SDAE_Code_v3-supervised.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\n",
    "import time\n",
    "from tensorflow.keras import regularizers"
   ]
  },
  {
   "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": [
    "\n",
    "XL=pd.read_csv('9_5day_resampled.csv')\n",
    "#XU_train=pd.read_csv('9_510_U_resample_95_5.csv')# No resampling done in the unlabeled set, It just denotes the pair of L and U\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "#XU_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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>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",
       "      <th>ProtocolName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>180881369</td>\n",
       "      <td>38752</td>\n",
       "      <td>874422793</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494318e+09</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AMAZON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>180881347</td>\n",
       "      <td>60112</td>\n",
       "      <td>921084361</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494322e+09</td>\n",
       "      <td>117939538</td>\n",
       "      <td>214</td>\n",
       "      <td>242</td>\n",
       "      <td>541</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>2104280.0</td>\n",
       "      <td>43740.211271</td>\n",
       "      <td>2135209.0</td>\n",
       "      <td>2073351.0</td>\n",
       "      <td>26398694.5</td>\n",
       "      <td>993864.743909</td>\n",
       "      <td>27101463.0</td>\n",
       "      <td>25695926.0</td>\n",
       "      <td>AMAZON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180881370</td>\n",
       "      <td>45916</td>\n",
       "      <td>915964343</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494318e+09</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</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>180881370</td>\n",
       "      <td>45075</td>\n",
       "      <td>915964343</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494317e+09</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AMAZON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180881370</td>\n",
       "      <td>43447</td>\n",
       "      <td>872483344</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494318e+09</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AMAZON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source.IP  Source.Port  Destination.IP  Destination.Port  Protocol  \\\n",
       "0  180881369        38752       874422793                80         6   \n",
       "1  180881347        60112       921084361                80         6   \n",
       "2  180881370        45916       915964343                80         6   \n",
       "3  180881370        45075       915964343                80         6   \n",
       "4  180881370        43447       872483344                80         6   \n",
       "\n",
       "      Timestamp  Flow.Duration  Total.Fwd.Packets  Total.Backward.Packets  \\\n",
       "0  1.494318e+09             86                  1                       1   \n",
       "1  1.494322e+09      117939538                214                     242   \n",
       "2  1.494318e+09            101                  1                       1   \n",
       "3  1.494317e+09            140                  1                       1   \n",
       "4  1.494318e+09            118                  1                       1   \n",
       "\n",
       "   Fwd.Packet.Length.Max  ...  min_seg_size_forward  Active.Mean  \\\n",
       "0                      0  ...                    32          0.0   \n",
       "1                    541  ...                    32    2104280.0   \n",
       "2                      0  ...                    32          0.0   \n",
       "3                      0  ...                    32          0.0   \n",
       "4                      0  ...                    32          0.0   \n",
       "\n",
       "     Active.Std  Active.Max  Active.Min   Idle.Mean       Idle.Std  \\\n",
       "0      0.000000         0.0         0.0         0.0       0.000000   \n",
       "1  43740.211271   2135209.0   2073351.0  26398694.5  993864.743909   \n",
       "2      0.000000         0.0         0.0         0.0       0.000000   \n",
       "3      0.000000         0.0         0.0         0.0       0.000000   \n",
       "4      0.000000         0.0         0.0         0.0       0.000000   \n",
       "\n",
       "     Idle.Max    Idle.Min  ProtocolName  \n",
       "0         0.0         0.0        AMAZON  \n",
       "1  27101463.0  25695926.0        AMAZON  \n",
       "2         0.0         0.0        AMAZON  \n",
       "3         0.0         0.0        AMAZON  \n",
       "4         0.0         0.0        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",
       "GMAIL             5330\n",
       "MICROSOFT         5285\n",
       "EDONKEY           5000\n",
       "HTTP_DOWNLOAD     5000\n",
       "WHATSAPP          5000\n",
       "WIKIPEDIA         5000\n",
       "H323              5000\n",
       "GOOGLE_MAPS       5000\n",
       "WINDOWS_UPDATE    5000\n",
       "FTP_DATA          5000\n",
       "FACEBOOK          5000\n",
       "EBAY              5000\n",
       "INSTAGRAM         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",
       "UPNP              5000\n",
       "TWITTER           5000\n",
       "TOR               5000\n",
       "RTMP              5000\n",
       "TEAMVIEWER        5000\n",
       "TEAMSPEAK         5000\n",
       "STARCRAFT         5000\n",
       "SSL_NO_CERT       5000\n",
       "UBUNTUONE         5000\n",
       "SSH               5000\n",
       "SPOTIFY           5000\n",
       "SKYPE             5000\n",
       "SIMET             5000\n",
       "OSCAR             5000\n",
       "IP_OSPF           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",
       "YAHOO             5000\n",
       "LASTFM            5000\n",
       "IP_ICMP           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\n",
    "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": 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.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": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#different test set\n",
    "dummy = (yL_test.head(pd.read_csv('yL_test_95U-5L.csv').shape[0])*0).reset_index(drop=True)\n",
    "ytest = pd.read_csv('yL_test_95U-5L.csv')\n",
    "#dummy converts 24-one hot encoded classes into 54-one hot encoded classes\n",
    "for i in yL_test.columns:\n",
    "    if i in ytest.columns:\n",
    "        dummy[i] = ytest[i]\n",
    "yL_test = dummy\n",
    "XL_test = pd.read_csv('XL_test_95U-5L.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Reversing the one hot encoding for each set to be used for ML methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "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(XL_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": 21,
   "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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
    "        \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",
    "        \n",
    "        ec.append(encoder)\n",
    "        X=encoder.predict(X)\n",
    "    print('All Layers Done!')    \n",
    "    return ec       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Layer 1 : \n",
      "Train on 222092 samples, validate on 222092 samples\n",
      "Epoch 1/10\n",
      "222092/222092 [==============================] - 4s 17us/step - loss: 5.5291 - val_loss: 0.4855\n",
      "Epoch 2/10\n",
      "222092/222092 [==============================] - 3s 14us/step - loss: 0.4385 - val_loss: 0.3348\n",
      "Epoch 3/10\n",
      "222092/222092 [==============================] - 3s 15us/step - loss: 0.3581 - val_loss: 0.2882\n",
      "Epoch 4/10\n",
      "222092/222092 [==============================] - 3s 16us/step - loss: 0.3201 - val_loss: 0.3027\n",
      "Epoch 5/10\n",
      "222092/222092 [==============================] - 3s 15us/step - loss: 0.2930 - val_loss: 0.2497\n",
      "Epoch 6/10\n",
      "222092/222092 [==============================] - 3s 14us/step - loss: 0.2747 - val_loss: 0.2356\n",
      "Epoch 7/10\n",
      "222092/222092 [==============================] - 3s 14us/step - loss: 0.2583 - val_loss: 0.2384\n",
      "Epoch 8/10\n",
      "222092/222092 [==============================] - 3s 14us/step - loss: 0.2479 - val_loss: 0.2180\n",
      "Epoch 9/10\n",
      "222092/222092 [==============================] - 3s 13us/step - loss: 0.2336 - val_loss: 0.2111\n",
      "Epoch 10/10\n",
      "222092/222092 [==============================] - 3s 14us/step - loss: 0.2238 - val_loss: 0.2108\n",
      "Training Layer 2 : \n",
      "Train on 222092 samples, validate on 222092 samples\n",
      "Epoch 1/10\n",
      "222092/222092 [==============================] - 4s 18us/step - loss: 0.3143 - val_loss: 0.2222\n",
      "Epoch 2/10\n",
      "222092/222092 [==============================] - 4s 17us/step - loss: 0.2054 - val_loss: 0.2089\n",
      "Epoch 3/10\n",
      "222092/222092 [==============================] - 5s 21us/step - loss: 0.1920 - val_loss: 0.1973\n",
      "Epoch 4/10\n",
      "222092/222092 [==============================] - 4s 19us/step - loss: 0.1766 - val_loss: 0.1817\n",
      "Epoch 5/10\n",
      "222092/222092 [==============================] - 4s 17us/step - loss: 0.1674 - val_loss: 0.1671\n",
      "Epoch 6/10\n",
      "222092/222092 [==============================] - 4s 17us/step - loss: 0.1596 - val_loss: 0.1572\n",
      "Epoch 7/10\n",
      "222092/222092 [==============================] - 4s 17us/step - loss: 0.1548 - val_loss: 0.1817\n",
      "Epoch 8/10\n",
      "222092/222092 [==============================] - 3s 15us/step - loss: 0.1531 - val_loss: 0.1796\n",
      "Epoch 9/10\n",
      "222092/222092 [==============================] - 3s 15us/step - loss: 0.1500 - val_loss: 0.1626\n",
      "Epoch 10/10\n",
      "222092/222092 [==============================] - 3s 15us/step - loss: 0.1461 - val_loss: 0.1520\n",
      "Training Layer 3 : \n",
      "Train on 222092 samples, validate on 222092 samples\n",
      "Epoch 1/10\n",
      "222092/222092 [==============================] - 8s 34us/step - loss: 0.6344 - val_loss: 0.4933\n",
      "Epoch 2/10\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 0.4664 - val_loss: 0.7636\n",
      "Epoch 3/10\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 0.4796 - val_loss: 0.5208\n",
      "Epoch 4/10\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 0.4647 - val_loss: 0.6722\n",
      "Epoch 5/10\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 0.4686 - val_loss: 0.7174\n",
      "Epoch 6/10\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 0.4532 - val_loss: 0.8336\n",
      "Epoch 7/10\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 0.4455 - val_loss: 0.3725\n",
      "Epoch 8/10\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 0.4449 - val_loss: 0.5382\n",
      "Epoch 9/10\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 0.4391 - val_loss: 0.3730\n",
      "Epoch 10/10\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 0.4594 - val_loss: 0.6157\n",
      "Training Layer 4 : \n",
      "Train on 222092 samples, validate on 222092 samples\n",
      "Epoch 1/10\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 0.1301 - val_loss: 0.1096\n",
      "Epoch 2/10\n",
      "222092/222092 [==============================] - 5s 22us/step - loss: 0.1083 - val_loss: 0.1515\n",
      "Epoch 3/10\n",
      "222092/222092 [==============================] - 4s 20us/step - loss: 0.1093 - val_loss: 0.1230\n",
      "Epoch 4/10\n",
      "222092/222092 [==============================] - 4s 19us/step - loss: 0.1092 - val_loss: 0.0809\n",
      "Epoch 5/10\n",
      "222092/222092 [==============================] - 4s 19us/step - loss: 0.1028 - val_loss: 0.0685\n",
      "Epoch 6/10\n",
      "222092/222092 [==============================] - 4s 19us/step - loss: 0.1049 - val_loss: 0.0641\n",
      "Epoch 7/10\n",
      "222092/222092 [==============================] - 4s 19us/step - loss: 0.1046 - val_loss: 0.1502\n",
      "Epoch 8/10\n",
      "222092/222092 [==============================] - 4s 19us/step - loss: 0.1040 - val_loss: 0.0704\n",
      "Epoch 9/10\n",
      "222092/222092 [==============================] - 4s 20us/step - loss: 0.0980 - val_loss: 0.1500\n",
      "Epoch 10/10\n",
      "222092/222092 [==============================] - 5s 21us/step - loss: 0.1011 - val_loss: 0.1699\n",
      "All Layers Done!\n"
     ]
    }
   ],
   "source": [
    "#Train Sparse Stacked Denoising Autoencoder\n",
    "layers = [100,200,400,50]\n",
    "activations=['relu','relu','relu','relu']\n",
    "rho=[0.06,0.06,0.06,0.06]\n",
    "traint0=time.time()\n",
    "stacked_layers=SDAE(XU_train,layers,activations,rho,10,'mask',0.02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.Training the SoftMax classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "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": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 222092 samples, validate on 27761 samples\n",
      "Epoch 1/50\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 3.1876 - accuracy: 0.2602 - val_loss: 2.9073 - val_accuracy: 0.3069\n",
      "Epoch 2/50\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 2.7746 - accuracy: 0.3404 - val_loss: 2.6953 - val_accuracy: 0.3637\n",
      "Epoch 3/50\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 2.6176 - accuracy: 0.3800 - val_loss: 2.5718 - val_accuracy: 0.3895\n",
      "Epoch 4/50\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 2.5190 - accuracy: 0.4024 - val_loss: 2.4912 - val_accuracy: 0.4067\n",
      "Epoch 5/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.4488 - accuracy: 0.4189 - val_loss: 2.4313 - val_accuracy: 0.4306\n",
      "Epoch 6/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.3955 - accuracy: 0.4316 - val_loss: 2.3873 - val_accuracy: 0.4409\n",
      "Epoch 7/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.3536 - accuracy: 0.4429 - val_loss: 2.3500 - val_accuracy: 0.4450\n",
      "Epoch 8/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.3197 - accuracy: 0.4515 - val_loss: 2.3164 - val_accuracy: 0.4434\n",
      "Epoch 9/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.2908 - accuracy: 0.4578 - val_loss: 2.2909 - val_accuracy: 0.4562\n",
      "Epoch 10/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.2673 - accuracy: 0.4627 - val_loss: 2.2705 - val_accuracy: 0.4570\n",
      "Epoch 11/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.2461 - accuracy: 0.4660 - val_loss: 2.2500 - val_accuracy: 0.4591\n",
      "Epoch 12/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.2283 - accuracy: 0.4695 - val_loss: 2.2344 - val_accuracy: 0.4644\n",
      "Epoch 13/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.2123 - accuracy: 0.4712 - val_loss: 2.2178 - val_accuracy: 0.4678\n",
      "Epoch 14/50\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 2.1982 - accuracy: 0.4737 - val_loss: 2.2045 - val_accuracy: 0.4695\n",
      "Epoch 15/50\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 2.1858 - accuracy: 0.4764 - val_loss: 2.1933 - val_accuracy: 0.4760\n",
      "Epoch 16/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.1743 - accuracy: 0.4777 - val_loss: 2.1803 - val_accuracy: 0.4750\n",
      "Epoch 17/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.1642 - accuracy: 0.4785 - val_loss: 2.1698 - val_accuracy: 0.4789\n",
      "Epoch 18/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.1545 - accuracy: 0.4803 - val_loss: 2.1637 - val_accuracy: 0.4744\n",
      "Epoch 19/50\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 2.1462 - accuracy: 0.4817 - val_loss: 2.1563 - val_accuracy: 0.4823\n",
      "Epoch 20/50\n",
      "222092/222092 [==============================] - 7s 31us/step - loss: 2.1384 - accuracy: 0.4833 - val_loss: 2.1483 - val_accuracy: 0.4821\n",
      "Epoch 21/50\n",
      "222092/222092 [==============================] - 8s 35us/step - loss: 2.1311 - accuracy: 0.4847 - val_loss: 2.1435 - val_accuracy: 0.4829\n",
      "Epoch 22/50\n",
      "222092/222092 [==============================] - 7s 33us/step - loss: 2.1246 - accuracy: 0.4861 - val_loss: 2.1338 - val_accuracy: 0.4856\n",
      "Epoch 23/50\n",
      "222092/222092 [==============================] - 7s 30us/step - loss: 2.1184 - accuracy: 0.4873 - val_loss: 2.1300 - val_accuracy: 0.4886\n",
      "Epoch 24/50\n",
      "222092/222092 [==============================] - 7s 30us/step - loss: 2.1129 - accuracy: 0.4877 - val_loss: 2.1253 - val_accuracy: 0.4868\n",
      "Epoch 25/50\n",
      "222092/222092 [==============================] - 7s 31us/step - loss: 2.1073 - accuracy: 0.4883 - val_loss: 2.1166 - val_accuracy: 0.4861\n",
      "Epoch 26/50\n",
      "222092/222092 [==============================] - 7s 30us/step - loss: 2.1025 - accuracy: 0.4901 - val_loss: 2.1126 - val_accuracy: 0.4873\n",
      "Epoch 27/50\n",
      "222092/222092 [==============================] - 7s 31us/step - loss: 2.0976 - accuracy: 0.4911 - val_loss: 2.1067 - val_accuracy: 0.4867\n",
      "Epoch 28/50\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 2.0932 - accuracy: 0.4917 - val_loss: 2.1053 - val_accuracy: 0.4872\n",
      "Epoch 29/50\n",
      "222092/222092 [==============================] - 7s 30us/step - loss: 2.0890 - accuracy: 0.4926 - val_loss: 2.0997 - val_accuracy: 0.4919\n",
      "Epoch 30/50\n",
      "222092/222092 [==============================] - 8s 35us/step - loss: 2.0852 - accuracy: 0.4932 - val_loss: 2.0973 - val_accuracy: 0.4949\n",
      "Epoch 31/50\n",
      "222092/222092 [==============================] - 6s 29us/step - loss: 2.0815 - accuracy: 0.4937 - val_loss: 2.0932 - val_accuracy: 0.4892\n",
      "Epoch 32/50\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 2.0779 - accuracy: 0.4943 - val_loss: 2.0913 - val_accuracy: 0.4913\n",
      "Epoch 33/50\n",
      "222092/222092 [==============================] - 7s 32us/step - loss: 2.0747 - accuracy: 0.4950 - val_loss: 2.0892 - val_accuracy: 0.4923\n",
      "Epoch 34/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.0717 - accuracy: 0.4953 - val_loss: 2.0852 - val_accuracy: 0.4878\n",
      "Epoch 35/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.0685 - accuracy: 0.4957 - val_loss: 2.0813 - val_accuracy: 0.4930\n",
      "Epoch 36/50\n",
      "222092/222092 [==============================] - 6s 25us/step - loss: 2.0658 - accuracy: 0.4965 - val_loss: 2.0782 - val_accuracy: 0.4910\n",
      "Epoch 37/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.0633 - accuracy: 0.4964 - val_loss: 2.0772 - val_accuracy: 0.4961\n",
      "Epoch 38/50\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 2.0606 - accuracy: 0.4970 - val_loss: 2.0760 - val_accuracy: 0.4931\n",
      "Epoch 39/50\n",
      "222092/222092 [==============================] - 6s 28us/step - loss: 2.0581 - accuracy: 0.4981 - val_loss: 2.0702 - val_accuracy: 0.4968\n",
      "Epoch 40/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.0555 - accuracy: 0.4979 - val_loss: 2.0695 - val_accuracy: 0.4903\n",
      "Epoch 41/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.0537 - accuracy: 0.4981 - val_loss: 2.0695 - val_accuracy: 0.4935\n",
      "Epoch 42/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.0513 - accuracy: 0.4987 - val_loss: 2.0665 - val_accuracy: 0.4971\n",
      "Epoch 43/50\n",
      "222092/222092 [==============================] - 6s 26us/step - loss: 2.0493 - accuracy: 0.4990 - val_loss: 2.0640 - val_accuracy: 0.4937\n",
      "Epoch 44/50\n",
      "222092/222092 [==============================] - 6s 27us/step - loss: 2.0473 - accuracy: 0.4994 - val_loss: 2.0645 - val_accuracy: 0.4961\n",
      "Epoch 45/50\n",
      "222092/222092 [==============================] - 7s 32us/step - loss: 2.0455 - accuracy: 0.4996 - val_loss: 2.0600 - val_accuracy: 0.4936\n",
      "Epoch 46/50\n",
      "222092/222092 [==============================] - 7s 31us/step - loss: 2.0437 - accuracy: 0.4997 - val_loss: 2.0617 - val_accuracy: 0.4921\n",
      "Epoch 47/50\n",
      "222092/222092 [==============================] - 7s 34us/step - loss: 2.0420 - accuracy: 0.5001 - val_loss: 2.0557 - val_accuracy: 0.4997\n",
      "Epoch 48/50\n",
      "222092/222092 [==============================] - 8s 38us/step - loss: 2.0403 - accuracy: 0.5000 - val_loss: 2.0563 - val_accuracy: 0.4991\n",
      "Epoch 49/50\n",
      "222092/222092 [==============================] - 11s 50us/step - loss: 2.0387 - accuracy: 0.5005 - val_loss: 2.0533 - val_accuracy: 0.4949\n",
      "Epoch 50/50\n",
      "222092/222092 [==============================] - 8s 36us/step - loss: 2.0372 - accuracy: 0.5008 - val_loss: 2.0536 - val_accuracy: 0.4966\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": "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": 29,
   "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[-2].activity_regularizer=None  \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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add dropout till n-2 layers\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": 31,
   "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"
     ]
    }
   ],
   "source": [
    "model.save('temp.h5')\n",
    "model=load_model('temp.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=RMSprop(learning_rate=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fine tune model runs for no. of epochs set below\n",
    "history=model.fit(XL_train,yL_train,\n",
    "                epochs=750,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(XL_val, yL_val),\n",
    "                verbose=0)\n",
    "traint1=time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train_time: 6695.371121406555\n"
     ]
    }
   ],
   "source": [
    "print(\"Train_time: \"+str(traint1-traint0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12037/12037 [==============================] - 0s 29us/step\n",
      "[0.6717147101736588, 0.8292763829231262]\n",
      "Test_time: 0.384021520614624\n"
     ]
    }
   ],
   "source": [
    "test0=time.time()\n",
    "print(model.evaluate(XL_test,yL_test))\n",
    "test1=time.time()\n",
    "print(\"Test_time: \"+str(test1-test0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aa1713e320>]"
      ]
     },
     "execution_count": 36,
     "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": [
    "#Visualise convergence with no. of epochs\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Results\n",
    "#### Below are the results obtained on our NN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(XL_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1=y_pred.argmax(axis=1)\n",
    "y_pred1=np.append(y_pred1,np.arange(54))\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[:-54]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AMAZON            500\n",
       "APPLE             474\n",
       "APPLE_ICLOUD        0\n",
       "APPLE_ITUNES      515\n",
       "CITRIX_ONLINE       0\n",
       "CLOUDFLARE        461\n",
       "CONTENT_FLASH     494\n",
       "DNS                 0\n",
       "DROPBOX           487\n",
       "EASYTAXI            0\n",
       "EBAY              508\n",
       "EDONKEY             0\n",
       "FACEBOOK          473\n",
       "FTP_DATA            0\n",
       "GMAIL             501\n",
       "GOOGLE            550\n",
       "GOOGLE_MAPS         0\n",
       "H323                0\n",
       "HTTP              491\n",
       "HTTP_CONNECT      490\n",
       "HTTP_DOWNLOAD       0\n",
       "HTTP_PROXY        533\n",
       "INSTAGRAM           0\n",
       "IP_ICMP             0\n",
       "IP_OSPF             0\n",
       "LASTFM              0\n",
       "MICROSOFT         497\n",
       "MQTT                0\n",
       "MSN               497\n",
       "MSSQL               0\n",
       "MS_ONE_DRIVE        0\n",
       "NETFLIX             0\n",
       "NTP                 0\n",
       "OFFICE_365        522\n",
       "OSCAR               0\n",
       "RTMP                0\n",
       "SIMET               0\n",
       "SKYPE             491\n",
       "SPOTIFY             0\n",
       "SSH                 0\n",
       "SSL               517\n",
       "SSL_NO_CERT         0\n",
       "STARCRAFT           0\n",
       "TEAMSPEAK           0\n",
       "TEAMVIEWER          0\n",
       "TOR                 0\n",
       "TWITTER           509\n",
       "UBUNTUONE           0\n",
       "UPNP                0\n",
       "WHATSAPP          506\n",
       "WIKIPEDIA         511\n",
       "WINDOWS_UPDATE    502\n",
       "YAHOO             515\n",
       "YOUTUBE           493\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yL_test.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test1 = yL_test.columns[np.where(yL_test!=0)[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['WINDOWS_UPDATE', 'APPLE', 'APPLE_ITUNES', 'OFFICE_365', 'MICROSOFT',\n",
       "       'WIKIPEDIA', 'CLOUDFLARE', 'NETFLIX', 'EBAY', 'MSN',\n",
       "       ...\n",
       "       'WINDOWS_UPDATE', 'YAHOO', 'HTTP_CONNECT', 'GMAIL', 'HTTP', 'MSN',\n",
       "       'GMAIL', 'TWITTER', 'WHATSAPP', 'SKYPE'],\n",
       "      dtype='object', length=12037)"
      ]
     },
     "execution_count": 42,
     "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": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm=confusion_matrix(y_test1,y_pred1,labels=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2520x1800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy : 0.8292763977735316\n"
     ]
    }
   ],
   "source": [
    "print('Test Accuracy : '+str(accuracy_score(y_test1, y_pred1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weighted F1 Score : 0.8293751920828161\n",
      "Macro F1 Score : 0.4866299593841758\n",
      "Micro F1 Score : 0.8292763977735316\n",
      "F1 Scores : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1465: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  average, \"true nor predicted\", 'F-score is', len(true_sum)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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>0.873444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.922747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ICLOUD</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.943223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRIX_ONLINE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.946912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.982741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNS</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.959514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EASYTAXI</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>0.949533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDONKEY</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.894901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTP_DATA</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.763132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.454829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE_MAPS</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H323</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.728762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.573643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_DOWNLOAD</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.606506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTAGRAM</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_ICMP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_OSPF</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASTFM</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.775673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQTT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSQL</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ONE_DRIVE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETFLIX</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.976614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSCAR</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTMP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMET</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.812371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPOTIFY</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSH</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.600626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL_NO_CERT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STARCRAFT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMSPEAK</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMVIEWER</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.868496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBUNTUONE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPNP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.963810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>0.978927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.932563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.939597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.623266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                F1-score\n",
       "AMAZON          0.873444\n",
       "APPLE           0.922747\n",
       "APPLE_ICLOUD    0.000000\n",
       "APPLE_ITUNES    0.943223\n",
       "CITRIX_ONLINE   0.000000\n",
       "CLOUDFLARE      0.946912\n",
       "CONTENT_FLASH   0.982741\n",
       "DNS             0.000000\n",
       "DROPBOX         0.959514\n",
       "EASYTAXI        0.000000\n",
       "EBAY            0.949533\n",
       "EDONKEY         0.000000\n",
       "FACEBOOK        0.894901\n",
       "FTP_DATA        0.000000\n",
       "GMAIL           0.763132\n",
       "GOOGLE          0.454829\n",
       "GOOGLE_MAPS     0.000000\n",
       "H323            0.000000\n",
       "HTTP            0.728762\n",
       "HTTP_CONNECT    0.573643\n",
       "HTTP_DOWNLOAD   0.000000\n",
       "HTTP_PROXY      0.606506\n",
       "INSTAGRAM       0.000000\n",
       "IP_ICMP         0.000000\n",
       "IP_OSPF         0.000000\n",
       "LASTFM          0.000000\n",
       "MICROSOFT       0.775673\n",
       "MQTT            0.000000\n",
       "MSN             0.880000\n",
       "MSSQL           0.000000\n",
       "MS_ONE_DRIVE    0.000000\n",
       "NETFLIX         0.000000\n",
       "NTP             0.000000\n",
       "OFFICE_365      0.976614\n",
       "OSCAR           0.000000\n",
       "RTMP            0.000000\n",
       "SIMET           0.000000\n",
       "SKYPE           0.812371\n",
       "SPOTIFY         0.000000\n",
       "SSH             0.000000\n",
       "SSL             0.600626\n",
       "SSL_NO_CERT     0.000000\n",
       "STARCRAFT       0.000000\n",
       "TEAMSPEAK       0.000000\n",
       "TEAMVIEWER      0.000000\n",
       "TOR             0.000000\n",
       "TWITTER         0.868496\n",
       "UBUNTUONE       0.000000\n",
       "UPNP            0.000000\n",
       "WHATSAPP        0.963810\n",
       "WIKIPEDIA       0.978927\n",
       "WINDOWS_UPDATE  0.932563\n",
       "YAHOO           0.939597\n",
       "YOUTUBE         0.623266"
      ]
     },
     "execution_count": 47,
     "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(yL_train.columns)# No. of distinct labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted Precision : 0.8325331844456951\n",
      "Macro Precision : 0.48840228596205093\n",
      "Micro Precision : 0.8292763977735316\n",
      "Precision Scores : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.842000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.907173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ICLOUD</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRIX_ONLINE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.947939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.979757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNS</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.973306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EASYTAXI</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDONKEY</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTP_DATA</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.768463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.398182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE_MAPS</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H323</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.725051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.528571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_DOWNLOAD</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.542214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTAGRAM</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_ICMP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_OSPF</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASTFM</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.782696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQTT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.885312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSQL</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ONE_DRIVE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETFLIX</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSCAR</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTMP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMET</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.802444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPOTIFY</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSH</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.557060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL_NO_CERT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STARCRAFT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMSPEAK</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMVIEWER</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.901768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBUNTUONE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPNP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>1.000000</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>0.964143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.951456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.592292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Precision-score\n",
       "AMAZON                 0.842000\n",
       "APPLE                  0.907173\n",
       "APPLE_ICLOUD           0.000000\n",
       "APPLE_ITUNES           1.000000\n",
       "CITRIX_ONLINE          0.000000\n",
       "CLOUDFLARE             0.947939\n",
       "CONTENT_FLASH          0.979757\n",
       "DNS                    0.000000\n",
       "DROPBOX                0.973306\n",
       "EASYTAXI               0.000000\n",
       "EBAY                   1.000000\n",
       "EDONKEY                0.000000\n",
       "FACEBOOK               0.909091\n",
       "FTP_DATA               0.000000\n",
       "GMAIL                  0.768463\n",
       "GOOGLE                 0.398182\n",
       "GOOGLE_MAPS            0.000000\n",
       "H323                   0.000000\n",
       "HTTP                   0.725051\n",
       "HTTP_CONNECT           0.528571\n",
       "HTTP_DOWNLOAD          0.000000\n",
       "HTTP_PROXY             0.542214\n",
       "INSTAGRAM              0.000000\n",
       "IP_ICMP                0.000000\n",
       "IP_OSPF                0.000000\n",
       "LASTFM                 0.000000\n",
       "MICROSOFT              0.782696\n",
       "MQTT                   0.000000\n",
       "MSN                    0.885312\n",
       "MSSQL                  0.000000\n",
       "MS_ONE_DRIVE           0.000000\n",
       "NETFLIX                0.000000\n",
       "NTP                    0.000000\n",
       "OFFICE_365             1.000000\n",
       "OSCAR                  0.000000\n",
       "RTMP                   0.000000\n",
       "SIMET                  0.000000\n",
       "SKYPE                  0.802444\n",
       "SPOTIFY                0.000000\n",
       "SSH                    0.000000\n",
       "SSL                    0.557060\n",
       "SSL_NO_CERT            0.000000\n",
       "STARCRAFT              0.000000\n",
       "TEAMSPEAK              0.000000\n",
       "TEAMVIEWER             0.000000\n",
       "TOR                    0.000000\n",
       "TWITTER                0.901768\n",
       "UBUNTUONE              0.000000\n",
       "UPNP                   0.000000\n",
       "WHATSAPP               1.000000\n",
       "WIKIPEDIA              1.000000\n",
       "WINDOWS_UPDATE         0.964143\n",
       "YAHOO                  0.951456\n",
       "YOUTUBE                0.592292"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Weighted Precision : '+str(precision_score(y_test1, y_pred1,average=\"weighted\")))\n",
    "print('Macro Precision : '+str(precision_score(y_test1, y_pred1,average=\"macro\")))\n",
    "print('Micro Precision : '+str(precision_score(y_test1, y_pred1,average=\"micro\")))\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": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted Recall : 0.8292763977735316\n",
      "Macro Recall : 0.4868029104521999\n",
      "Micro Recall : 0.8292763977735316\n",
      "Recall Scores : \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recall-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.907328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.938865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ICLOUD</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.892548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRIX_ONLINE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.945887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.985743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNS</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.946108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EASYTAXI</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>0.903915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDONKEY</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.881148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTP_DATA</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.757874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.530266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE_MAPS</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H323</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.732510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.627119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_DOWNLOAD</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.688095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTAGRAM</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_ICMP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_OSPF</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASTFM</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.768775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQTT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.874751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSQL</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ONE_DRIVE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETFLIX</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.954296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSCAR</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTMP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMET</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.822547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPOTIFY</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSH</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.651584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL_NO_CERT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STARCRAFT</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMSPEAK</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMVIEWER</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.837591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBUNTUONE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPNP</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.930147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>0.958724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.902985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.928030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.657658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                recall-score\n",
       "AMAZON              0.907328\n",
       "APPLE               0.938865\n",
       "APPLE_ICLOUD        0.000000\n",
       "APPLE_ITUNES        0.892548\n",
       "CITRIX_ONLINE       0.000000\n",
       "CLOUDFLARE          0.945887\n",
       "CONTENT_FLASH       0.985743\n",
       "DNS                 0.000000\n",
       "DROPBOX             0.946108\n",
       "EASYTAXI            0.000000\n",
       "EBAY                0.903915\n",
       "EDONKEY             0.000000\n",
       "FACEBOOK            0.881148\n",
       "FTP_DATA            0.000000\n",
       "GMAIL               0.757874\n",
       "GOOGLE              0.530266\n",
       "GOOGLE_MAPS         0.000000\n",
       "H323                0.000000\n",
       "HTTP                0.732510\n",
       "HTTP_CONNECT        0.627119\n",
       "HTTP_DOWNLOAD       0.000000\n",
       "HTTP_PROXY          0.688095\n",
       "INSTAGRAM           0.000000\n",
       "IP_ICMP             0.000000\n",
       "IP_OSPF             0.000000\n",
       "LASTFM              0.000000\n",
       "MICROSOFT           0.768775\n",
       "MQTT                0.000000\n",
       "MSN                 0.874751\n",
       "MSSQL               0.000000\n",
       "MS_ONE_DRIVE        0.000000\n",
       "NETFLIX             0.000000\n",
       "NTP                 0.000000\n",
       "OFFICE_365          0.954296\n",
       "OSCAR               0.000000\n",
       "RTMP                0.000000\n",
       "SIMET               0.000000\n",
       "SKYPE               0.822547\n",
       "SPOTIFY             0.000000\n",
       "SSH                 0.000000\n",
       "SSL                 0.651584\n",
       "SSL_NO_CERT         0.000000\n",
       "STARCRAFT           0.000000\n",
       "TEAMSPEAK           0.000000\n",
       "TEAMVIEWER          0.000000\n",
       "TOR                 0.000000\n",
       "TWITTER             0.837591\n",
       "UBUNTUONE           0.000000\n",
       "UPNP                0.000000\n",
       "WHATSAPP            0.930147\n",
       "WIKIPEDIA           0.958724\n",
       "WINDOWS_UPDATE      0.902985\n",
       "YAHOO               0.928030\n",
       "YOUTUBE             0.657658"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Weighted Recall : '+str(recall_score(y_test1, y_pred1,average=\"weighted\")))\n",
    "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('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": [
    "#### Confusion Matrix with 10 chosen well-known classes\n",
    "Classes : AMAZON, FACEBOOK, GMAIL, GOOGLE, HTTP, OFFICE_365, SKYPE, TWITTER, WHATSAPP, YOUTUBE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "apps=['AMAZON','FACEBOOK','GMAIL','GOOGLE','HTTP','OFFICE_365','SKYPE','TWITTER','WHATSAPP','YOUTUBE']#reporting for these well-known Apps\n",
    "y_test1[y_test1.isin(apps)]\n",
    "cm=confusion_matrix(y_test1[y_test1.isin(apps)],y_pred1[y_test1.isin(apps)],labels=y_test1[y_test1.isin(apps)].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_pc=pd.DataFrame((cm.T/np.sum(cm,axis=1)).T,columns=y_test1[y_test1.isin(apps)].unique(),index=y_test1[y_test1.isin(apps)].unique())\n",
    "cm_pc=cm_pc.replace(np.nan,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "df_cm=pd.DataFrame(cm,columns=y_test1[y_test1.isin(apps)].unique(),index=y_test1[y_test1.isin(apps)].unique())\n",
    "sns.set(font_scale=1.1)\n",
    "sns.heatmap(cm_pc, annot=True,fmt='.1%',cmap=sns.color_palette(\"Reds\",150)) # font size\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_test_restricted = y_test1[y_pred1.isin(ytest.columns)]\n",
    "y_pred_restricted = y_pred1[y_pred1.isin(ytest.columns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro F1 : 0.837060911787677\n",
      "Macro Precision : 0.8343539051851702\n",
      "Macro Recall : 0.8427278760728454\n"
     ]
    }
   ],
   "source": [
    "print('Macro F1 : '+str(f1_score(y_test_restricted, y_pred_restricted ,average=\"macro\")))\n",
    "print('Macro Precision : '+str(precision_score(y_test_restricted, y_pred_restricted ,average=\"macro\")))\n",
    "print('Macro Recall : '+str(recall_score(y_test_restricted, y_pred_restricted ,average=\"macro\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}