val_loss: 0.2646\n",
      "Epoch 4/20\n",
      "121392/121392 [==============================] - 3s 23us/step - loss: 0.1569 - val_loss: 0.2288\n",
      "Epoch 5/20\n",
      "121392/121392 [==============================] - 3s 23us/step - loss: 0.1546 - val_loss: 0.3287\n",
      "Epoch 6/20\n",
      "121392/121392 [==============================] - 3s 23us/step - loss: 0.1490 - val_loss: 0.1586\n",
      "Epoch 7/20\n",
      "121392/121392 [==============================] - 2s 20us/step - loss: 0.1548 - val_loss: 0.1850\n",
      "Epoch 8/20\n",
      "121392/121392 [==============================] - 3s 23us/step - loss: 0.1544 - val_loss: 0.1952\n",
      "Epoch 9/20\n",
      "121392/121392 [==============================] - 2s 19us/step - loss: 0.1550 - val_loss: 0.1719\n",
      "Epoch 10/20\n",
      "121392/121392 [==============================] - 3s 22us/step - loss: 0.1524 - val_loss: 0.2295\n",
      "Epoch 11/20\n",
      "121392/121392 [==============================] - 3s 22us/step - loss: 0.1519 - val_loss: 0.2512\n",
      "Epoch 12/20\n",
      "121392/121392 [==============================] - 3s 23us/step - loss: 0.1498 - val_loss: 0.3114\n",
      "Epoch 13/20\n",
      "121392/121392 [==============================] - 2s 19us/step - loss: 0.1583 - val_loss: 0.2140\n",
      "Epoch 14/20\n",
      "121392/121392 [==============================] - 3s 23us/step - loss: 0.1477 - val_loss: 0.3496\n",
      "Epoch 15/20\n",
      "121392/121392 [==============================] - 3s 22us/step - loss: 0.1544 - val_loss: 0.2570\n",
      "Epoch 16/20\n",
      "121392/121392 [==============================] - 3s 28us/step - loss: 0.1477 - val_loss: 0.1227\n",
      "Epoch 17/20\n",
      "121392/121392 [==============================] - 4s 36us/step - loss: 0.1551 - val_loss: 0.3374\n",
      "Epoch 18/20\n",
      "121392/121392 [==============================] - 3s 28us/step - loss: 0.1428 - val_loss: 0.2398\n",
      "Epoch 19/20\n",
      "121392/121392 [==============================] - 3s 23us/step - loss: 0.1469 - val_loss: 0.2125\n",
      "Epoch 20/20\n",
      "121392/121392 [==============================] - 3s 21us/step - loss: 0.1474 - val_loss: 0.2564\n",
      "All Layers Done!\n"
     ]
    }
   ],
   "source": [
    "#Train Sparse Stacked Denoising Autoencoder\n",
    "layers = [100,200,400,50]\n",
    "activations=['relu','relu','relu','relu','relu']\n",
    "rho=[0.09,0.09,0.09,0.09,0.1,0.1]\n",
    "stacked_layers=SDAE(XU_train,layers,activations,rho,20,'mask',0.03)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.Training the SoftMax classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Performs a Forward Pass through the SDAE\n",
    "\n",
    "'''\n",
    "def SDAE_predict(stacked_layers,X):\n",
    "    for layer in stacked_layers:\n",
    "        X1=layer.predict(X)\n",
    "        X=X1\n",
    "    return X1    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#forward pass through SDAE\n",
    "XL_train1=SDAE_predict(stacked_layers,XL_train)\n",
    "XL_val1=SDAE_predict(stacked_layers,XL_val)\n",
    "XL_test1=SDAE_predict(stacked_layers,XL_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Please skip this section to part 6 If only ML methods are to be tested, The following is the DL NN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Classifier separately\n",
    "input1 = Input(shape=(layers[-1],))\n",
    "L1 = Dense(len(yL_train.columns),activation='softmax')(input1)\n",
    "classifier=Model(input1,L1)\n",
    "classifier.compile(optimizer=SGD(learning_rate=0.1,momentum=0.9),loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 181600 samples, validate on 22700 samples\n",
      "Epoch 1/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.9037 - accuracy: 0.2836 - val_loss: 2.6600 - val_accuracy: 0.3363\n",
      "Epoch 2/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.5811 - accuracy: 0.3487 - val_loss: 2.4975 - val_accuracy: 0.3663\n",
      "Epoch 3/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.4668 - accuracy: 0.3683 - val_loss: 2.4186 - val_accuracy: 0.3770\n",
      "Epoch 4/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.4014 - accuracy: 0.3844 - val_loss: 2.3615 - val_accuracy: 0.3993\n",
      "Epoch 5/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.3584 - accuracy: 0.3936 - val_loss: 2.3230 - val_accuracy: 0.4077\n",
      "Epoch 6/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.3265 - accuracy: 0.4013 - val_loss: 2.2940 - val_accuracy: 0.4084\n",
      "Epoch 7/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.3028 - accuracy: 0.4069 - val_loss: 2.2772 - val_accuracy: 0.4164\n",
      "Epoch 8/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.2830 - accuracy: 0.4119 - val_loss: 2.2571 - val_accuracy: 0.4193\n",
      "Epoch 9/50\n",
      "181600/181600 [==============================] - 5s 30us/step - loss: 2.2676 - accuracy: 0.4156 - val_loss: 2.2408 - val_accuracy: 0.4163\n",
      "Epoch 10/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.2548 - accuracy: 0.4176 - val_loss: 2.2313 - val_accuracy: 0.4295\n",
      "Epoch 11/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.2435 - accuracy: 0.4209 - val_loss: 2.2199 - val_accuracy: 0.4215\n",
      "Epoch 12/50\n",
      "181600/181600 [==============================] - 5s 30us/step - loss: 2.2341 - accuracy: 0.4241 - val_loss: 2.2167 - val_accuracy: 0.4248\n",
      "Epoch 13/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.2260 - accuracy: 0.4276 - val_loss: 2.2080 - val_accuracy: 0.4390\n",
      "Epoch 14/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.2186 - accuracy: 0.4297 - val_loss: 2.1967 - val_accuracy: 0.4342\n",
      "Epoch 15/50\n",
      "181600/181600 [==============================] - 5s 30us/step - loss: 2.2123 - accuracy: 0.4318 - val_loss: 2.1910 - val_accuracy: 0.4377\n",
      "Epoch 16/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.2065 - accuracy: 0.4332 - val_loss: 2.1865 - val_accuracy: 0.4413\n",
      "Epoch 17/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.2015 - accuracy: 0.4358 - val_loss: 2.1827 - val_accuracy: 0.4368\n",
      "Epoch 18/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1972 - accuracy: 0.4365 - val_loss: 2.1777 - val_accuracy: 0.4481\n",
      "Epoch 19/50\n",
      "181600/181600 [==============================] - 5s 30us/step - loss: 2.1925 - accuracy: 0.4378 - val_loss: 2.1730 - val_accuracy: 0.4445\n",
      "Epoch 20/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1884 - accuracy: 0.4387 - val_loss: 2.1662 - val_accuracy: 0.4453\n",
      "Epoch 21/50\n",
      "181600/181600 [==============================] - 6s 31us/step - loss: 2.1849 - accuracy: 0.4399 - val_loss: 2.1662 - val_accuracy: 0.4455\n",
      "Epoch 22/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.1820 - accuracy: 0.4404 - val_loss: 2.1655 - val_accuracy: 0.4422\n",
      "Epoch 23/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1792 - accuracy: 0.4415 - val_loss: 2.1613 - val_accuracy: 0.4451\n",
      "Epoch 24/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.1761 - accuracy: 0.4429 - val_loss: 2.1595 - val_accuracy: 0.4486\n",
      "Epoch 25/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1738 - accuracy: 0.4438 - val_loss: 2.1557 - val_accuracy: 0.4503\n",
      "Epoch 26/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.1716 - accuracy: 0.4439 - val_loss: 2.1550 - val_accuracy: 0.4455\n",
      "Epoch 27/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1691 - accuracy: 0.4447 - val_loss: 2.1469 - val_accuracy: 0.4481\n",
      "Epoch 28/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1668 - accuracy: 0.4453 - val_loss: 2.1501 - val_accuracy: 0.4531\n",
      "Epoch 29/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1650 - accuracy: 0.4462 - val_loss: 2.1466 - val_accuracy: 0.4527\n",
      "Epoch 30/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1637 - accuracy: 0.4473 - val_loss: 2.1420 - val_accuracy: 0.4491\n",
      "Epoch 31/50\n",
      "181600/181600 [==============================] - 5s 30us/step - loss: 2.1612 - accuracy: 0.4470 - val_loss: 2.1408 - val_accuracy: 0.4538\n",
      "Epoch 32/50\n",
      "181600/181600 [==============================] - 6s 31us/step - loss: 2.1598 - accuracy: 0.4472 - val_loss: 2.1441 - val_accuracy: 0.4481\n",
      "Epoch 33/50\n",
      "181600/181600 [==============================] - 5s 28us/step - loss: 2.1580 - accuracy: 0.4478 - val_loss: 2.1394 - val_accuracy: 0.4573\n",
      "Epoch 34/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1565 - accuracy: 0.4481 - val_loss: 2.1352 - val_accuracy: 0.4607\n",
      "Epoch 35/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1550 - accuracy: 0.4490 - val_loss: 2.1382 - val_accuracy: 0.4506\n",
      "Epoch 36/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1541 - accuracy: 0.4496 - val_loss: 2.1368 - val_accuracy: 0.4560\n",
      "Epoch 37/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1530 - accuracy: 0.4491 - val_loss: 2.1400 - val_accuracy: 0.4485\n",
      "Epoch 38/50\n",
      "181600/181600 [==============================] - 5s 30us/step - loss: 2.1519 - accuracy: 0.4499 - val_loss: 2.1289 - val_accuracy: 0.4567\n",
      "Epoch 39/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1502 - accuracy: 0.4497 - val_loss: 2.1385 - val_accuracy: 0.4474\n",
      "Epoch 40/50\n",
      "181600/181600 [==============================] - 6s 33us/step - loss: 2.1495 - accuracy: 0.4499 - val_loss: 2.1310 - val_accuracy: 0.4584\n",
      "Epoch 41/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1482 - accuracy: 0.4497 - val_loss: 2.1304 - val_accuracy: 0.4540\n",
      "Epoch 42/50\n",
      "181600/181600 [==============================] - 5s 30us/step - loss: 2.1478 - accuracy: 0.4512 - val_loss: 2.1317 - val_accuracy: 0.4562\n",
      "Epoch 43/50\n",
      "181600/181600 [==============================] - 6s 30us/step - loss: 2.1466 - accuracy: 0.4510 - val_loss: 2.1363 - val_accuracy: 0.4554\n",
      "Epoch 44/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1457 - accuracy: 0.4516 - val_loss: 2.1243 - val_accuracy: 0.4540\n",
      "Epoch 45/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1446 - accuracy: 0.4514 - val_loss: 2.1312 - val_accuracy: 0.4532\n",
      "Epoch 46/50\n",
      "181600/181600 [==============================] - 6s 33us/step - loss: 2.1443 - accuracy: 0.4517 - val_loss: 2.1305 - val_accuracy: 0.4590\n",
      "Epoch 47/50\n",
      "181600/181600 [==============================] - 6s 31us/step - loss: 2.1432 - accuracy: 0.4524 - val_loss: 2.1244 - val_accuracy: 0.4574\n",
      "Epoch 48/50\n",
      "181600/181600 [==============================] - 5s 29us/step - loss: 2.1424 - accuracy: 0.4522 - val_loss: 2.1215 - val_accuracy: 0.4613\n",
      "Epoch 49/50\n",
      "181600/181600 [==============================] - 6s 32us/step - loss: 2.1418 - accuracy: 0.4523 - val_loss: 2.1226 - val_accuracy: 0.4562\n",
      "Epoch 50/50\n",
      "181600/181600 [==============================] - 6s 31us/step - loss: 2.1409 - accuracy: 0.4527 - val_loss: 2.1233 - val_accuracy: 0.4605\n"
     ]
    }
   ],
   "source": [
    "history=classifier.fit(XL_train1,yL_train,\n",
    "                epochs=50,\n",
    "                #batch_size=256, #default batch_size=32\n",
    "                shuffle=True,\n",
    "                validation_data=(XL_val1, yL_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18f00856128>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Fine Tuning\n",
    "#### Fine Tuning Involves removing the sparsity regularizer from all the layers , adding dropout etc and performing backpropagation to improve the overall model accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import regularizers\n",
    "from keras.models import load_model\n",
    "import keras\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\engine\\saving.py:341: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "# change regularisation settings\n",
    "a=[]\n",
    "for i in range(len(stacked_layers)):\n",
    "    encoder=stacked_layers[i]\n",
    "    encoder.layers[-1].kernel_regularizer=None #set or reset weight regularizer \n",
    "    encoder.layers[-1].activity_regularizer=None #remove Sparse activity regularizer\n",
    "    encoder.save('temp.h5')\n",
    "    encoder=load_model('temp.h5')\n",
    "    print(encoder.losses)\n",
    "    a.append(encoder)\n",
    "    \n",
    "classifier.save('temp.h5')\n",
    "classifier=load_model('temp.h5')\n",
    "print(classifier.losses)\n",
    "a.append(classifier)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add dropout till n-2 layers\n",
    "from keras.models import Sequential\n",
    "model=Sequential()\n",
    "for i in a:\n",
    "    model.add(i)\n",
    "    if i != classifier and i != encoder:\n",
    "        model.add(Dropout(0.02))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('temp.h5')\n",
    "model=load_model('temp.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=RMSprop(learning_rate=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 181600 samples, validate on 22700 samples\n",
      "Epoch 1/300\n",
      "102144/181600 [===============>..............] - ETA: 3s - loss: 2.0649 - accuracy: 0.4572"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-53-e517d5a3adb8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m                 \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                 \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m                 validation_data=(XL_val, yL_val))\n\u001b[0m",
      "\u001b[1;32mc:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[0;32m   1240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1241\u001b[0m     def evaluate(self,\n",
      "\u001b[1;32mc:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[0;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 196\u001b[1;33m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   3725\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3726\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3727\u001b[1;33m     \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3729\u001b[0m     \u001b[1;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1549\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1550\u001b[0m     \"\"\"\n\u001b[1;32m-> 1551\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1552\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1553\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1589\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[0;32m   1590\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[1;32m-> 1591\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1593\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1690\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1691\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[1;32m-> 1692\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[0;32m   1693\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[0;32m   1694\u001b[0m         \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    543\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"executor_type\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"config_proto\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    546\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32mc:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow_core\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[0;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m                                                num_outputs)\n\u001b[0m\u001b[0;32m     62\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#fine tune model runs for no. of epochs set below\n",
    "history=model.fit(XL_train,yL_train,\n",
    "                epochs=300,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(XL_val, yL_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22700/22700 [==============================] - 1s 33us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.6652489478997723, 0.5479295253753662]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(XL_test,yL_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eea0fad278>]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualise convergence with no. of epochs\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Results\n",
    "#### Below are the results obtained on our NN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.2913923e-14, 3.0351409e-16, 3.5164938e-21, ..., 1.1380052e-18,\n",
       "        1.9137769e-11, 4.1763305e-06],\n",
       "       [4.9162604e-04, 9.3076178e-09, 4.8519864e-12, ..., 3.2606172e-07,\n",
       "        3.5962557e-07, 1.3435394e-07],\n",
       "       [7.1954773e-08, 4.8477481e-21, 7.7184126e-23, ..., 5.1185622e-10,\n",
       "        1.1683595e-08, 1.3112775e-10],\n",
       "       ...,\n",
       "       [1.8238999e-11, 9.9996948e-01, 4.2627728e-09, ..., 7.0772365e-13,\n",
       "        2.0799262e-11, 1.6600808e-08],\n",
       "       [6.2613909e-10, 7.3030949e-11, 3.5931952e-17, ..., 7.5679242e-13,\n",
       "        5.4761886e-06, 2.3616825e-12],\n",
       "       [4.9030415e-11, 6.1379789e-15, 3.0997163e-10, ..., 1.5042368e-11,\n",
       "        2.5878926e-12, 1.0793907e-05]], dtype=float32)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(XL_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1=y_pred.argmax(axis=1)\n",
    "y_pred1=np.append(y_pred1,np.arange(42))\n",
    "y_pred1=pd.get_dummies(y_pred1,columns=yL_train.columns)\n",
    "y_pred1.columns=yL_test.columns\n",
    "y_pred1=y_pred1.iloc[:-42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AMAZON            600\n",
       "APPLE             496\n",
       "APPLE_ICLOUD      477\n",
       "APPLE_ITUNES      517\n",
       "CITRIX_ONLINE     477\n",
       "CLOUDFLARE        472\n",
       "CONTENT_FLASH     521\n",
       "DNS               481\n",
       "DROPBOX           481\n",
       "EASYTAXI          528\n",
       "EBAY              513\n",
       "FACEBOOK          523\n",
       "FTP_DATA          483\n",
       "GMAIL             502\n",
       "GOOGLE            623\n",
       "GOOGLE_MAPS       476\n",
       "H323              486\n",
       "HTTP              602\n",
       "HTTP_CONNECT      596\n",
       "HTTP_DOWNLOAD     495\n",
       "HTTP_PROXY        616\n",
       "INSTAGRAM         485\n",
       "IP_ICMP           467\n",
       "LASTFM            495\n",
       "MICROSOFT         492\n",
       "MQTT              501\n",
       "MSN               513\n",
       "MSSQL             511\n",
       "MS_ONE_DRIVE      551\n",
       "NETFLIX           522\n",
       "NTP               491\n",
       "OFFICE_365        529\n",
       "SKYPE             484\n",
       "SPOTIFY           506\n",
       "SSL               596\n",
       "SSL_NO_CERT       505\n",
       "TEAMVIEWER        496\n",
       "TOR               502\n",
       "TWITTER           501\n",
       "WHATSAPP          496\n",
       "WIKIPEDIA         475\n",
       "WINDOWS_UPDATE    497\n",
       "YAHOO             493\n",
       "YOUTUBE           627\n",
       "dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yL_test.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test1 = yL_test.columns[np.where(yL_test!=0)[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GOOGLE_MAPS', 'MSN', 'CONTENT_FLASH', 'FTP_DATA', 'HTTP_PROXY', 'NTP',\n",
       "       'MICROSOFT', 'LASTFM', 'WINDOWS_UPDATE', 'DROPBOX',\n",
       "       ...\n",
       "       'GOOGLE', 'EASYTAXI', 'INSTAGRAM', 'FTP_DATA', 'MSN', 'GOOGLE_MAPS',\n",
       "       'CONTENT_FLASH', 'APPLE', 'FTP_DATA', 'SKYPE'],\n",
       "      dtype='object', length=22700)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1=y_pred1.columns[np.where(y_pred1!=0)[1]]\n",
    "y_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm=confusion_matrix(y_test1,y_pred1,labels=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_pc=pd.DataFrame((cm.T/np.sum(cm,axis=1)).T,columns=yL_test.columns,index=yL_test.columns)\n",
    "cm_pc=cm_pc.replace(np.nan,0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2520x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(35,25))\n",
    "df_cm=pd.DataFrame(cm,columns=yL_test.columns,index=yL_test.columns)\n",
    "sns.set(font_scale=1.0)\n",
    "sns.heatmap(cm_pc, annot=True,fmt='.1%',cmap=sns.color_palette(\"Reds\",50)) # font size\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy : 0.8905286343612335\n"
     ]
    }
   ],
   "source": [
    "print('Test Accuracy : '+str(accuracy_score(y_test1, y_pred1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weighted F1 Score : 0.8951018519503067\n",
      "Macro F1 Score : 0.8955458003918594\n",
      "Micro F1 Score : 0.8905286343612335\n",
      "F1 Scores : \n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.865272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.945919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ICLOUD</th>\n",
       "      <td>0.989627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.959459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRIX_ONLINE</th>\n",
       "      <td>0.998953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.960905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.993314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNS</th>\n",
       "      <td>0.996891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.950617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EASYTAXI</th>\n",
       "      <td>0.996226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>0.939227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.912603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTP_DATA</th>\n",
       "      <td>0.983707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.651256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.455718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE_MAPS</th>\n",
       "      <td>0.983471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H323</th>\n",
       "      <td>0.997947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.734970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.536713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_DOWNLOAD</th>\n",
       "      <td>0.993976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.599638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTAGRAM</th>\n",
       "      <td>0.985772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_ICMP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASTFM</th>\n",
       "      <td>0.991984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.665208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQTT</th>\n",
       "      <td>0.994048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.898336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSQL</th>\n",
       "      <td>0.999022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ONE_DRIVE</th>\n",
       "      <td>0.991899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETFLIX</th>\n",
       "      <td>0.986767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.948135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.797056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPOTIFY</th>\n",
       "      <td>0.995084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.584354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL_NO_CERT</th>\n",
       "      <td>0.987292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMVIEWER</th>\n",
       "      <td>0.995984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>0.994059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.848138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.961877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>0.962513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.903674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.906130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.560272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                F1-score\n",
       "AMAZON          0.865272\n",
       "APPLE           0.945919\n",
       "APPLE_ICLOUD    0.989627\n",
       "APPLE_ITUNES    0.959459\n",
       "CITRIX_ONLINE   0.998953\n",
       "CLOUDFLARE      0.960905\n",
       "CONTENT_FLASH   0.993314\n",
       "DNS             0.996891\n",
       "DROPBOX         0.950617\n",
       "EASYTAXI        0.996226\n",
       "EBAY            0.939227\n",
       "FACEBOOK        0.912603\n",
       "FTP_DATA        0.983707\n",
       "GMAIL           0.651256\n",
       "GOOGLE          0.455718\n",
       "GOOGLE_MAPS     0.983471\n",
       "H323            0.997947\n",
       "HTTP            0.734970\n",
       "HTTP_CONNECT    0.536713\n",
       "HTTP_DOWNLOAD   0.993976\n",
       "HTTP_PROXY      0.599638\n",
       "INSTAGRAM       0.985772\n",
       "IP_ICMP         1.000000\n",
       "LASTFM          0.991984\n",
       "MICROSOFT       0.665208\n",
       "MQTT            0.994048\n",
       "MSN             0.898336\n",
       "MSSQL           0.999022\n",
       "MS_ONE_DRIVE    0.991899\n",
       "NETFLIX         0.986767\n",
       "NTP             1.000000\n",
       "OFFICE_365      0.948135\n",
       "SKYPE           0.797056\n",
       "SPOTIFY         0.995084\n",
       "SSL             0.584354\n",
       "SSL_NO_CERT     0.987292\n",
       "TEAMVIEWER      0.995984\n",
       "TOR             0.994059\n",
       "TWITTER         0.848138\n",
       "WHATSAPP        0.961877\n",
       "WIKIPEDIA       0.962513\n",
       "WINDOWS_UPDATE  0.903674\n",
       "YAHOO           0.906130\n",
       "YOUTUBE         0.560272"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('weighted F1 Score : ' + str(f1_score(y_pred1,y_test1,average = 'weighted')))\n",
    "print('Macro F1 Score : ' + str(f1_score(y_pred1,y_test1,average = 'macro')))\n",
    "print('Micro F1 Score : ' + str(f1_score(y_pred1,y_test1,average = 'micro')))\n",
    "print('F1 Scores : ' )\n",
    "pd.DataFrame(f1_score(y_pred1,y_test1,labels=yL_test.columns,average = None),columns=['F1-score'],index=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(yL_train.columns)# No. of distinct labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro Presicion : 0.8917709309544887\n",
      "Micro Presicion : 0.8905286343612335\n",
      "Weighted Presicion : 0.883588131048132\n",
      "Precision Scores : \n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision-score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.861667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.969758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ICLOUD</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.961315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRIX_ONLINE</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.989407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.998081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNS</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.960499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EASYTAXI</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>0.994152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.948375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTP_DATA</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.645418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.425361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE_MAPS</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H323</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.515101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_DOWNLOAD</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.537338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTAGRAM</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_ICMP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASTFM</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.617886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQTT</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSQL</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ONE_DRIVE</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETFLIX</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.984877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.783058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPOTIFY</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.520134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL_NO_CERT</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMVIEWER</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.886228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.991935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.915493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.959432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Precision-score\n",
       "AMAZON                 0.861667\n",
       "APPLE                  0.969758\n",
       "APPLE_ICLOUD           1.000000\n",
       "APPLE_ITUNES           0.961315\n",
       "CITRIX_ONLINE          1.000000\n",
       "CLOUDFLARE             0.989407\n",
       "CONTENT_FLASH          0.998081\n",
       "DNS                    1.000000\n",
       "DROPBOX                0.960499\n",
       "EASYTAXI               1.000000\n",
       "EBAY                   0.994152\n",
       "FACEBOOK               0.948375\n",
       "FTP_DATA               1.000000\n",
       "GMAIL                  0.645418\n",
       "GOOGLE                 0.425361\n",
       "GOOGLE_MAPS            1.000000\n",
       "H323                   1.000000\n",
       "HTTP                   0.720930\n",
       "HTTP_CONNECT           0.515101\n",
       "HTTP_DOWNLOAD          1.000000\n",
       "HTTP_PROXY             0.537338\n",
       "INSTAGRAM              1.000000\n",
       "IP_ICMP                1.000000\n",
       "LASTFM                 1.000000\n",
       "MICROSOFT              0.617886\n",
       "MQTT                   1.000000\n",
       "MSN                    0.947368\n",
       "MSSQL                  1.000000\n",
       "MS_ONE_DRIVE           1.000000\n",
       "NETFLIX                1.000000\n",
       "NTP                    1.000000\n",
       "OFFICE_365             0.984877\n",
       "SKYPE                  0.783058\n",
       "SPOTIFY                1.000000\n",
       "SSL                    0.520134\n",
       "SSL_NO_CERT            1.000000\n",
       "TEAMVIEWER             1.000000\n",
       "TOR                    1.000000\n",
       "TWITTER                0.886228\n",
       "WHATSAPP               0.991935\n",
       "WIKIPEDIA              1.000000\n",
       "WINDOWS_UPDATE         0.915493\n",
       "YAHOO                  0.959432\n",
       "YOUTUBE                0.526316"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Macro Presicion : '+str(precision_score(y_test1, y_pred1,average=\"macro\")))\n",
    "print('Micro Presicion : '+str(precision_score(y_test1, y_pred1,average=\"micro\")))\n",
    "print('Weighted Presicion : '+str(precision_score(y_test1, y_pred1,average=\"weighted\")))\n",
    "print('Precision Scores : ' )\n",
    "pd.DataFrame(precision_score(y_pred1,y_test1,labels=yL_test.columns,average = None),columns=['Precision-score'],index=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro Recall : 0.9013665648385332\n",
      "Micro Recall : 0.8905286343612335\n",
      "Weighted Recall : 0.8905286343612335\n",
      "Recall Scores : \n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>recall-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.868908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.923225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ICLOUD</th>\n",
       "      <td>0.979466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.957611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRIX_ONLINE</th>\n",
       "      <td>0.997908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.934000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.988593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNS</th>\n",
       "      <td>0.993802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.940937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EASYTAXI</th>\n",
       "      <td>0.992481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>0.890052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.879433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTP_DATA</th>\n",
       "      <td>0.967936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.657201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.490741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE_MAPS</th>\n",
       "      <td>0.967480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H323</th>\n",
       "      <td>0.995902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.749568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.560219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_DOWNLOAD</th>\n",
       "      <td>0.988024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.678279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTAGRAM</th>\n",
       "      <td>0.971944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_ICMP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASTFM</th>\n",
       "      <td>0.984095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.720379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQTT</th>\n",
       "      <td>0.988166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.854130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSQL</th>\n",
       "      <td>0.998047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ONE_DRIVE</th>\n",
       "      <td>0.983929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETFLIX</th>\n",
       "      <td>0.973881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.914035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.811563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPOTIFY</th>\n",
       "      <td>0.990215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL_NO_CERT</th>\n",
       "      <td>0.974903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMVIEWER</th>\n",
       "      <td>0.992000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>0.988189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.813187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.933586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>0.927734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.892157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.858439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.598911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                recall-score\n",
       "AMAZON              0.868908\n",
       "APPLE               0.923225\n",
       "APPLE_ICLOUD        0.979466\n",
       "APPLE_ITUNES        0.957611\n",
       "CITRIX_ONLINE       0.997908\n",
       "CLOUDFLARE          0.934000\n",
       "CONTENT_FLASH       0.988593\n",
       "DNS                 0.993802\n",
       "DROPBOX             0.940937\n",
       "EASYTAXI            0.992481\n",
       "EBAY                0.890052\n",
       "FACEBOOK            0.879433\n",
       "FTP_DATA            0.967936\n",
       "GMAIL               0.657201\n",
       "GOOGLE              0.490741\n",
       "GOOGLE_MAPS         0.967480\n",
       "H323                0.995902\n",
       "HTTP                0.749568\n",
       "HTTP_CONNECT        0.560219\n",
       "HTTP_DOWNLOAD       0.988024\n",
       "HTTP_PROXY          0.678279\n",
       "INSTAGRAM           0.971944\n",
       "IP_ICMP             1.000000\n",
       "LASTFM              0.984095\n",
       "MICROSOFT           0.720379\n",
       "MQTT                0.988166\n",
       "MSN                 0.854130\n",
       "MSSQL               0.998047\n",
       "MS_ONE_DRIVE        0.983929\n",
       "NETFLIX             0.973881\n",
       "NTP                 1.000000\n",
       "OFFICE_365          0.914035\n",
       "SKYPE               0.811563\n",
       "SPOTIFY             0.990215\n",
       "SSL                 0.666667\n",
       "SSL_NO_CERT         0.974903\n",
       "TEAMVIEWER          0.992000\n",
       "TOR                 0.988189\n",
       "TWITTER             0.813187\n",
       "WHATSAPP            0.933586\n",
       "WIKIPEDIA           0.927734\n",
       "WINDOWS_UPDATE      0.892157\n",
       "YAHOO               0.858439\n",
       "YOUTUBE             0.598911"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Macro Recall : '+str(recall_score(y_test1, y_pred1,average=\"macro\")))\n",
    "print('Micro Recall : '+str(recall_score(y_test1, y_pred1,average=\"micro\")))\n",
    "print('Weighted Recall : '+str(recall_score(y_test1, y_pred1,average=\"weighted\")))\n",
    "print('Recall Scores : ' )\n",
    "pd.DataFrame(recall_score(y_pred1,y_test1,labels=yL_test.columns,average = None),columns=['recall-score'],index=yL_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. ML methods\n",
    "#### Performing ML methods on Transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sklearn libraries\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomForest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_features=None, random_state=0)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf=RandomForestClassifier(random_state=0,max_features=None)\n",
    "clf.fit(XL_train1,yL_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.983920704845815\n",
      "0.864273127753304\n"
     ]
    }
   ],
   "source": [
    "print(clf.score(XL_train1,yL_train2))# train accuracy\n",
    "print(clf.score(XL_test1,yL_test2))# test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict(XL_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weighted F1 Score : 0.8696102779947574\n",
      "weighted Precision : 0.8819690265237563\n",
      "weighted Recall : 0.864273127753304\n"
     ]
    }
   ],
   "source": [
    "# Results\n",
    "print('weighted F1 Score : ' + str(f1_score(y_pred,yL_test2,average='weighted')))\n",
    "print('weighted Precision : ' + str(precision_score(y_pred,yL_test2,average='weighted')))\n",
    "print('weighted Recall : ' + str(recall_score(y_pred,yL_test2,average='weighted')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf=SVC(random_state=0,kernel='rbf').fit(XL_train1,yL_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.score(XL_train1,yL_train2) # train accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.score(XL_test1,yL_test2) # test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict(XL_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#results\n",
    "print('weighted F1 Score : ' + str(f1_score(y_pred,yL_test2,average = 'weighted')))\n",
    "print('weighted Precision : ' + str(precision_score(y_pred,yL_test2,average = 'weighted')))\n",
    "print('weighted Recall : ' + str(recall_score(y_pred,yL_test2,average = 'weighted')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf=DecisionTreeClassifier(random_state=0,max_depth=20).fit(XL_train1,yL_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.690897577092511"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(XL_train1,yL_train2) #train accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6444052863436124"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(XL_test1,yL_test2)# test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weighted F1 Score : 0.6445420293703698\n",
      "weighted Precision : 0.7235119488636761\n",
      "weighted Recall : 0.6444052863436124\n"
     ]
    }
   ],
   "source": [
    "#results\n",
    "y_pred=clf.predict(XL_test1)\n",
    "print('weighted F1 Score : ' + str(f1_score(y_pred,yL_test2,average = 'weighted')))\n",
    "print('weighted Precision : ' + str(precision_score(y_pred,yL_test2,average = 'weighted')))\n",
    "print('weighted Recall : ' + str(recall_score(y_pred,yL_test2,average = 'weighted')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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