Testing_net_attacks_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train=pd.read_csv('train_data.csv')\n",
    "data_test=pd.read_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the Training set:  (169307, 43)\n",
      "The shape of the Testing set:  (91166, 42)\n"
     ]
    }
   ],
   "source": [
    "print(\"The shape of the Training set: \", data_train.shape)\n",
    "print(\"The shape of the Testing set: \", data_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Target Class')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p06dRqVT4+vqSlpam9Xjrjh07UrZsWRYvXmxQLEIIIcTbzmTHNCQmJpKUlETz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\nQVg0EBERkSAsGoiIiEiQ/wCnge+CxidFhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xccbc278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.style as style\n",
    "style.use('fivethirtyeight')\n",
    "sns.countplot(x=\"target\", data=data_train)\n",
    "plt.title('Frequency of the target class in the Training Set')\n",
    "plt.ylabel('Frequency of the Target')\n",
    "plt.xlabel('Target Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd1bf160>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P66GHHtKBAwf0hz/8QZGRkTp48KBef/11bdq0Sfv27VOLFi2UkZGhp59+2tX9fOutt7R+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd1bf0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_train_num=data_train.drop(['connection_id','cat_1','cat_2','cat_3','cat_4','cat_5','cat_6','cat_7','cat_8','cat_9','cat_10','cat_11','cat_12','cat_13','cat_14','cat_15','cat_16','cat_17','cat_18','cat_19','cat_20','cat_21','cat_22','cat_23','target'],axis=1)\n",
    "colormap = plt.cm.viridis\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(data_train_num.astype(float).corr(),linewidths=0.05,vmax=1.0, square=True, cmap=colormap, linecolor='black', annot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prox_matrix(df, target, features, cluster_dimension,trees = 10):\n",
    "    #https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#prox\n",
    "\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "\n",
    "    #initialize datframe for independant variables\n",
    "    independant = pd.DataFrame()\n",
    "\n",
    "    #Handle Categoricals: This should really be added to RandomForestRegressor\n",
    "    for column,data_type in df[features].dtypes.iteritems():       \n",
    "        try:\n",
    "            independant[column] = pd.to_numeric(df[column],downcast = 'integer')\n",
    "        except ValueError:\n",
    "            contains_nulls = df[column].isnull().values.any()\n",
    "            dummies = pd.get_dummies(df[column],prefix=column,dummy_na=contains_nulls,drop_first=True)\n",
    "            independant[dummies.columns] = dummies\n",
    "\n",
    "    if len(independant.index) != len(df.index):\n",
    "        raise Exception('independant variables not stored properly')\n",
    "\n",
    "    #train Model    \n",
    "    clf = RandomForestClassifier(n_estimators=trees, n_jobs=-1)\n",
    "    clf.fit(independant, df[target])\n",
    "\n",
    "    #Final leaf for each tree\n",
    "    leaves = clf.apply(independant)\n",
    "    #value in cluster dimension\n",
    "    labels = df[cluster_dimension].values\n",
    "\n",
    "    numerator_matrix = {}\n",
    "    for i,value_i in enumerate(labels):\n",
    "        for j,value_j in enumerate(labels):\n",
    "            if i >= j:       \n",
    "                numerator_matrix[(value_i,value_j)] = numerator_matrix.get((value_i,value_j), 0) + np.count_nonzero(leaves[i]==leaves[j])\n",
    "                numerator_matrix[(value_j,value_i)] = numerator_matrix[(value_i,value_j)] \n",
    "\n",
    "    #normalize by the total number of possible matchnig leaves        \n",
    "    prox_matrix = {key: 1.0 - float(x)/(trees*np.count_nonzero(labels==key[0])*np.count_nonzero(labels==key[1])) for key, x in numerator_matrix.iteritems()}                                                                  \n",
    "\n",
    "    #make sorted dataframe                                                                                                                                                                                                                                                                \n",
    "    levels = np.unique(labels)\n",
    "    D = pd.DataFrame(data=[[ prox_matrix[(i,j)] for i in levels] for j in levels],index=levels,columns=levels)\n",
    "\n",
    "    return D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def kMedoids(D, k, tmax=100):\n",
    "    #https://www.researchgate.net/publication/272351873_NumPy_SciPy_Recipes_for_Data_Science_k-Medoids_Clustering\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "\n",
    "    # determine dimensions of distance matrix D\n",
    "    m, n = D.shape\n",
    "\n",
    "    if m != n:\n",
    "        raise Exception('matrix not symmetric')\n",
    "\n",
    "    if sum(D.columns.values != D.index.values):\n",
    "        raise Exception('rows and columns do not match')\n",
    "\n",
    "    if k > n:\n",
    "        raise Exception('too many medoids')\n",
    "\n",
    "    #Some distance matricies will not have a 0 diagonal    \n",
    "    Dtemp =D.copy()\n",
    "    np.fill_diagonal(Dtemp.values,0)\n",
    "\n",
    "    # randomly initialize an array of k medoid indices\n",
    "    M = list(Dtemp.sample(k).index.values)\n",
    "\n",
    "    # initialize a dictionary to represent clusters\n",
    "    Cnew = {}\n",
    "\n",
    "    for t in xrange(tmax):    \n",
    "        # determine mapping to clusters\n",
    "        J = Dtemp.loc[M].idxmin(axis='index')\n",
    "        #Fill dictionary with cluster members\n",
    "        C = {kappa: J[J==kappa].index.values for kappa in J.unique()}  \n",
    "        # update cluster medoids\n",
    "        Cnew = {Dtemp.loc[C[kappa],C[kappa]].mean().idxmin() : C[kappa] for kappa in C.keys()}       \n",
    "        #Update mediod list\n",
    "        M = Cnew.keys()\n",
    "\n",
    "        # check for convergence (ie same clusters)\n",
    "        if set(C.keys()) == set(Cnew.keys()):\n",
    "            if not sum(set(C[kappa]) != set(Cnew[kappa]) for kappa in C.keys()): break            \n",
    "    else:        \n",
    "        print('did not converge')\n",
    "\n",
    "    #Calculate silhouette \n",
    "    S = {}\n",
    "    for kappa_same in Cnew.keys():\n",
    "        a = Dtemp.loc[Cnew[kappa_same],Cnew[kappa_same]].mean().mean()\n",
    "        b = np.min([Dtemp.loc[Cnew[kappa_other],Cnew[kappa_same]].mean().mean() for kappa_other in Cnew.keys() if kappa_other!=kappa_same])\n",
    "        S[kappa_same] = (b - a) / max(a, b)\n",
    "\n",
    "    # return results\n",
    "    return M, Cnew, S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total=data_train.drop('target',axis=1)\n",
    "X_total=X_total.append(data_test)\n",
    "X_test=data_test[['connection_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train=data_train[['connection_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_num=X_total.drop(['connection_id','cat_1','cat_2','cat_3','cat_4','cat_5','cat_6','cat_7','cat_8','cat_9','cat_10','cat_11','cat_12','cat_13','cat_14','cat_15','cat_16','cat_17','cat_18','cat_19','cat_20','cat_21','cat_22','cat_23'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(260473, 42)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_total.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train_num=data_train.drop(['connection_id','cat_1','cat_2','cat_3','cat_4','cat_5','cat_6','cat_7','cat_8','cat_9','cat_10','cat_11','cat_12','cat_13','cat_14','cat_15','cat_16','cat_17','cat_18','cat_19','cat_20','cat_21','cat_22','cat_23','target'],axis=1)\n",
    "data_test_num=data_test.drop(['connection_id','cat_1','cat_2','cat_3','cat_4','cat_5','cat_6','cat_7','cat_8','cat_9','cat_10','cat_11','cat_12','cat_13','cat_14','cat_15','cat_16','cat_17','cat_18','cat_19','cat_20','cat_21','cat_22','cat_23'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_num_scld=StandardScaler().fit_transform(data_train_num)\n",
    "X_test_num_scld=StandardScaler().fit_transform(data_test_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca=PCA()\n",
    "pca2=PCA()\n",
    "X_train_num_trf=pca.fit_transform(X_train_num_scld)\n",
    "X_test_num_trf=pca2.fit_transform(X_test_num_scld)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NFElEREREpIjgQLt582YEBwejS5cuWLVqFaRSqazN1NQUHTt2xK+//qqRIomIiIiIFBEc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11965ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var=(pca.explained_variance_)\n",
    "tot=sum(var)\n",
    "var_exp = [(i/tot)*100 for i in sorted(var, reverse=True)] # Individual explained variance\n",
    "\n",
    "exp_var_ratio=pca.explained_variance_ratio_\n",
    "cum_var=np.cumsum(exp_var_ratio*100)\n",
    "\n",
    "# PLOT OUT THE EXPLAINED VARIANCES SUPERIMPOSED \n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(18), var_exp, alpha=0.3333, align='center', label='individual explained variance', color = 'g')\n",
    "plt.step(range(18), cum_var, where='mid',label='cumulative explained variance')\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.xlabel('Principal components')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca=PCA(n_components=10)\n",
    "X_train_num_trf_10d=pca.fit_transform(X_train_num_scld)\n",
    "X_train_pca=pd.DataFrame(X_train_num_trf_10d,columns=['PC1','PC2','PC3','PC4','PC5','PC6','PC7','PC8','PC9','PC10'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca2=PCA(n_components=10)\n",
    "X_test_num_trf_10d=pca.fit_transform(X_test_num_scld)\n",
    "X_test_pca=pd.DataFrame(X_test_num_trf_10d,columns=['PC1','PC2','PC3','PC4','PC5','PC6','PC7','PC8','PC9','PC10'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del X_train_num_trf\n",
    "del X_train_num_trf_10d\n",
    "del X_test_num_trf\n",
    "del X_test_num_trf_10d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.526824</td>\n",
       "      <td>-0.273090</td>\n",
       "      <td>0.055980</td>\n",
       "      <td>-0.370688</td>\n",
       "      <td>-0.013640</td>\n",
       "      <td>-0.011342</td>\n",
       "      <td>-0.023437</td>\n",
       "      <td>0.121546</td>\n",
       "      <td>-0.255491</td>\n",
       "      <td>0.120225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.526767</td>\n",
       "      <td>-0.273085</td>\n",
       "      <td>0.055472</td>\n",
       "      <td>-0.371114</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>-0.003077</td>\n",
       "      <td>-0.022022</td>\n",
       "      <td>0.121603</td>\n",
       "      <td>-0.255331</td>\n",
       "      <td>0.120371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.526824</td>\n",
       "      <td>-0.273090</td>\n",
       "      <td>0.055980</td>\n",
       "      <td>-0.370688</td>\n",
       "      <td>-0.013640</td>\n",
       "      <td>-0.011342</td>\n",
       "      <td>-0.023437</td>\n",
       "      <td>0.121546</td>\n",
       "      <td>-0.255491</td>\n",
       "      <td>0.120225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.526824</td>\n",
       "      <td>-0.273090</td>\n",
       "      <td>0.055980</td>\n",
       "      <td>-0.370688</td>\n",
       "      <td>-0.013640</td>\n",
       "      <td>-0.011342</td>\n",
       "      <td>-0.023437</td>\n",
       "      <td>0.121546</td>\n",
       "      <td>-0.255491</td>\n",
       "      <td>0.120225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.526824</td>\n",
       "      <td>-0.273090</td>\n",
       "      <td>0.055980</td>\n",
       "      <td>-0.370688</td>\n",
       "      <td>-0.013640</td>\n",
       "      <td>-0.011342</td>\n",
       "      <td>-0.023437</td>\n",
       "      <td>0.121546</td>\n",
       "      <td>-0.255491</td>\n",
       "      <td>0.120225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
       "0 -1.526824 -0.273090  0.055980 -0.370688 -0.013640 -0.011342 -0.023437   \n",
       "1 -1.526767 -0.273085  0.055472 -0.371114 -0.022053 -0.003077 -0.022022   \n",
       "2 -1.526824 -0.273090  0.055980 -0.370688 -0.013640 -0.011342 -0.023437   \n",
       "3 -1.526824 -0.273090  0.055980 -0.370688 -0.013640 -0.011342 -0.023437   \n",
       "4 -1.526824 -0.273090  0.055980 -0.370688 -0.013640 -0.011342 -0.023437   \n",
       "\n",
       "        PC8       PC9      PC10  \n",
       "0  0.121546 -0.255491  0.120225  \n",
       "1  0.121603 -0.255331  0.120371  \n",
       "2  0.121546 -0.255491  0.120225  \n",
       "3  0.121546 -0.255491  0.120225  \n",
       "4  0.121546 -0.255491  0.120225  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.924014</td>\n",
       "      <td>-0.169952</td>\n",
       "      <td>0.165744</td>\n",
       "      <td>-0.285230</td>\n",
       "      <td>0.113625</td>\n",
       "      <td>-0.089368</td>\n",
       "      <td>-0.400043</td>\n",
       "      <td>-1.480095</td>\n",
       "      <td>-0.379930</td>\n",
       "      <td>-0.210480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.524722</td>\n",
       "      <td>-0.271372</td>\n",
       "      <td>-0.044706</td>\n",
       "      <td>0.068415</td>\n",
       "      <td>-0.362890</td>\n",
       "      <td>-0.017348</td>\n",
       "      <td>0.122403</td>\n",
       "      <td>0.260594</td>\n",
       "      <td>0.109133</td>\n",
       "      <td>0.006373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.524724</td>\n",
       "      <td>-0.271371</td>\n",
       "      <td>-0.043494</td>\n",
       "      <td>0.068362</td>\n",
       "      <td>-0.362953</td>\n",
       "      <td>-0.017374</td>\n",
       "      <td>0.122428</td>\n",
       "      <td>0.260691</td>\n",
       "      <td>0.109144</td>\n",
       "      <td>0.006299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.524724</td>\n",
       "      <td>-0.271371</td>\n",
       "      <td>-0.043494</td>\n",
       "      <td>0.068362</td>\n",
       "      <td>-0.362953</td>\n",
       "      <td>-0.017374</td>\n",
       "      <td>0.122428</td>\n",
       "      <td>0.260691</td>\n",
       "      <td>0.109144</td>\n",
       "      <td>0.006299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.524724</td>\n",
       "      <td>-0.271371</td>\n",
       "      <td>-0.043494</td>\n",
       "      <td>0.068362</td>\n",
       "      <td>-0.362953</td>\n",
       "      <td>-0.017374</td>\n",
       "      <td>0.122428</td>\n",
       "      <td>0.260691</td>\n",
       "      <td>0.109144</td>\n",
       "      <td>0.006299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
       "0 -0.924014 -0.169952  0.165744 -0.285230  0.113625 -0.089368 -0.400043   \n",
       "1 -1.524722 -0.271372 -0.044706  0.068415 -0.362890 -0.017348  0.122403   \n",
       "2 -1.524724 -0.271371 -0.043494  0.068362 -0.362953 -0.017374  0.122428   \n",
       "3 -1.524724 -0.271371 -0.043494  0.068362 -0.362953 -0.017374  0.122428   \n",
       "4 -1.524724 -0.271371 -0.043494  0.068362 -0.362953 -0.017374  0.122428   \n",
       "\n",
       "        PC8       PC9      PC10  \n",
       "0 -1.480095 -0.379930 -0.210480  \n",
       "1  0.260594  0.109133  0.006373  \n",
       "2  0.260691  0.109144  0.006299  \n",
       "3  0.260691  0.109144  0.006299  \n",
       "4  0.260691  0.109144  0.006299  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_cat=pd.DataFrame(X_total,columns=['cat_1','cat_2','cat_3','cat_4','cat_5','cat_6','cat_7','cat_8','cat_9','cat_10','cat_11','cat_12','cat_13','cat_14','cat_15','cat_16','cat_17','cat_18','cat_19','cat_20','cat_21','cat_22','cat_23'],dtype=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature:- cat_1 has 3 unique categories\n",
      "Feature:- cat_2 has 66 unique categories\n",
      "Feature:- cat_3 has 11 unique categories\n",
      "Feature:- cat_4 has 2 unique categories\n",
      "Feature:- cat_5 has 3 unique categories\n",
      "Feature:- cat_6 has 2 unique categories\n",
      "Feature:- cat_7 has 19 unique categories\n",
      "Feature:- cat_8 has 3 unique categories\n",
      "Feature:- cat_9 has 2 unique categories\n",
      "Feature:- cat_10 has 15 unique categories\n",
      "Feature:- cat_11 has 2 unique categories\n",
      "Feature:- cat_12 has 3 unique categories\n",
      "Feature:- cat_13 has 13 unique categories\n",
      "Feature:- cat_14 has 11 unique categories\n",
      "Feature:- cat_15 has 3 unique categories\n",
      "Feature:- cat_16 has 7 unique categories\n",
      "Feature:- cat_17 has 1 unique categories\n",
      "Feature:- cat_18 has 2 unique categories\n",
      "Feature:- cat_19 has 2 unique categories\n",
      "Feature:- cat_20 has 462 unique categories\n",
      "Feature:- cat_21 has 431 unique categories\n",
      "Feature:- cat_22 has 256 unique categories\n",
      "Feature:- cat_23 has 256 unique categories\n"
     ]
    }
   ],
   "source": [
    "for col_name in X_total_cat.columns:\n",
    "    if X_total_cat[col_name].dtype=='object':\n",
    "        unique_cat=len(X_total_cat[col_name].unique())\n",
    "        print(\"Feature:- {col_name} has {unique_cat} unique categories\".format(col_name=col_name,unique_cat=unique_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Target Class')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l0dfkfSQkJLBo0SI2bdqke63eHC+SNDj0bcmd60nXcCtXrmxQVrBgQRwcHAw+FE19f7yv\n2NhYLl68iI2NDeXLl3/vdkw5ZlqtlgYNGrB7926qV6+Ol5cXVapUoWLFigaXfVq3bs1ff/1Fu3bt\naNKkCbVq1cLd3f2DnvPbjJ2HSV8YpnzWve++MsI5n5xy5cole/kjNjaWuXPnsnPnTq5du8aTJ09Q\nFEVXntz7xJj8+fPrJYtJkhLWmJgYVckrGD/Gb7aTJOl9aewHZ+bMmSlXrhx3795VtU+A7Nmzs2DB\nAn755Rf27dtHSEgIoaGhnDlzhnnz5rFkyRLmz5+vG1sTHByMoigkJCQY/a5JGg+VGj8M0j1pMEVy\no+4fPnwIwKxZs1J8/JMnTwB0A56Say+1RvcnxbV582ZVcb0puWur5ubmer/eY2NjAUzKZLt160ZQ\nUBD/+c9/GD16NFFRUfj7++Pq6mo0CTDmXcfQ3t6es2fPqhpclhrUHq+k1+TKlSv4+fkl297Lly9T\nJa5vv/2WXbt24eLigpeXF7a2trpftH/88UeyI6CTO66PHz9OsdzGxsYgaTD1/fG+3udcNMbUY7Z0\n6VJmzZrF+vXrda9plixZaNSoEePHj9clxhUrVmTXrl1MmzaNbdu2sXbtWuB1sjVo0CC6dOnyQXGD\n8fPQ3Pz1x2xq97pl1HM+Ocmds0+fPqVhw4ZcuHCBMmXK4O3tjbW1NWZmZjx//pwZM2aYNFMgueOS\nlLCY8joYa8tYO0nvy+RmdLzvd4qjoyOdO3emc+fOAERGRjJ8+HDWr19P//79qVOnDjly5NC9xseO\nHdMNYjbmzcHU7+uTShqSGwiT9MLevHlT1UCmpDpvDqR8U3LbM2XKlOwbK+kD09h+Vq5ciaen5zvj\neh+5cuUCTMvEmzZtip2dHcuXL2f48OGsWLGC58+f891336lu481jaGwg3/379/XqZRRJ8Xh5eeku\nsaSVEydOsGvXLurWrcuaNWt0Xx7w+gPn999/T/axyZ3rSYMSIyIiKF68uEG5sVHdpr4/3tf7nItv\ne59jljVrVnx9ffH19eXu3bsEBQWxdu1atmzZQlhYGIcPH9a1U6lSJVavXs2LFy84e/YsBw4c4K+/\n/mLgwIFYWVkZdPd/Dj7mOZ+S5M7p9evXc+HCBXr27GmQ1ISFhTFjxoyPEd4HSXpfJjerIrnvFFPl\nzZuXefPmsW/fPmJiYjh9+jS1atXSvcZDhw5l1KhRqbKv5KT7mIbUkDQyVe0lha+++gp4nZUZyzqP\nHDli9HG5cuUiPDzcaOLw5vWt943rfRQvXpycOXNy7tw51dfqMmfOTOfOnbl//z7bt29n6dKlZM2a\nlY4dO6reb9IxDAwMNCgLDw8nLCyMHDlyfPDiI0nXoVPrV1rS8Tp58mSqTDNLKb6rV68C4Onpqffl\nB6+7E1+8eGHy/sqUKQNg9NfEjRs3jH5hv895+D7HPVeuXLi5uREREaGb5muqDz1mjo6OtGrVijVr\n1lChQgX+/fdf3WXAN2XJkoVKlSrh6+urW31WzXS4jyGjn/OpLek1N7Y0d3KfxRlN0vvS2HssISEh\nVcezZM6cGUtLS71t7/Mef5+eF/hMkoYePXqQOXNmRo4cafSazcuXL/W+3JycnKhRowY3btxgwYIF\nenW3b9+e7IGvVKkSL168MMjW9+zZY/QShJeXF87OzixcuJDdu3cbbTMkJOSDrnWam5vTtWtXnj17\nxqBBg4iPj9crT0xMJDw83OBxPj4+mJub4+vry9WrV2nevDlarVb1fpPm+E6ZMkUvi1YUhTFjxhAf\nH0+HDh0+eApX0rXJ27dvf1A7STJnzkyPHj24f/8+w4YN49mzZwZ1IiMj+d///vfB8Tk5OQGvxxK8\nKSIigmHDhpkaOgBt2rTBzMyMBQsW6O1TURTGjh1rdH0NU98f8P7HPWlZ8B9++MHgvFYU5Z3XdU09\nZhEREZw8edJge3x8vK73L+kD9siRI0Z7BJPeHxYWFinG9rFk9HM+tSW95m+fg2FhYUavz2dEderU\nwc7Ojt27dxskOvPmzTNpPAPAuHHjkn3MsmXLuH//PlmzZtWNuXBxccHT05PDhw8ze/Zso58D169f\n1xtA+b7n2Sd1eSI5xYoVY86cOfTv358qVapQt25dihQpwsuXL7l9+zbHjx/n1atXuowWYNq0adSv\nX58RI0YQEBBAqVKluHr1Ktu2baNhw4bs2rXLYD+9e/dm9erVDB48mICAAJycnPj33385ePAgTZs2\nZcuWLXr1s2TJwooVK2jVqhVt27bF3d2dMmXKkDVrVu7evcvZs2e5fPkyR48eNekL+20jRowgJCSE\nPXv2UL58eRo2bEiuXLm4e/cugYGB+Pj4MHToUL3HODo60qhRI93Mje+//96kfVatWpUBAwYwa9Ys\nqlSpQvPmzcmePTsBAQGEhoZSqlQp3YjdD1G5cmWyZcumm0OeL18+NBoN7du3VzXw0xhfX1/Onz/P\nkiVLdPPhHR0diYqK4tq1axw7doxevXpRunTpd7ZVp04d/P396d+/P02aNMHKyorcuXPTrVs33N3d\nqVSpEps2beLu3bt4eHgQHh7O3r17dYvOmKpIkSL4+voyYcIEqlevTsuWLXXrNMTGxlKiRAnOnz+v\nN5Pgfd4fKT2vlPj4+BAcHMzq1aspV66cbp2G8PBwgoKCqFatGrNnz0728aYeszt37lCvXj3dWiSO\njo48efKE/fv3c/XqVVq0aKEb/T5r1iwOHTpE9erVcXZ2JkeOHFy6dIndu3eTLVs2evfubfLrkRYy\n+jmf2lq0aMGUKVOYNGkSp0+fplixYty4cYNdu3bh6enJxo0bP3pMprKwsGDGjBl06tSJFi1a0Lx5\nc/Lnz09oaChBQUF8/fXXBAQEqFr/Bl5/P02fPp1y5crx1VdfkSdPHmJiYjh16hQhISFkypSJyZMn\n6y4JAsyZM4fmzZvz008/sWzZMjw8PMibNy/379/n4sWLnD59mrlz5+pmqpQpUwZra2v27NlD7969\nKViwIJkyZaJFixYprg3xWSQN8HqqU+nSpZk7dy6BgYEEBARgaWmJg4MDDRo0MOj6Klq0KPv27ePX\nX3/l77//5vDhw5QqVYpVq1Zx7949o0mDm5sbW7duZezYsezdu5dMmTJRoUIF/P39uXjxokHSAK9n\nKxw5coQ//viDXbt26VbmsrW1pXjx4gwcOJBChQp90HO3sLBg/fr1LF26lNWrV7N27VpevnyJnZ0d\n1atXp0GDBkYf16lTJ/z9/SldunSyo/VTMnbsWMqWLctff/3F2rVrefHiBc7OzgwdOpSBAwcaHetg\nqty5c7NixQomTZrEhg0bdAN5qlev/t4foJkzZ2bFihWsW7eOlStXsnfvXuLi4rC2tqZAgQL8+OOP\ntG3bVlVbSQsvbdiwgblz55KQkICLiwvdunXDzMyM1atXM378ePbu3cv8+fNxdHTEx8eHIUOGUKFC\nhfeKf9iwYeTLl4958+axYsUKcuTIQd26dRk7dqxuIaO3xy6Y+v5I6XmlRKPR8Oeff1K3bl3+85//\n4O/vT3x8PLa2tpQvX542bdqk+HhTj5mLiwsjRozg8OHDBAYGEhUVRa5cuShcuDCDBg2iffv2urrd\nu3fH2tqaU6dOcfz4cRISEnBwcKBjx4707ds3VWdRfIiMfs6ntty5c7Njxw5+/vlnjh07xqFDh3Bx\nceHXX3+lTZs2n0TSANCoUSM2b97MxIkT2bZtG+bm5ri7u7Nz507mzJkDqB/jtWXLFg4ePMjRo0fZ\ns2cPDx48wNzcHEdHRzp16kS3bt0MZnbkyZOHPXv2sHjxYjZt2sTWrVt17z0XFxfGjRtH/fr1dfUt\nLS1ZtWoVY8eOZfv27Tx+/BhFUShevHiKSYMmJiZGSbb0C7Vs2TIGDBjAqFGjDH6hf078/PyYOHEi\nM2bMMGkQpMiYYmJiKFq0KLa2tvzzzz/pHY4QgteX5apWrcqFCxe4dOlSqtwzIz19FmMahOkePXrE\nokWL0Gq1eHt7p3c4wgSRkZEGA9oSEhIYOXIkL168oGnTpukUmRBfrri4ON3UyzfNmzePCxcuUKVK\nlU8+YYDP6PKEUGf79u2cPXuW3bt3ExERwbhx47CyskrvsIQJtm7dysSJE6lduzb58uUjKiqKo0eP\ncuXKFQoVKvRZ944JkVFdunRJt2qxi4sLz58/5/Tp05w6dYqcOXOmuEbGp0SShi/Mli1bWLt2LXZ2\ndgwePNikm7eIjKFChQpUq1aNY8eOERkZyatXryhQoAD9+/dn0KBBqm4sJYRIXfny5aNly5YEBQVx\n6NAh4uPjsbOzo0OHDvz4448fPP08o5AxDUIIIYRQRcY0CCGEEEIVSRqEEEIIoYokDUIIIYRQRZIG\nIYQQQqgiSYMQQgghVJGkQQghhBCqSNIghBBCCFUkaRBCCCGEKpI0CCGEEEIVSRqEEEIIoYokDUII\nIYRQRZIGIYQQQqgid7nM4Er+EZzeIYjP1Lk+7ukdghDiEyM9DUIIIYRQRZIGIYQQQqgiSYMQQggh\nVJGkQQghhBCqqE4aQkJCiImJSbY8NjaWkJAQ1TuePn06X3/9NQUKFKBw4cK0bduW8+fP69Xp3bs3\nWq1W769evXp6dZ4/f87QoUMpVKgQjo6OtGvXjjt37ujVuXXrFm3btsXR0ZFChQoxbNgwXrx4oVfn\n8OHD1KpVCzs7O7766isWL15sEPPChQspU6YMdnZ21KpVi6NHj6p+vkIIIcSnTnXSUKdOHfbu3Zts\n+f79+6lTp47qHR8+fJjvv/+e3bt3s3XrVszNzWnevDnR0dF69WrXrk1YWJjub926dXrlI0aMwN/f\nn0WLFrFjxw4eP35M27ZtSUxMBCAxMZG2bdsSFxfHjh07WLRoEVu3bmXUqFG6Nq5fv06bNm1wd3fn\n0KFDDB48mGHDhrFlyxZdnY0bNzJ8+HB+/PFHDh06hLu7O97e3ty6dUv1cxZCCCE+ZaqnXCqKkmJ5\nQkICmTKpv9qxceNGvf/Pnz8fJycnjh07RqNGjXTbLSwssLOzM9pGbGws//3vf5k7dy5ff/21rp3S\npUtz8OBB6taty4EDB7hw4QL/+9//yJ8/PwC//vorAwYM4KeffiJnzpwsWbIEe3t7pkyZAoCbmxsn\nT55kzpw5NGvWDIC5c+fSoUMHunTpAsCUKVPYv38/ixcv5ueff1b9vIUQQohPVYrf8vHx8URHR/Pw\n4UMAnj59SnR0tMHftWvX2Lp1a7Jf7mrExcXx6tUrtFqt3vagoCCKFClChQoVGDBgAA8ePNCVhYSE\nkJCQoNfDkT9/ftzc3Dh+/DgAwcHBuLm56RIGgLp16/L8+XPd5ZTg4GCDXpK6dety5swZEhISePHi\nBSEhIQZ16tSpo9uPEEII8blLsadhxowZTJ48GQCNRsOgQYMYNGiQ0bqKojB06ND3DmT48OGULl0a\nd/f/W3CmXr16NGnSBGdnZ27evMn48eNp2rQpBw8exMLCgoiICMzMzLC2ttZry8bGhoiICAAiIiKw\nsbHRK7e2tsbMzEyvTu3atQ3aePnyJVFRUSiKQmJiokE7b+7HmEuXLpl8HIT4WOT8FMI0rq6u6R1C\nuksxaahatSqDBw8GYNq0aXh6elK8eHG9OhqNBisrK8qVK0etWrXeK4iRI0dy7Ngxdu3ahZmZmW57\nq1atdP8uWbIkZcuWpXTp0uzevZumTZsm256iKGg0Gr0YjUmpTtLlGI1Go/fvlPbztlQ5wfbKipAi\nbcgHoBDCVCkmDTVr1qRmzZrA61kK3t7elClTJlUDGDFiBBs3bsTf35+CBQumWNfBwQFHR0euXr0K\ngK2tLYmJiURFRZE3b15dvcjISKpWraqr8/YlhKioKL2eA1tbW4Meg8jISMzNzcmTJw+Kouj1TLxZ\n5+3eByGEEOJzpXrk4rhx4/QShufPn79zcOS7+Pr6sn79erZu3UrRokXfWT8qKop79+7pxk6ULVuW\nzJkzExAQoKtz584dwsLC8PDwAMDd3Z2wsDC9aZgBAQFYWFhQtmxZXZ2DBw/q7SsgIIBy5cqROXNm\nsmTJQtmyZfX2k1QnaT9CCCHE586kxZ3Onz9Px44dcXJywtHRkUOHDgGvv8x9fHwICgpS3daQIUNY\nuXIlCxcuRKvVEh4eTnh4OHFxccDrgZGjR48mODiYGzduEBgYSLt27bCxscHLywuAXLly8e233zJm\nzBgOHjzI2bNn6dmzJyVLltST+QIzAAAgAElEQVSNUahTpw7FixenV69enD17loMHDzJmzBg6d+5M\nzpw5AfDx8eHu3bsMHz6csLAwli1bxsqVK+nXr58u3r59+7Jy5UqWLVtGWFgYvr6+3L9/Hx8fH1MO\noRBCCPHJUj3lMiQkBE9PT3LlykX9+vX1pkxaW1tz584dlixZQpUqVVS1t3DhQgDdlMYkvr6+jBgx\nAjMzM86fP8/q1auJjY3Fzs6OGjVqsGTJEnLkyKGrP2HCBMzMzPDx8SE+Pp6aNWvy559/6sZGmJmZ\nsWbNGoYMGULDhg2xtLSkdevWjB8/XtdGwYIFWbt2LSNHjmTx4sXY29vj5+enF1vLli15+PAhU6ZM\nITw8nOLFi7N27VqcnJzUHkIhhBDik6aJiYlRdY2hZcuW3L59m/379/PixQuKFCnC5s2bdYMfJ0yY\nwLp16zhz5kyaBvylkVtji7Qit8YWQphK9eWJ4OBgunTpQo4cOYzOGMifPz/h4eGpGpwQQgghMg6T\nxjRkyZIl2bIHDx6kWC6EEEKIT5vqpKF06dLs27fPaNnLly/ZsGEDlSpVSrXAhBBCCJGxqE4aBg4c\nyJ49e/D19eXKlSsAxMTEEBQUhLe3NxcvXuSHH35Is0CFEEIIkb5UD4QEWLZsGSNHjuTp06d6qyFm\nzZqVqVOn0r59+zQL9EslAyFFWpGBkEIIU5mUNMDr3oXdu3dz5coVXr16hYuLC40aNSJPnjxpFeMX\nTZIGkVYkaRBCmEr1Og1JtFotbdu2TYtYhBBCCJGBqU4aoqOjUyzXaDRYWFiQNWvWDw5KCCGEEBmP\n6qShUKFCKd7RMYlWq6VmzZoMHjw41W9uJYQQQoj0ozpp8PPzY/ny5Vy9epWmTZtSpEgRFEXh8uXL\n+Pv7U6RIEZo3b87ly5fZvHkzu3fvZtu2bVSoUCEt4xdCCCHER6I6aVAUhdjYWE6dOqW7y2SSMWPG\n0KBBA7Jly8acOXMYOXIktWvXZsKECWzYsCHVgxZCCCHEx6d6nYZ58+bRtWtXg4QBwMHBAR8fH/74\n4w8AHB0d6dKlCydOnEi9SIUQQgiRrlQnDffv30+5oUyZ9Orkz5+fhISE949MCCGEEBmK6qTBzc2N\nJUuWEBkZaVD24MEDlixZQrFixXTbrl69io2NTepEKYQQQoh0p3pMw6+//krbtm0pX748zZs3p1Ch\nQgBcuXKFLVu2EB8fz4wZMwB48eIFa9eupXbt2mkStBBCCCE+PpNWhDxx4gTjxo3j6NGjJCYmAmBm\nZka1atUYNWoU7u6vV5hTFIWnT59iYWGBubnJ60eJN8iKkCKtyIqQQghTmfSNXqlSJbZu3Up8fDx3\n794FXg96tLS01Kun0WiwsrJKvSiFEEIIke5UjWl49uwZ1apVY+HChQBYWlpSqFAhChUqZJAwCCGE\nEOLzpCppyJo1K3fu3MHMzCyt4xFCCCFEBqV69kTdunUJCAhIy1iEEEIIkYGpThp++uknLl++TJ8+\nfTh16hQPHz7k6dOnBn9CCCGE+Dypnj2RO3duNBoNiqIke+MqjUZDVFRUqgb4pZPZEyKtyOwJIYSp\nVM+e6Nu3r6q7XAohhBDi86Q6aRg/fnxaxiGEEEKIDE71mAYhhBBCfNlMXq7x3LlzhISEEBsby6tX\nr/TKNBoNffv2TbXghBBCCJFxqE4anjx5QufOnQkICNANhlSU12Mo3xwgKUmDEEII8XlSfXnil19+\nITAwkGnTpnHkyBEURWHFihX8/ffftG7dmjJlyhAaGpqWsQohhBAiHalOGnbs2MG3336Lj48P9vb2\nAFhZWVGmTBkWLFhA3rx5mTRpUpoFKoQQQoj0pTppiIyM5KuvvgIgc+bMAHqLOTVs2JBdu3alcnhC\nCCGEyChUJw02NjZERkYCkCNHDqysrLh8+bKuPC4ujhcvXqR+hEIIIYTIEFQnDeXKlePYsWO6/3/9\n9dfMnTuXbdu2sXXrVubNm0f58uVV73j69Ol8/fXXFChQgMKFC9O2bVvOnz+vV0dRFCZOnEixYsWw\nt7encePGXLhwQa9OTEwMPXr0wMnJCScnJ3r06EFMTIxenXPnzuHp6Ym9vT3FixfHz89PN4gzyZYt\nW/Dw8MDW1hYPDw/8/f1NjkUIIYT4nKlOGrp164atrS3x8fEAjBs3jixZsvDtt9/SpUsXsmTJwsSJ\nE1Xv+PDhw3z//ffs3r2brVu3Ym5uTvPmzYmOjtbVmTlzJnPnzsXPz48DBw5gY2NDixYtePz4sV5c\noaGhrFu3jvXr1xMaGkrPnj115Y8ePaJFixbY2tpy4MABJk2axOzZs5kzZ46uTnBwMF27dsXb25vA\nwEC8vb357rvvOHnypEmxCCGEEJ8z1feeMCYhIYFTp05hZmZG6dKlsbS0fO9A4uLicHJyYsWKFTRq\n1AhFUShWrBjdu3dnyJAhADx79gxXV1fGjRuHj48PYWFheHh4sGvXLipXrgxAUFAQjRo14sSJE7i6\nurJo0SJ++eUXLl68SNasWQGYMmUKixcv5vz582g0Gnx8fIiOjmbz5s26eJo1a0bevHlZtGiRqljS\nitx7QqQVufeEEMJUKfY0tGjRgkOHDiVbnjlzZipXrkylSpU+KGGA10nDq1ev0Gq1ANy4cYPw8HDq\n1Kmjq5M1a1aqVq3K8ePHgdc9BNmzZ8fDw0NXp3LlylhZWenVqVKlii5hgNe3+b537x43btwA4MSJ\nE3r7SaqT1IaaWIQQQojPXYpJw8GDBwkPD/8ogQwfPpzSpUvj7v7610/Sfm1sbPTq2djYEBERAUBE\nRATW1tZ6N9LSaDTkzZtXr46xNpLKkvaV0n7UxCKEEEJ87kxeRjotjBw5kmPHjrFr1y7MzMz0yt6+\ns+bbt+Y2dufNd9V5cyVLtftRW+dNly5dSrZMiPQm56cQpnF1dU3vENJduicNI0aMYOPGjfj7+1Ow\nYEHddjs7O+B1b0D+/Pl12yMjI3W/+G1tbYmMjNT78lYUhaioKL06b/cGJE0dTapjZ2dntM6b5e+K\nxZhUOcH2ypgGkTbkA1AIYap3Jg2nT5/GwsJCdYNNmzZVXdfX15eNGzeybds2ihYtqlfm7OyMnZ0d\nAQEBuqmc8fHxBAUFMXbsWADc3d2Ji4sjODhYN64hODiYJ0+e6P7v7u7OL7/8Qnx8vG7cRUBAAA4O\nDjg7OwNQqVIlAgICGDBggG7/AQEBujbUxCKEEEJ87t6ZNMyfP58///xTVWMajYaHDx+qqjtkyBDW\nrFnD8uXL0Wq1unEDVlZWZM+eHY1GQ+/evZk2bRqurq4UKVKEqVOnYmVlRevWrQFwc3OjXr16DBo0\niJkzZ6IoCoMGDaJBgwa6X1GtW7fGz8+PPn36MGTIEC5fvsyMGTMYNmyYrneiV69eeHp6Mn36dLy8\nvNi2bRuBgYG6FS7VxCKEEEJ87lKccpk7d24GDx5MzZo1VTdYq1YtVfWSZkm8zdfXlxEjRgCvLzVM\nmjSJpUuXEhMTQ4UKFZg6dSolSpTQ1Y+OjsbX15edO3cC0KhRIyZPnqzX/rlz5xgyZAinT59Gq9Xi\n4+ODr6+v3niELVu2MH78eK5fv46LiwujR4/W6zVRE0takCmXIq3IlEshhKnemTQsWLAAb2/vjxmT\neIMkDSKtSNIghDCV6hUhhRBCCPFlk6RBCCGEEKqkmDR4e3vrTYMUQgghxJcrxdkTCxYs+FhxCCGE\nECKDk8sTQgghhFBFkgYhhBBCqCJJgxBCCCFUkaRBCCGEEKq8d9IQGxtLYmJiasYihBBCiAzMpKQh\nJCSE1q1b4+DgQKFChTh8+DAAUVFRtG/fnsDAwDQJUgghhBDpT3XScPLkSRo2bEhYWBgtW7ZEUf5v\n9Wlra2tiYmJYtmxZmgQphBBCiPSnOmkYN24chQoV4vjx44wdO1YvaQCoWbMmJ06cSPUAhRBCCJEx\nmNTT0KlTJ7Jly6Z3d8gk+fLl093eWgghhBCfH9VJg0ajwczMLNny8PBwLC0tUyUoIYQQQmQ8qpOG\nr776ij179hgtS0hIYP369bi7y612hRBCiM+V6qRh8ODBBAQE8MMPP3D+/HkAHjx4wMGDB2nWrBlX\nr15l8ODBaRaoEEIIIdKXJiYmRnl3tddWr16Nr68vjx8/RlEU3diG7NmzM2PGDFq2bJlmgX6pSv4R\nnN4hiM/UuT7SMyiEMI1JSQNAXFwc+/fv58qVK7x69QoXFxe++eYbcubMmVYxftEkaRBpRZIGIYSp\nUrw1tjHZs2enWbNmaRGLEEIIITIwk5OGp0+fcufOHaKjow3WagDw8PBIlcCEEEIIkbGoThpiY2MZ\nPnw4GzZs4OXLlwblSWMcHj58mKoBCiGEECJjUJ00DBo0iC1bttC1a1eqV6+OVqtNy7iEEEIIkcGo\nThr27t1Ljx49mDhxYlrGI4QQQogMSvU6Debm5ri6uqZlLEIIIYTIwFQnDU2bNmX//v1pGYsQQggh\nMrBkk4bo6Gi9v0GDBnH37l369evHmTNniIyMNKgTHR39MWMXQgghxEeU7OJOuXPnNribZdIUS2N3\nuUwisydSlyzuJNKKLO4khDBVsgMhf/zxx48ZhxBCCCEyuGSThtGjR3/MOIQQQgiRwakeCDlw4EBO\nnTqVbPmZM2cYOHBgqgQlhBBCiIxHddKwbNkyrl69mmz5tWvX+O9//5sqQQkhhBAi41GdNLzLw4cP\nsbCwSK3mhBBCCJHBpLgiZFBQEEePHtX9f8eOHdy8edOgXkxMDOvXr6dkyZIm7fzIkSPMnj2bs2fP\ncu/ePebOnUvHjh115b1792bVqlV6j6lYsSL79u3T/f/58+eMHj2aDRs2EB8fT82aNZk2bRr58uXT\n1bl16xZDhgwhMDAQS0tLWrduzfjx48mSJYuuzuHDhxk1ahT//vsv9vb2DBw4kK5du+rte+HChcya\nNYvw8HCKFSvGxIkTqVq1qknPWQghhPhUpZg0/P333/j5+QGvp1lu3ryZzZs3G63r4uLChAkTTNr5\nkydPKFGiBO3bt6dXr15G69SuXZv58+fr/v/mFz3AiBEj2LFjB4sWLSJ37tyMGjWKtm3b8vfff2Nm\nZkZiYiJt27Yld+7c7Nixg+joaHr37o2iKEyZMgWA69ev06ZNGzp27MiCBQs4duwYP/74I9bW1rrb\ngG/cuJHhw4czbdo0KleuzMKFC/H29ubYsWMUKFDApOcthBBCfIqSXacB4PHjxzx+/BhFUShVqhST\nJ0+mcePG+g1oNGTLlo1cuXJ9UCD58uVj8uTJBj0NDx8+ZM2aNUYfExsbS5EiRZg7dy5t2rQB4Pbt\n25QuXZr169dTt25d9u7dS5s2bfjf//5H/vz5AVizZg0DBgzg0qVL5MyZk59//hl/f39Onz6ta7t/\n//78+++/7N27F4C6detSsmRJZs2apatTvnx5mjVrxs8///xBzz0lsk6DSCuyToMQwlQpjmnIkSMH\njo6O5MuXj9OnT9OhQwccHR31/hwcHD44YUhJUFAQRYoUoUKFCgwYMIAHDx7oykJCQkhISKBOnTq6\nbfnz58fNzY3jx48DEBwcjJubmy5hgNcJwPPnzwkJCdHVebONpDpnzpwhISGBFy9eEBISYlCnTp06\nuv0IIYQQnzvVd7l0cXFJyziMqlevHk2aNMHZ2ZmbN28yfvx4mjZtysGDB7GwsCAiIgIzMzOsra31\nHmdjY0NERAQAERER2NjY6JVbW1tjZmamV6d27doGbbx8+ZKoqCgURSExMdGgnTf3Y8ylS5fe96kL\nkebk/BTCNHLTRhOShvTQqlUr3b9LlixJ2bJlKV26NLt376Zp06bJPk5RFL2lrpNb9jqlOm8umZ3c\n8tlv7+dtqXKC7ZXLEyJtyAegEMJUqTbl8mNwcHDA0dFRt16Era0tiYmJREVF6dWLjIzU9QrY2toa\n9AZERUXp9RwYqxMZGYm5uTl58uQx6Jkwth8hhBDic/dJJQ1RUVHcu3cPOzs7AMqWLUvmzJkJCAjQ\n1blz5w5hYWF4eHgA4O7uTlhYGHfu3NHVCQgIwMLCgrJly+rqHDx4UG9fAQEBlCtXjsyZM5MlSxbK\nli2rt5+kOkn7EUIIIT53ySYNx48fT/M7VsbFxREaGkpoaCivXr3i9u3bhIaGcuvWLeLi4hg9ejTB\nwcHcuHGDwMBA2rVrh42NDV5eXgDkypWLb7/9ljFjxnDw4EHOnj1Lz549KVmypG6MQp06dShevDi9\nevXi7NmzHDx4kDFjxtC5c2dy5swJgI+PD3fv3mX48OGEhYWxbNkyVq5cSb9+/XSx9u3bl5UrV7Js\n2TLCwsLw9fXl/v37+Pj4pOkxEkIIITKKZKdc5smTh/nz5+Pt7Q1AhQoVGD9+PI0aNUq1nQcGBtKk\nSROD7e3bt2f69Ol07NiR0NBQYmNjsbOzo0aNGowaNUpvJkR8fDw//fQT69ev11vc6c06SYs7HTp0\nSG9xpzdXsDx8+DAjR47ULe70ww8/GF3caebMmYSHh1O8eHEmTJhAtWrVUu14GCNTLkVakSmXQghT\nJZs05M+fn99++40uXboAkDt3bhYsWKBLIsTHIUmDSCuSNAghTJXs7IlSpUoxa9YsXr58qevGDw5+\n9xeYJBVCCCHE5ynZnoYzZ87QpUsXbt269briG1MPk21Mo0nzcRBfGulpEGlFehqEEKZKtqehXLly\nnDlzhps3bxIREUHDhg0ZNmwYX3/99ceMTwghhBAZRIqLO5mZmeHi4oKLiwve3t7Uq1ePSpUqfazY\nhBBCCJGBqF4RcsGCBXr/f/bsGQBZs2ZN3YiEEEIIkSGZtLjTnTt36Nu3L66uruTLl498+fLh6upK\nv379uH37dlrFKIQQQogMQHVPw5UrV2jQoAEPHz6kRo0auLm5oSgKly5dYuXKlezevZvdu3dTqFCh\ntIxXCCGEEOlEddLw66+/kpCQwIEDB3TLLycJCQmhRYsW/Prrr/znP/9J9SCFEEIIkf5UX544dOgQ\nvXr1MkgY4PU9ILp3787ff/+dqsEJIYQQIuNQnTQ8f/4crVabbHnu3Ll5/vx5qgQlhBBCiIxHddJQ\nokQJ1qxZQ3x8vEHZixcvWLt2LSVKlEjV4IQQQgiRcage0/DDDz/QuXNn6tSpQ/fu3SlSpAgAly5d\nYuHChfz7778sW7YszQIVQgghRPpKdhlpY1avXs2YMWN48OABGo0GAEVRyJs3L+PHj6dt27ZpFuiX\nSpaRFmlFlpEWQpjKpKQBICEhgVOnTnHz5k0AnJycqFChApkzZ06TAL90kjSItCJJgxDCVKovTyTJ\nnDkzlStXpnLlymkRjxBCCCEyKJNWhBRCCCHEl0uSBiGEEEKoIkmDEEIIIVSRpEEIIYQQqkjSIIQQ\nQghVVCcN3bp1Y9++fbx69Sot4xFCCCFEBqU6aTh48CBt2rShWLFijBw5kpCQkLSMSwghhBAZjOqk\nISwsjFWrVlGjRg2WLl1KnTp1qFy5MjNmzODOnTtpGaMQQgghMgCTV4QEiIuLY8uWLaxdu5bDhw8D\nUK1aNdq1a0fTpk3Jnj17qgf6pZIVIUVakRUhhRCmeq+k4U337t1j1KhRbNq0CY1Gg6WlJV5eXvTp\n04eyZcumVpxfLEkaRFqRpEEIYSqTl5FOcuvWLdatW8eaNWu4ePEi1tbWtG7dmixZsrBmzRo2bNjA\npEmT6N69e2rGK4QQQoh0YlJPQ2xsLFu2bGH16tUcP34cc3Nz6tevT/v27alfvz7m5q9zkBcvXtCt\nWzeOHz9OWFhYmgX/JZCeBpFWpKdBCGEq1T0NXbp0Yffu3Tx//pxy5coxadIkWrduTe7cuQ3qZsmS\nhSZNmuDv75+qwQohhBAi/ahOGk6cOEGvXr1o3749bm5u76xfu3ZtNm/e/EHBCSGEECLjUJ00/PPP\nP2TKpH4BSRsbG2rVqvVeQQkhhBAi41GdBVy5coUNGzYkW75hwwYuX76cKkEJIYQQIuNRnTT88ssv\nrFq1KtnytWvXMnbs2FQJSgghhBAZj+qk4eTJk9SsWTPZ8urVqxMcbNpI/yNHjtCuXTuKFy+OVqtl\nxYoVeuWKojBx4kSKFSuGvb09jRs35sKFC3p1YmJi6NGjB05OTjg5OdGjRw9iYmL06pw7dw5PT0/s\n7e0pXrw4fn5+KIr+pJEtW7bg4eGBra0tHh4eBoM41cQihBBCfM5UJw2xsbFYWVklW54tWzaio6NN\n2vmTJ08oUaIEkyZNImvWrAblM2fOZO7cufj5+XHgwAFsbGxo0aIFjx8/1tXp1q0boaGhrFu3jvXr\n1xMaGkrPnj115Y8ePaJFixbY2tpy4MABJk2axOzZs5kzZ46uTnBwMF27dsXb25vAwEC8vb357rvv\nOHnypEmxCCGEEJ8z1es0uLu7U7p0aRYtWmS0/Pvvv+fMmTOcPn36vQLJly8fkydPpmPHjsDrX/bF\nihWje/fuDBkyBIBnz57h6urKuHHj8PHxISwsDA8PD3bt2kXlypUBCAoKolGjRpw4cQJXV1cWLVrE\nL7/8wsWLF3WJyZQpU1i8eDHnz59Ho9Hg4+NDdHS03myPZs2akTdvXhYtWqQqlrQi6zSItCLrNAgh\nTKW6p6FVq1Zs2rSJ2bNnk5iYqNuemJjInDlz2LRpE61atUq1wG7cuEF4eDh16tTRbcuaNStVq1bl\n+PHjwOseguzZs+Ph4aGrU7lyZaysrPTqVKlSRa8no27duty7d48bN24Ar6eTvrmfpDpJbaiJRQgh\nhPjcqZ5yOWjQII4ePcqYMWOYOXMmrq6uAFy6dImoqCiqV6+u+xWeGsLDw4HXUzffZGNjw7179wCI\niIjA2toajUajK9doNOTNm5eIiAhdHUdHR4M2ksoKFixIeHi40f0ktaEmFmMuXbqk7skKkQ7k/BTC\nNEnfe18y1UlDlixZ2LRpE8uXL2fr1q1cu3YNRVEoW7YsTZs2pVOnTiat46DWmwkBvL5s8XaS8LZ3\n1UkaBPmuOm9vU1PnTalygu2VyxMibcgHoBDCVCbdsCpTpkx07tyZzp07p1U8OnZ2dsDr3oD8+fPr\ntkdGRup+8dva2hIZGan35a0oClFRUXp1knoM3mwD/q/nwM7OzmidN8vfFYsQQgjxuUv9roFU4uzs\njJ2dHQEBAbpt8fHxBAUF6cYwuLu7ExcXpzfVMzg4mCdPnujVCQoKIj4+XlcnICAABwcHnJ2dAahU\nqZLefpLqJLWhJhYhhBDic2dST8OhQ4f473//y/Xr14mOjjZY60Cj0ehNU3yXuLg4rl69CsCrV6+4\nffs2oaGh5M6dmwIFCtC7d2+mTZuGq6srRYoUYerUqVhZWdG6dWsA3NzcqFevHoMGDWLmzJkoisKg\nQYNo0KCBruu1devW+Pn50adPH4YMGcLly5eZMWMGw4YN0/VO9OrVC09PT6ZPn46Xlxfbtm0jMDCQ\nXbt26Z7Xu2IRQgghPneqp1zOnz+fESNGkCdPHipUqECuXLmM1luwYIHqnQcGBtKkSROD7e3bt2fe\nvHkoisKkSZNYunQpMTExVKhQgalTp1KiRAld3ejoaHx9fdm5cycAjRo1YvLkyWi1Wl2dc+fOMWTI\nEE6fPo1Wq8XHxwdfX1+98Qhbtmxh/PjxXL9+HRcXF0aPHk3Tpk115WpiSQsy5VKkFZlyKYQwleqk\noVSpUjg7O7NhwwYsLS3TOi7x/0nSINKKJA1CCFOpHtMQFRVFq1atJGEQQgghvlCqk4YyZcpw+/bt\ntIxFCCGEEBmY6qTht99+Y8WKFRw5ciQt4xFCCCFEBqV69sTUqVPRarU0adIENzc3ChQoYLCYk0aj\nSfH22UIIIYT4dKlOGkJDQ9FoNDg4OPDo0SPOnTtnUCel1RGFEEII8WlTnTScP38+LeMQQgghRAaX\nYVeEFEIIIUTGYlLS8OrVKzZu3MgPP/xAx44ddZcoYmNj2bp1q8H9G4QQQgjx+VCdNDx69IiGDRvy\n/fffs3btWnbu3Km78ZOVlRW+vr7Mnz8/zQIVQgghRPpSnTSMGzeOf/75h1WrVhEaGqp33wlzc3Oa\nNGnCnj170iRIIYQQQqQ/1UmDv78/3bt3p2HDhgZTLQGKFCnCrVu3UjU4IYQQQmQcqpOG6OhoChcu\nnGy5oii8ePEiVYISQgghRMajOmkoUKAAFy5cSLY8KCgoxaRCCCGEEJ821UlD69atWbZsGceOHdNt\nS1rMadGiRWzdupX27dunfoRCCCGEyBBUL+40ePBggoODady4MW5ubmg0GkaOHEl0dDR3796lYcOG\n9OrVKy1jFUIIIUQ6Ut3TkCVLFjZs2MCcOXMoUKAAhQoV4unTpxQrVow5c+awcuVKowMkhRBCCPF5\n0MTExCjvribSS8k/gtM7BPGZOtfHPb1DEEJ8YqRrQAghhBCqqB7T0KJFi3fW0Wg0bNy48YMCEkII\nIUTGpDppePbsmcGtrxMTE7l58ybh4eG4uLhgZ2eX6gEKIYQQImNQnTTs2rUr2bItW7YwbNgwpkyZ\nkipBCSGEECLjSZUxDc2aNaNly5aMGDEiNZoTQgghRAaUagMh3dzcOHXqVGo1J4QQQogMJtWShv37\n95MjR47Uak4IIYQQGYzqMQ3Tpk0zuj02NpbDhw9z5swZfvzxx1QLTAghhBAZi+qkYfz48Ua358iR\nAxcXF37//Xe6dOmSaoEJIYQQImNRnTRERkYabNNoNLJ0tBBCCPGFUJ00mJmZpWUcQgghhMjgVCcN\n9+7de68dODg4vNfjhBBCCJGxqE4aSpQoYbAipBoPHz40+TFCCCGEyHhUJw0zZsxg4cKF3Lhxg1at\nWlGkSBEUReHy5cts3LiRggUL0q1bt7SMVQghhBDpSPUoxkePHhEXF8fp06f5/fff6du3L/369WPG\njBmcPHmSR48eERsbS+fOnfX+PsTEiRPRarV6f0WLFtWVK4rCxIkTKVasGPb29jRu3JgLFy7otRET\nE0OPHj1wcnLCycmJHmq7BiQAABOCSURBVD16EBMTo1fn3LlzeHp6Ym9vT/HixfHz80NR9O8YvmXL\nFjw8PLC1tcXDwwN/f/8Pem5CCCHEp0Z10rBgwQJ8fHzImzevQZmtrS0+Pj789ddfqRocgKurK2Fh\nYbq/o0eP6spmzpzJ3Llz8fPz48CBA9jY2NCiRQseP36sq9OtWzdCQ0NZt24d69evJzQ0lJ49e+rK\nHz16RIsWLbC1teXAgQNMmjSJ2bNnM2fOHF2d4OBgunbtire3N4GBgXh7e/Pdd99x8uTJVH++Qggh\nREZl0pTLxMTEZMsTExN58OBBqgT1JnNzc6N3z1QUhXnz5vHDDz/QrFkzAObNm4erqyvr16/Hx8eH\nsLAw9u3bx65du/Dw8ADg999/p1GjRly6dAlXV1fWrVvHs2fPmDdvHlmzZqVEiRJcvHiRP/74g379\n+qHRaJg3bx41atRgyJAhwOslswMDA5k3bx6LFi1K9ecshBBCZESqexpKlizJokWLuH37tkHZrVu3\nWLRoEaVKlUrV4ACuX79O8eLFKVOmDF27duX69esA3Lhxg/DwcOrUqaOrmzVrVqpWrcrx48eB1z0E\n2bNn1yUMAJUrV8bKykqvTpUqVciaNauuTt26dbl37x43btwA4MSJE3r7SaqT1IYQQgjxJVDd0/Db\nb7/RsmVLKlWqROPGjSlcuDAajYZLly6xY8cONBoNixcvTtXgKlasyB9//IGrqyuRkZFMmTKF+vXr\nc+zYMcLDwwGwsbHRe4yNjY1uemhERATW1tZ6sz40Gg158+YlIiJCV8fR0dGgjaSyggULEh4ebnQ/\nSW0IIYQQXwLVSYOHhwd79+5l3LhxbN++nfj4eAAsLS2pXbs2o0aNSvWehm+++Ubv/xUrVqRs2bKs\nXLmSSpUqARhMA1UUxSBJeNu76iQNgnxXnXdNQb106VKK5UKkJzk/hTCNq6treoeQ7lQnDfB6rYZV\nq1bx8uVLIiIiUBQFOzs7zM1Naua9Zc+enWLFinH16lW8vLyA170B+fPn19WJjIzU9QrY2toSGRmp\n9wWvKApRUVF6dd7uMUhaMjupjp2dndE6b/c+vC1VTrC9wR/ehhBGyAegEMJU73XjCHNzc6ysrLC3\nt/9oCQNAfHw8ly5dws7ODmdnZ+zs7AgICNArDwoK0o1hcHd3Jy4ujuDg//viDQ4O5smTJ3p1goKC\ndD0n8P/au/egqMo+DuDfhRFQLkKKiFwSURDUQg3QTBFEBZIoFkcRhSxDXfOGQ5qjOaOWF0YkFBFB\nvCJIhkZp4wURsDTFwBuNoIS3UoEVBIIk2PcPX/d1Q/HAy7Ir+/3MMMN5znPO/g7/8N3nPOc5QGZm\nJszNzfH6668DAJydnRU+52mfZ+dKEBERdXQtCg35+fkICAiAubk5+vTpg9OnTwMAysvLERgYiJyc\nnDYtbtmyZTh9+jRKSkqQm5uLkJAQ/PXXXwgMDIRIJMLs2bMRFRWF9PR0FBQUQCKRQF9fHwEBAQCe\nPOXg6emJhQsX4vz58zh37hwWLlyI8ePHy79lBQQEoHPnzpBIJCgoKEB6ejqioqIgkUjkoxOzZs1C\ndnY2IiMjUVhYiMjISOTk5GD27Nlter1ERETqTHBoyM3NhZeXF65duwZ/f3+FxY+6deuGiooK7N69\nu02L++OPPzBjxgw4Oztj2rRp0NHRwfHjx2FtbQ0AmD9/PiQSCcLDw+Hu7o579+4hLS0NhoaG8nPE\nx8dj4MCB8Pf3h1gsxsCBAxEXFyff37VrVxw8eBB//vkn3N3dER4eLl+46ilXV1ckJiYiOTkZI0aM\nQEpKChITE/HWW2+16fUSERGpM1FFRYXs5d0APz8/lJaW4sSJE6itrUXfvn1x6NAhuLm5AXiyeuP+\n/fuRn5+v1II1zYAtnNNAynFV4qLqEojoFdOikYapU6eiS5cuz31qwMLCQv4YJBEREXU8gkODSCSC\ntrb2C/ffv38fenp6bVIUERERqR/BoeHNN9/EsWPHnruvvr4eBw4cgIsLhzuJiIg6KsGhISwsDJmZ\nmViwYAEKCgoAAKWlpTh16hT8/PxQXFyMsLAwpRVKREREqiV4IiQApKSkYPHixaiqqlJYMMnAwABR\nUVHw9/dXWqGaihMhSVk4EZKIWqpFoQEAqqurkZGRgRs3bqCxsRE2NjYYO3YsjIyMlFWjRmNoIGVh\naCCilhK0nGNdXR1iYmIwdOhQjB49Wv4qaiIiItIcguY06OnpISIiArdu3VJ2PURERKSmBE+EHDBg\nAEpKSpRYChEREakzwaHhiy++wM6dO5GRkaHMeoiIiEhNCX5FZWxsLExMTDBx4kRYW1ujd+/eTRZz\nEolESE5ObvMiiYiISPUEh4ZLly5BJBLB3Nwc9fX1KCoqatLnectLExERUccgODQ8XdCJiIiINJPg\nOQ1ERESk2ZoNDYsWLUJeXp5C28OHD9HQ0KDUooiIiEj9NBsaEhMTcf36dfm2VCqFra0tTp8+rfTC\niIiISL20+PaETNaiVaeJiIiog+CcBiIiIhKEoYGIiIgEeekjlyUlJbhw4QIA4NGjRwCAoqIiGBgY\nPLf/0KFD27A8IiIiUhfNvhrbxMSkyYJNMpnsuYs4PW2XSqVtX6UG46uxSVn4amwiaqlmRxpiYmLa\nqw4iIiJSc82GhilTprRXHURERKTmOBGSiIiIBGFoICIiIkEYGoiIiEgQhgYiIiIShKGBiIiIBGFo\nICIiIkEYGoiIiEgQhgYiIiIShKGBiIiIBGFoaKGEhAS88cYbMDMzg5ubG37++WdVl0RERNQuGBpa\nIC0tDUuWLMGiRYuQnZ0NFxcXTJw4Ebdv31Z1aURERErH0NACMTExmDJlCkJCQmBvb4+IiAiYmZkh\nMTFR1aUREREpXbMvrKL/efz4MfLz8zF37lyFdg8PD/zyyy9K+1y+vpiIiNQFRxoEKi8vR0NDA0xN\nTRXaTU1N8eDBAxVVRURE1H4YGlpIJBIpbMtksiZtREREHRFDg0DdunWDtrZ2k1GFsrKyJqMPRERE\nHRFDg0A6OjpwcnJCZmamQntmZiZcXV1VVBUREVH74UTIFpgzZw5mzpyJoUOHwtXVFYmJibh37x6m\nT5+u6tKIiIiUjiMNLeDv7481a9YgIiICI0eOxNmzZ5Gamgpra2tVl6bxuOgWaYKffvoJkydPhoOD\nA4yNjZGUlKTqkkjDMDS00IwZM3D58mU8ePAAWVlZGDFihKpL0nhcdIs0RU1NDRwdHbF27Vp07txZ\n1eWQBhJVVFTIVF0E0f9jzJgxGDBgAKKjo+VtQ4YMgZ+fH1asWKHCyoiUx8LCAuvXr0dQUJCqSyEN\nwpEGeqU9XXTLw8NDoV3Zi24REWkihgZ6pXHRLSKi9sPQQB0CF90iIlI+hgZ6pXHRLSKi9sPQQK80\nLrpFRNR+uLgTvfK46BZpiurqahQXFwMAGhsbcefOHVy6dAkmJiawsrJScXWkCfjIJXUICQkJ+Prr\nr3H//n04ODjgq6++4hoa1OHk5OTA19e3SXtgYCBiY2NVUBFpGoYGIiIiEoRzGoiIiEgQhgYiIiIS\nhKGBiIiIBGFoICIiIkEYGoiIiEgQhgYiIiIShKGBiNROXV0djI2NsXHjRlWXQkTPYGggamfGxsaC\nfpKSklRdarO2bNmC/fv3t/i4nJwcTJs2Dfb29jA1NUWfPn3g7++P/fv3o7GxUQmVElFb4TLSRO0s\nLi5OYXvnzp3Izc3F5s2bFdrV/d0ZsbGxsLOzw6RJkwQfs3LlSkRGRsLW1hYhISGwsrJCRUUFMjMz\nMXPmTJSWluLTTz9VYtVE9P9gaCBqZ//+J3vq1Cn8+uuvLfrnK1RtbS06d+7c5udtjZSUFERGRsLf\n3x9xcXHo1KmTfN/cuXNx9uxZ3Lx5U4UVEtHL8PYEkZqLjIzE2LFjYWNjAzMzM4wYMQIpKSlN+tnZ\n2WHKlCk4efIk3N3dYWZmhq1btwIAGhoasGbNGjg4OMDc3Bze3t7Iy8uDp6cnxGKxwnnq6uqwZs0a\nDBkyBD169ED//v0RHh6OR48eKXzW7du3kZGRIb+d4uzs/MJrkMlkWLt2LUxNTbFp0yaFwPDUsGHD\nmg1OZWVlWLp0KYYPHw4LCwtYWlrCz88Pubm5TfqmpqbCzc0NlpaWsLa2xttvv43IyEj5/sePH8uv\n0czMDLa2tvDy8sKRI0de+PlExJEGIrW3efNm+Pr6QiwWQyaTIT09HbNmzYJMJkNgYKBC32vXruGj\njz7C9OnTERISgt69ewMAli9fji1btmD8+PHw9PREYWEhxGIxDAwM0LVrV/nxDQ0NmDRpEs6fP4/g\n4GD0798fRUVF2L59Oy5fvozDhw9DW1sbERERCAsLQ8+ePTFv3jwAgJGR0QuvoaCgACUlJfj444+h\nr6/fqr9DUVERDh8+DD8/P9jY2EAqlWLnzp3w9fVFdnY2+vXrBwA4evQoQkNDMWbMGAQHBwMACgsL\ncebMGfm5Vq1ahc2bN2P69OlwcnJCVVUVLl68iAsXLsDHx6dV9RFpAoYGIjV35coVdOnSRb49a9Ys\n+Pj4YNOmTU1Cw40bN5CWlgYPDw952927dxEXF4cJEyZg79698nY7OzuEh4fL/9kCwL59+5CTk4Mj\nR45g2LBh8nZXV1cEBwfj+++/x/vvvw8/Pz8sW7YMPXv2FHRbpbCwEAAwYMCAlv8B/mvw4MHIy8uD\nltb/BkiDg4Ph7OyMbdu2ISIiAsCT0NC9e3d88803Cn2fdfToUfj6+iqMPhDRy/H2BJGaexoY6uvr\n8fDhQ0ilUowaNQq//fYb6urqFPra2NgoBAYAOHnyJBoaGvDJJ58otAcHBzf51n/o0CE4ODigX79+\nKC8vl/8MHz4cOjo6yM7ObtU1VFVVAQAMDAxadTwA6OnpyUNAXV0dpFIptLS04OTkhIsXL8r7GRoa\n4tGjR83WamhoiKtXr+L3339vdT1EmogjDURq7rvvvsOGDRtw9epVNDQ0KOyrqqqCnp6efNvGxqbJ\n8bdv3wYA2NraKrTr6urC0tJSoe369eu4efNmk75PlZaWtuoaDA0NAQDV1dWtOh54cutkw4YN2Lt3\nL27duqWwz97eXv57aGiofETEwsICbm5ueO+99+Dl5SXvs2zZMoSEhGDw4MFwdHSEh4cHAgIC4OTk\n1Or6iDQBQwORGsvKysKHH36Id955B1FRUejZsyc6deqEw4cPIz4+vsm6Bs8GCCFkMpnCdmNjIwYN\nGoRVq1Y9t3/37t1bdgH/ZWdnB+DJ3IbWWrduHdavX49p06Zh9OjRMDExgZaWFtatW4eysjJ5PwsL\nC5w5cwYnT55ERkYGTpw4gX379uHdd9+Vr33h7u6O/Px8/Pjjj8jMzERSUhJiYmKwevVqzJkzp9U1\nEnV0DA1EauzQoUMwNDREWlqawhMHx48fF3wOKysrAE/mOzw7svD333/j7t278v3Ak5GK4uJiuLm5\nQSQSNXvel+1/lqOjI3r37o309HSsXLlSYY6GUAcPHoSnpyc2bdqk0L5ixYomfXV1deHt7Q1vb2/I\nZDIsXboUsbGxyM/Pl48mvPbaawgKCkJQUBBqamrwwQcfYO3atZBIJC26NiJNwjkNRGpMW1sbABRu\nS5SVlT33kcsX8fDwgJaWFuLj4xXad+/ejZqaGoU2sViMO3fuYNeuXU3OU19fj8rKSvm2vr4+Kioq\nBNUgEomwZMkSPHjwAPPnz8c///zTpM+5c+eQmpr6wnNoa2s3GRnJzs5WmM8AAFKptMlnDxo0CADk\n9f67j76+Pvr27YuamhrU19cLuiYiTcSRBiI15uXlhYSEBIjFYojFYkilUuzYsQO9evVCeXm5oHNY\nWFggNDQUW7duxaRJkzB27FgUFhbi22+/hbW1tcK36qlTp+KHH37AggULkJWVhWHDhkEmk6G4uFg+\nt2LChAkAACcnJ6SmpiIiIgJ9+vSBoaEhxo0b98I6Jk+ejGvXrmHjxo3Iy8uDWCyGlZUVKisrkZWV\nhePHj2P16tUvPN7b2xsbN27EnDlz4OLigsLCQuzZswf29vYKt2lCQ0NRW1uLkSNHolevXrh79y7i\n4+NhaWkJFxcXee1jxoyBk5MTTExMcOnSJaSkpGDChAnQ0dER9Hcl0kQMDURqzNPTE9HR0YiOjsbn\nn38OS0tLzJs3D506dUJYWJjg83z55ZcwNDTEnj17kJWVBScnJxw4cAASiQS6urryftra2khOTkZs\nbCySk5Nx5MgR6OrqwtraGkFBQQoLOC1fvhzl5eWIjo5GVVUV+vXr12xoAJ7cSnB3d8e2bduwY8cO\nSKVSGBkZYfDgwUhISIC/v/8Lj128eDEeP36MtLQ0pKWlwdHREXv27MGuXbtw+fJleb/AwEAkJSVh\n+/btqKysRI8ePeDj44PFixfLb4tIJBIcO3YMmZmZqKurg5WVFT777DP5mhNE9HyiiooK2cu7EVFH\nU19fDxsbGwQGBsrXOCAiag7nNBBpgNra2iZtO3bsQHV1NUaNGqWCiojoVcTbE0QaICUlBampqRg3\nbhyMjIyQm5uLlJQUDBkyBN7e3qouj4heEbw9QaQBcnNzsWrVKly5cgWVlZUwNTWFj48Pli9fDmNj\nY1WXR0SvCIYGIiIiEoRzGoiIiEgQhgYiIiIShKGBiIiIBGFoICIiIkEYGoiIiEgQhgYiIiIS5D9h\nMFA8kEGFrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b832208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cat_1--OK, cat_2--Not, cat_3--Not, cat_4--Not,cat_5--Not,cat_6--Not,cat_7-- not,cat_8--not,cat_9--Ok,cat_10--not, cat11--not,cat_12--not, cat_13--not,cat_14--not,cat_15--not,cat_16--not,cat_18-not,cat_19--not,cat_20\n",
    "sns.countplot(x=\"cat_19\", data=X_total_cat)\n",
    "plt.title('Frequency of the target class in the Training Set')\n",
    "plt.ylabel('Frequency of the Target')\n",
    "plt.xlabel('Target Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    260473\n",
      "Name: cat_17, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_total_cat['cat_17'].value_counts().sort_values(ascending=False).head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3    149935\n",
      "1     99533\n",
      "2     11005\n",
      "Name: cat_1, dtype: int64\n",
      "10    148841\n",
      "13     58964\n",
      "1      33121\n",
      "2       5301\n",
      "11      3751\n",
      "3       2999\n",
      "15      2162\n",
      "7        832\n",
      "5        366\n",
      "6        339\n",
      "14       311\n",
      "8        295\n",
      "12       258\n",
      "4        201\n",
      "9        198\n",
      "20       108\n",
      "36        72\n",
      "47        72\n",
      "42        68\n",
      "24        65\n",
      "Name: cat_2, dtype: int64\n",
      "1     199586\n",
      "8      44370\n",
      "6      15622\n",
      "9        447\n",
      "7        338\n",
      "11        53\n",
      "3         22\n",
      "4         13\n",
      "2         10\n",
      "10        10\n",
      "5          2\n",
      "Name: cat_3, dtype: int64\n",
      "0    260470\n",
      "1         3\n",
      "Name: cat_4, dtype: int64\n",
      "0    260406\n",
      "3        47\n",
      "1        20\n",
      "Name: cat_5, dtype: int64\n",
      "0    260472\n",
      "2         1\n",
      "Name: cat_6, dtype: int64\n",
      "0     259939\n",
      "2        227\n",
      "1         80\n",
      "4         49\n",
      "6         44\n",
      "5         25\n",
      "19        16\n",
      "22        16\n",
      "14        16\n",
      "30        15\n",
      "18        13\n",
      "24        11\n",
      "28        10\n",
      "3          6\n",
      "20         2\n",
      "13         1\n",
      "12         1\n",
      "10         1\n",
      "21         1\n",
      "Name: cat_7, dtype: int64\n",
      "0    260427\n",
      "1        45\n",
      "3         1\n",
      "Name: cat_8, dtype: int64\n",
      "0    222676\n",
      "1     37797\n",
      "Name: cat_9, dtype: int64\n",
      "0      260261\n",
      "1         181\n",
      "2          11\n",
      "3           5\n",
      "4           4\n",
      "6           2\n",
      "435         1\n",
      "8           1\n",
      "254         1\n",
      "166         1\n",
      "378         1\n",
      "884         1\n",
      "10          1\n",
      "21          1\n",
      "31          1\n",
      "Name: cat_10, dtype: int64\n",
      "0    260451\n",
      "1        22\n",
      "Name: cat_11, dtype: int64\n",
      "0    260466\n",
      "2         6\n",
      "1         1\n",
      "Name: cat_12, dtype: int64\n",
      "0      260201\n",
      "1         114\n",
      "9          69\n",
      "6          59\n",
      "5          12\n",
      "2           7\n",
      "4           5\n",
      "36          1\n",
      "993         1\n",
      "184         1\n",
      "421         1\n",
      "480         1\n",
      "289         1\n",
      "Name: cat_13, dtype: int64\n",
      "0     260355\n",
      "1         96\n",
      "2         13\n",
      "4          2\n",
      "15         1\n",
      "13         1\n",
      "25         1\n",
      "3          1\n",
      "22         1\n",
      "5          1\n",
      "14         1\n",
      "Name: cat_14, dtype: int64\n",
      "0    260453\n",
      "1        19\n",
      "2         1\n",
      "Name: cat_15, dtype: int64\n",
      "0    260224\n",
      "1       238\n",
      "2         7\n",
      "8         1\n",
      "4         1\n",
      "3         1\n",
      "5         1\n",
      "Name: cat_16, dtype: int64\n",
      "0    260473\n",
      "Name: cat_17, dtype: int64\n",
      "0    260472\n",
      "1         1\n",
      "Name: cat_18, dtype: int64\n",
      "0    260230\n",
      "1       243\n",
      "Name: cat_19, dtype: int64\n",
      "511    118998\n",
      "1       20849\n",
      "510     14282\n",
      "2        5809\n",
      "509      3247\n",
      "3        3167\n",
      "4        2652\n",
      "5        2213\n",
      "6        1875\n",
      "7        1739\n",
      "8        1456\n",
      "9        1371\n",
      "10       1289\n",
      "11       1199\n",
      "12       1106\n",
      "13        947\n",
      "14        888\n",
      "508       798\n",
      "15        777\n",
      "16        713\n",
      "Name: cat_20, dtype: int64\n",
      "511    118324\n",
      "1       20032\n",
      "510     14435\n",
      "2        9596\n",
      "3        6059\n",
      "4        5066\n",
      "5        4677\n",
      "6        4420\n",
      "7        4345\n",
      "8        4077\n",
      "9        4057\n",
      "10       4032\n",
      "11       3914\n",
      "12       3825\n",
      "13       3699\n",
      "14       3653\n",
      "15       3495\n",
      "17       3485\n",
      "16       3447\n",
      "18       3363\n",
      "Name: cat_21, dtype: int64\n",
      "255    228965\n",
      "1        1359\n",
      "2        1044\n",
      "3         715\n",
      "4         592\n",
      "5         539\n",
      "6         489\n",
      "7         456\n",
      "9         432\n",
      "8         428\n",
      "10        399\n",
      "12        380\n",
      "11        374\n",
      "14        339\n",
      "13        336\n",
      "15        323\n",
      "16        318\n",
      "17        310\n",
      "22        290\n",
      "18        287\n",
      "Name: cat_22, dtype: int64\n",
      "255    178250\n",
      "1        6350\n",
      "2        3607\n",
      "3        3094\n",
      "4        2927\n",
      "6        2893\n",
      "9        2885\n",
      "10       2851\n",
      "7        2847\n",
      "5        2846\n",
      "12       2803\n",
      "11       2793\n",
      "14       2773\n",
      "13       2771\n",
      "8        2754\n",
      "17       2741\n",
      "16       2712\n",
      "15       2686\n",
      "18       2680\n",
      "19       2648\n",
      "Name: cat_23, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in X_total_cat.columns:\n",
    "    print(X_total_cat[i].value_counts().sort_values(ascending=False).head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cat_less=['cat_4','cat_5','cat_6','cat_7','cat_8','cat_10','cat_11','cat_15','cat_17','cat_18']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10    148841\n",
      "13     58964\n",
      "1      33121\n",
      "2       5301\n",
      "11      3751\n",
      "Name: cat_2, dtype: int64\n",
      "0    260224\n",
      "1       238\n",
      "2         7\n",
      "8         1\n",
      "4         1\n",
      "3         1\n",
      "5         1\n",
      "Name: cat_16, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_total_cat['cat_2'].value_counts().sort_values(ascending=False).head(5))\n",
    "print(X_total_cat['cat_16'].value_counts().sort_values(ascending=False).head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#X_total_cat['target']=data_train['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#DF=prox_matrix(X_total_cat, 'target',list(X_total_cat.columns), ['cat_2','cat_3','cat_7','cat_10','cat_13','cat_14','cat_20','cat_21','cat_22','cat_23'],trees = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_cat.drop(cat_less,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#interesting_cols=['cat_2','cat_3','cat_7','cat_10','cat_13','cat_16','cat_14','cat_20','cat_21','cat_22','cat_23']\n",
    "threshold = 6000 # Anything that occurs less than this will be removed.\n",
    "for col in X_total_cat.columns:\n",
    "    vc = X_total_cat[col].value_counts()\n",
    "    vals_to_remove = vc[vc <= threshold].index.values\n",
    "    X_total_cat[col].loc[X_total_cat[col].isin(vals_to_remove)] = 'other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature:- cat_1 has 3 unique categories\n",
      "Feature:- cat_2 has 4 unique categories\n",
      "Feature:- cat_3 has 4 unique categories\n",
      "Feature:- cat_9 has 2 unique categories\n",
      "Feature:- cat_12 has 2 unique categories\n",
      "Feature:- cat_13 has 2 unique categories\n",
      "Feature:- cat_14 has 2 unique categories\n",
      "Feature:- cat_16 has 2 unique categories\n",
      "Feature:- cat_19 has 2 unique categories\n",
      "Feature:- cat_20 has 4 unique categories\n",
      "Feature:- cat_21 has 6 unique categories\n",
      "Feature:- cat_22 has 2 unique categories\n",
      "Feature:- cat_23 has 3 unique categories\n"
     ]
    }
   ],
   "source": [
    "for col_name in X_total_cat.columns:\n",
    "    if X_total_cat[col_name].dtype=='object':\n",
    "        unique_cat=len(X_total_cat[col_name].unique())\n",
    "        print(\"Feature:- {col_name} has {unique_cat} unique categories\".format(col_name=col_name,unique_cat=unique_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255      178250\n",
      "other     75873\n",
      "1          6350\n",
      "Name: cat_23, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_total_cat['cat_23'].value_counts().sort_values(ascending=False).head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#X_total_cat.drop('cat_17',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_dummies=pd.get_dummies(X_total_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat_1_1</th>\n",
       "      <th>cat_1_2</th>\n",
       "      <th>cat_1_3</th>\n",
       "      <th>cat_2_1</th>\n",
       "      <th>cat_2_10</th>\n",
       "      <th>cat_2_13</th>\n",
       "      <th>cat_2_other</th>\n",
       "      <th>cat_3_1</th>\n",
       "      <th>cat_3_6</th>\n",
       "      <th>cat_3_8</th>\n",
       "      <th>...</th>\n",
       "      <th>cat_21_2</th>\n",
       "      <th>cat_21_3</th>\n",
       "      <th>cat_21_510</th>\n",
       "      <th>cat_21_511</th>\n",
       "      <th>cat_21_other</th>\n",
       "      <th>cat_22_255</th>\n",
       "      <th>cat_22_other</th>\n",
       "      <th>cat_23_1</th>\n",
       "      <th>cat_23_255</th>\n",
       "      <th>cat_23_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cat_1_1  cat_1_2  cat_1_3  cat_2_1  cat_2_10  cat_2_13  cat_2_other  \\\n",
       "0        0        0        1        0         1         0            0   \n",
       "1        0        0        1        0         1         0            0   \n",
       "2        0        0        1        0         1         0            0   \n",
       "3        0        0        1        0         1         0            0   \n",
       "4        0        0        1        0         1         0            0   \n",
       "\n",
       "   cat_3_1  cat_3_6  cat_3_8      ...       cat_21_2  cat_21_3  cat_21_510  \\\n",
       "0        1        0        0      ...              0         0           0   \n",
       "1        1        0        0      ...              0         0           0   \n",
       "2        1        0        0      ...              0         0           0   \n",
       "3        1        0        0      ...              0         0           0   \n",
       "4        1        0        0      ...              0         0           0   \n",
       "\n",
       "   cat_21_511  cat_21_other  cat_22_255  cat_22_other  cat_23_1  cat_23_255  \\\n",
       "0           1             0           1             0         0           1   \n",
       "1           1             0           1             0         0           1   \n",
       "2           1             0           1             0         0           1   \n",
       "3           1             0           1             0         0           1   \n",
       "4           1             0           1             0         0           1   \n",
       "\n",
       "   cat_23_other  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_total_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_dummies=X_total_dummies.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_dummies.drop(['index'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_pca=pd.concat([X_train_pca,X_test_pca])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(260473, 10)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_total_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_pca=X_total_pca.reset_index()\n",
    "X_total_pca.drop(['index'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_dummied=pd.concat([X_total_pca,X_total_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del X_total_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#cid=X_total[['connection_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total=X_total.reset_index()\n",
    "X_total.drop('index',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_total_dummied['connection_id']=X_total['connection_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(260473, 49)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_total_dummied.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train=pd.merge(X_train,X_total_dummied,how='left',on='connection_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(169307, 49)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#X_total_dummies=pd.concat([X_total_pca,X_total_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test=pd.merge(X_test,X_total_dummied,how='left',on='connection_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91166, 49)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#X_train=pd.concat([X_train_pca,X_train],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_train=data_train[['target']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier,GradientBoostingClassifier,AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\namanda\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold,KFold\n",
    "\n",
    "num_train = X_train.shape[0]\n",
    "num_test = X_test.shape[0]\n",
    "SEED = 0 # for same randomized reproducibility\n",
    "NFOLDS = 5 # set folds for out-of-fold prediction\n",
    "skf = StratifiedKFold(n_splits=NFOLDS, random_state=SEED)\n",
    "#kf = KFold(n_splits= NFOLDS, random_state=SEED)\n",
    "\n",
    "# Class to extend the Sklearn classifier\n",
    "\n",
    "class Sklearn_Rep(object):\n",
    "    def __init__(self, clf, seed=0, params=None):\n",
    "        #params['random_state'] = seed\n",
    "        self.clf = clf(**params)\n",
    "\n",
    "    def train(self, x_train, y_train):\n",
    "        self.clf.fit(x_train, y_train)\n",
    "\n",
    "    def predict(self, x):\n",
    "        return self.clf.predict(x)\n",
    "    \n",
    "    def fit(self,x,y):\n",
    "        return self.clf.fit(x,y)\n",
    "    \n",
    "    def feature_importances(self,x,y):\n",
    "        return(self.clf.fit(x,y).feature_importances_)\n",
    "        #return(self.clf.feature_importances_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_oof(clf, x_train, y_train, x_test):\n",
    "    oof_train = np.zeros((num_train,))\n",
    "    oof_test = np.zeros((num_test,))\n",
    "    oof_test_skf = np.empty((NFOLDS, num_test))\n",
    "\n",
    "    for i,(train_index, test_index) in enumerate(skf.split(x_train,y_train)):\n",
    "        x_tr = x_train[train_index]\n",
    "        y_tr = y_train[train_index]\n",
    "        x_te = x_train[test_index]\n",
    "\n",
    "        clf.train(x_tr, y_tr)\n",
    "\n",
    "        oof_train[test_index] = clf.predict(x_te)\n",
    "        oof_test_skf[i, :] = clf.predict(x_test)\n",
    "\n",
    "    #oof_test[:] = (oof_test_skf.mean(axis=0))\n",
    "    return oof_train, oof_test_skf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Put in our parameters for said classifiers\n",
    "# Random Forest parameters\n",
    "rf_params = {\n",
    "    'n_jobs': -1,\n",
    "    'n_estimators': 1000,\n",
    "     #'warm_start': True, \n",
    "     #'max_features': 0.2,\n",
    "    'max_depth': 8,\n",
    "    #'min_samples_leaf': 2,\n",
    "    #'max_features' : 'sqrt',\n",
    "    #'verbose': 0,\n",
    "    'random_state':0\n",
    "}\n",
    "\n",
    "# Extra Trees Parameters\n",
    "et_params = {\n",
    "    'n_jobs': -1,\n",
    "    'n_estimators':500,\n",
    "    #'max_features': 0.5,\n",
    "    'max_depth': 10,\n",
    "    #'min_samples_leaf': 2,\n",
    "    'verbose': 0,\n",
    "    'random_state':0\n",
    "}\n",
    "\n",
    "# AdaBoost parameters\n",
    "ada_params = {\n",
    "    'n_estimators': 500,\n",
    "    'learning_rate' : 0.75,\n",
    "    'random_state':0\n",
    "}\n",
    "\n",
    "# gbm parameters\n",
    "gbm_params = {\n",
    "    'n_estimators': 500,\n",
    "    'learning_rate' : 0.75,\n",
    "    'random_state':0\n",
    "}\n",
    "\n",
    "# xtreme Gradient Boosting parameters\n",
    "xgb_params = {'n_estimators': 2000,\n",
    " 'max_depth': 6,\n",
    " 'objective': 'multi:softprob',\n",
    " 'nthread': -1,\n",
    " 'scale_pos_weight':1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create objects that represent our models\n",
    "rf = Sklearn_Rep(clf=RandomForestClassifier, seed=SEED, params=rf_params)\n",
    "#et = Sklearn_Rep(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\n",
    "#ada = Sklearn_Rep(clf=AdaBoostClassifier, seed=SEED, params=ada_params)\n",
    "#gbm = Sklearn_Rep(clf=GradientBoostingClassifier, seed=SEED, params=ada_params)\n",
    "#gb = Sklearn_Rep(clf=XGBClassifier, seed=SEED, params=xgb_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_cid=X_train['connection_id']\n",
    "X_train=X_train.drop('connection_id',axis=1)\n",
    "X_test_cid=X_test['connection_id']\n",
    "X_test=X_test.drop('connection_id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#y = pd.factorize(Y_train['target'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_oof_train, rf_oof_test_nfolds = get_oof(rf, X_train.values, Y_train.values.ravel(), X_test.values) # Random Forest#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    98868\n",
       "2    36854\n",
       "1    33585\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr=rf_oof_test_nfolds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_mean=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_mean=rf_oof_test_nfolds.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_mean['target_rf']=rf_mean.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "axis= 0\n",
    "u, indices = np.unique(arr, return_inverse=True)\n",
    "rf_oof_test=u[np.argmax(np.apply_along_axis(np.bincount, axis, indices.reshape(arr.shape),None, np.max(indices) + 1), axis=axis)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  0.  0. ...,  1.  0.  1.]\n"
     ]
    }
   ],
   "source": [
    "print(rf_oof_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "et_oof_train, et_oof_test_nfolds = get_oof(et, X_train.values, Y_train.values.ravel(), X_test.values) # extratrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr=et_oof_test_nfolds\n",
    "axis= 0\n",
    "u, indices = np.unique(arr, return_inverse=True)\n",
    "et_oof_test=u[np.argmax(np.apply_along_axis(np.bincount, axis, indices.reshape(arr.shape),None, np.max(indices) + 1), axis=axis)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  1.]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(et_oof_test_nfolds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "et_mean=et_oof_test_nfolds.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_et=pd.DataFrame(et_oof_test,columns=['target_et'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_mean['target_et']=et_mean.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target_et</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_et\n",
       "0        1.0\n",
       "1        0.0\n",
       "2        0.0\n",
       "3        0.0\n",
       "4        0.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_et.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ada_oof_train, ada_oof_test_nfolds = get_oof(ada, X_train.values, Y_train.values.ravel(), X_test.values) # adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr=ada_oof_test_nfolds\n",
    "axis= 0\n",
    "u, indices = np.unique(arr, return_inverse=True)\n",
    "ada_oof_test=u[np.argmax(np.apply_along_axis(np.bincount, axis, indices.reshape(arr.shape),None, np.max(indices) + 1), axis=axis)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ada_mean=ada_oof_test_nfolds.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_mean['target_ada']=ada_mean.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    54671\n",
       "2.0    14291\n",
       "1.0    12922\n",
       "0.4     4120\n",
       "1.6     1659\n",
       "0.8     1481\n",
       "0.2      900\n",
       "0.6      542\n",
       "1.2      493\n",
       "1.4       49\n",
       "1.8       38\n",
       "Name: target_ada, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_mean['target_ada'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_ada=pd.DataFrame(ada_oof_test,columns=['target_ada'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_rf=pd.DataFrame(rf_oof_test,columns=['target_rf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgb_oof_train, xgb_oof_test_nfolds = get_oof(gb, X_train.values, Y_train.values.ravel(), X_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1., ...,  1.,  2.,  0.],\n",
       "       [ 0.,  1.,  1., ...,  1.,  2.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  1.,  0.,  0.],\n",
       "       [ 0.,  1.,  1., ...,  1.,  2.,  0.]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_oof_test_nfolds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr=xgb_oof_test_nfolds\n",
    "axis= 0\n",
    "u, indices = np.unique(arr, return_inverse=True)\n",
    "xgb_oof_test=u[np.argmax(np.apply_along_axis(np.bincount, axis, indices.reshape(arr.shape),\n",
    "                                None, np.max(indices) + 1), axis=axis)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  1.  1. ...,  1.  2.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(xgb_oof_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_xgb=pd.DataFrame(xgb_oof_test,columns=['target_xgb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgb_mean=xgb_oof_test_nfolds.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_mean['target_xgb']=xgb_mean.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe tbody tr 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>target_xgb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_xgb\n",
       "0         0.0\n",
       "1         1.0\n",
       "2         1.0\n",
       "3         1.0\n",
       "4         1.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_xgb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gb_oof_train, gb_oof_test_nfolds = get_oof(gbm, X_train.values, Y_train.values.ravel(), X_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr=gb_oof_test_nfolds\n",
    "axis= 0\n",
    "u, indices = np.unique(arr, return_inverse=True)\n",
    "gb_oof_test=u[np.argmax(np.apply_along_axis(np.bincount, axis, indices.reshape(arr.shape),\n",
    "                                None, np.max(indices) + 1), axis=axis)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_gb=pd.DataFrame(gb_oof_test,columns=['target_gb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    63891\n",
       "0.0    27127\n",
       "2.0      148\n",
       "Name: target_xgb, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_xgb['target_xgb'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gb_mean=gb_oof_test_nfolds.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_mean['target_gb']=gb_mean.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train.shape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature PC4 : 0.161026\n",
      "2. feature PC6 : 0.102125\n",
      "3. feature PC9 : 0.100116\n",
      "4. feature PC10 : 0.097413\n",
      "5. feature PC2 : 0.084707\n",
      "6. feature cat_9_1 : 0.063921\n",
      "7. feature cat_9_0 : 0.047753\n",
      "8. feature cat_2_1 : 0.040746\n",
      "9. feature cat_22_other : 0.036844\n",
      "10. feature cat_22_255 : 0.036288\n",
      "11. feature PC7 : 0.035076\n",
      "12. feature PC1 : 0.034561\n",
      "13. feature PC8 : 0.025036\n",
      "14. feature cat_3_1 : 0.018574\n",
      "15. feature cat_20_1 : 0.017317\n",
      "16. feature PC5 : 0.015384\n",
      "17. feature cat_1_3 : 0.012852\n",
      "18. feature cat_1_2 : 0.010913\n",
      "19. feature PC3 : 0.010882\n",
      "20. feature cat_2_10 : 0.010573\n",
      "21. feature cat_2_13 : 0.007208\n",
      "22. feature cat_1_1 : 0.004989\n",
      "23. feature cat_2_other : 0.004609\n",
      "24. feature cat_21_1 : 0.004539\n",
      "25. feature cat_3_8 : 0.004200\n",
      "26. feature cat_3_6 : 0.002718\n",
      "27. feature cat_20_511 : 0.001870\n",
      "28. feature cat_23_255 : 0.001772\n",
      "29. feature cat_21_511 : 0.001159\n",
      "30. feature cat_20_other : 0.001139\n",
      "31. feature cat_3_other : 0.000846\n",
      "32. feature cat_23_other : 0.000779\n",
      "33. feature cat_23_1 : 0.000755\n",
      "34. feature cat_21_other : 0.000658\n",
      "35. feature cat_21_2 : 0.000332\n",
      "36. feature cat_21_3 : 0.000181\n",
      "37. feature cat_20_510 : 0.000044\n",
      "38. feature cat_19_other : 0.000026\n",
      "39. feature cat_19_0 : 0.000025\n",
      "40. feature cat_21_510 : 0.000013\n",
      "41. feature cat_14_0 : 0.000007\n",
      "42. feature cat_13_0 : 0.000006\n",
      "43. feature cat_13_other : 0.000005\n",
      "44. feature cat_14_other : 0.000005\n",
      "45. feature cat_16_0 : 0.000004\n",
      "46. feature cat_16_other : 0.000003\n",
      "47. feature cat_12_0 : 0.000000\n",
      "48. feature cat_12_other : 0.000000\n"
     ]
    }
   ],
   "source": [
    "importances=rf.feature_importances(X_train.values, Y_train.values.ravel())\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "x_cols=list(X_train.columns)\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(len(x_cols)):\n",
    "    #print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n",
    "    print(\"%d. feature %s : %f\" % (f + 1, x_cols[indices[f]], importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_new=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(45):\n",
    "    X_train_new[x_cols[indices[i]]]=X_train[x_cols[indices[i]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC11</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC10</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>...</th>\n",
       "      <th>cat_3_6</th>\n",
       "      <th>cat_2_2</th>\n",
       "      <th>cat_5_0</th>\n",
       "      <th>cat_20_510</th>\n",
       "      <th>cat_23_1</th>\n",
       "      <th>cat_10_0</th>\n",
       "      <th>cat_2_10</th>\n",
       "      <th>cat_1_3</th>\n",
       "      <th>cat_23_255</th>\n",
       "      <th>cat_23_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.526098</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>-0.032908</td>\n",
       "      <td>0.118693</td>\n",
       "      <td>0.361203</td>\n",
       "      <td>0.254473</td>\n",
       "      <td>-0.020681</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.079467</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.526095</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004877</td>\n",
       "      <td>-0.030896</td>\n",
       "      <td>0.118764</td>\n",
       "      <td>0.360939</td>\n",
       "      <td>0.254585</td>\n",
       "      <td>-0.020675</td>\n",
       "      <td>0.052368</td>\n",
       "      <td>-0.081447</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.526098</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>-0.032908</td>\n",
       "      <td>0.118693</td>\n",
       "      <td>0.361203</td>\n",
       "      <td>0.254473</td>\n",
       "      <td>-0.020681</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.079467</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.526098</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>-0.032908</td>\n",
       "      <td>0.118693</td>\n",
       "      <td>0.361203</td>\n",
       "      <td>0.254473</td>\n",
       "      <td>-0.020681</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.079467</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.526098</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>-0.032908</td>\n",
       "      <td>0.118693</td>\n",
       "      <td>0.361203</td>\n",
       "      <td>0.254473</td>\n",
       "      <td>-0.020681</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.079467</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2      PC11       PC8      PC10       PC5       PC9  \\\n",
       "0 -1.526098 -0.272538  0.004865 -0.032908  0.118693  0.361203  0.254473   \n",
       "1 -1.526095 -0.272538  0.004877 -0.030896  0.118764  0.360939  0.254585   \n",
       "2 -1.526098 -0.272538  0.004865 -0.032908  0.118693  0.361203  0.254473   \n",
       "3 -1.526098 -0.272538  0.004865 -0.032908  0.118693  0.361203  0.254473   \n",
       "4 -1.526098 -0.272538  0.004865 -0.032908  0.118693  0.361203  0.254473   \n",
       "\n",
       "        PC6       PC3       PC4      ...       cat_3_6  cat_2_2  cat_5_0  \\\n",
       "0 -0.020681  0.052632 -0.079467      ...             0        0        1   \n",
       "1 -0.020675  0.052368 -0.081447      ...             0        0        1   \n",
       "2 -0.020681  0.052632 -0.079467      ...             0        0        1   \n",
       "3 -0.020681  0.052632 -0.079467      ...             0        0        1   \n",
       "4 -0.020681  0.052632 -0.079467      ...             0        0        1   \n",
       "\n",
       "   cat_20_510  cat_23_1  cat_10_0  cat_2_10  cat_1_3  cat_23_255  cat_23_other  \n",
       "0           0         0         1         1        1           1             0  \n",
       "1           0         0         1         1        1           1             0  \n",
       "2           0         0         1         1        1           1             0  \n",
       "3           0         0         1         1        1           1             0  \n",
       "4           0         0         1         1        1           1             0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test_new=pd.DataFrame()\n",
    "for i in range(45):\n",
    "    X_test_new[x_cols[indices[i]]]=X_test[x_cols[indices[i]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Y_train_dummied=pd.get_dummies(Y_train['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Y_train_dummied.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#x_cols_selected=X_train_new.columns\n",
    "#X_train_new=X_train_new.values\n",
    "#Y_train_dummies=Y_train_dummied.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#X_test_new=X_test_new.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import tensorflow as tf\n",
    "#import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#n_nodes_hl1 = 500\n",
    "#n_nodes_hl2 = 400\n",
    "#n_nodes_hl3 = 300\n",
    "\n",
    "#n_classes = 3\n",
    "#batch_size = 500\n",
    "#hm_epochs = 20\n",
    "\n",
    "#x = tf.placeholder('float')\n",
    "#y = tf.placeholder('float')\n",
    "\n",
    "#hidden_1_layer = {'f_fum':n_nodes_hl1,\n",
    "                  #'weight':tf.Variable(tf.random_normal([X_train_new.shape[1], n_nodes_hl1])),\n",
    "                  #'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}\n",
    "\n",
    "#hidden_2_layer = {'f_fum':n_nodes_hl2,\n",
    "                  #'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),\n",
    "                  #'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}\n",
    "\n",
    "#hidden_3_layer = {'f_fum':n_nodes_hl3,\n",
    "                  #'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),\n",
    "                  #'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))}\n",
    "\n",
    "#output_layer = {'f_fum':None,\n",
    "                #'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),\n",
    "                #'bias':tf.Variable(tf.random_normal([n_classes])),}\n",
    "\n",
    "\n",
    "# Nothing changes\n",
    "#def neural_network_model(data):\n",
    "\n",
    "#    l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])\n",
    "#    l1 = tf.nn.relu(l1)\n",
    "\n",
    "#    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])\n",
    "#    l2 = tf.nn.relu(l2)\n",
    "\n",
    "#    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias'])\n",
    "#    l3 = tf.nn.relu(l3)\n",
    "\n",
    "#    output = tf.matmul(l3,output_layer['weight']) + output_layer['bias']\n",
    "\n",
    "#    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#def train_neural_network(x):\n",
    "#    prediction = neural_network_model(x)\n",
    "#    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))\n",
    "#    optimizer = tf.train.AdamOptimizer().minimize(cost)#\n",
    "\n",
    "#    with tf.Session() as sess:\n",
    "#        sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "#        for epoch in range(hm_epochs):\n",
    "#            epoch_loss = 0\n",
    "#            i=0\n",
    "#            while i < (X_train_new.shape[0]):\n",
    "#                start = i\n",
    "#                end = i+batch_size\n",
    "#                batch_x = np.array(X_train_new[start:end])\n",
    "#                batch_y = np.array(Y_train_dummies[start:end])\n",
    "#\n",
    "#                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,y: batch_y})\n",
    "#                epoch_loss += c\n",
    "#                i+=batch_size\n",
    "\n",
    "#            print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss)\n",
    "        #correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))\n",
    "        #accuracy = tf.reduce_mean(tf.cast(correct, 'float'))\n",
    "\n",
    "        #print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))\n",
    "#        preds= sess.run(tf.argmax(prediction,1),feed_dict={x: X_test_new})\n",
    "#        return preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#predictions=train_neural_network(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#pred_df=pd.DataFrame(predictions,columns=['pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#pred_df['pred'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#eclf1 = VotingClassifier(estimators=[('et', et), ('rf', rf), ('gbm', gbm),('ada',ada)], voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#eclf1.set_params(et=et_params,rf=rf_params,gbm=ada_params,ada=ada_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#eclf1 = eclf1.fit(X_train.values,Y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#gbm = XGBClassifier(\n",
    "# n_estimators= 1000,\n",
    "# max_depth= 5,\n",
    "# objective= 'multi:softprob',\n",
    "# nthread= -1,\n",
    "# scale_pos_weight=1)#.fit(X_train.values, Y_train.values.ravel())\n",
    "#predictions = gbm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#predictions_rf['target_rf'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sub = pd.read_csv('sample_submission.csv')\n",
    "#sub['target'] = xgb_oof_test\n",
    "#sub['target'] = sub['target'].astype(int)\n",
    "#sub.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#predictions=predictions_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_check=pd.read_csv('submission6.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    64291\n",
       "1    18026\n",
       "2     8849\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_check['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target\n",
       "0       2\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       2"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "import keras.optimizers as opt\n",
    "import keras.backend as K\n",
    "import keras.callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LossHistory(keras.callbacks.Callback):\n",
    "    \"\"\"\n",
    "    This class records the history of training.\n",
    "    \"\"\"\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.losses = []\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        self.losses.append(logs.get('loss'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_classes = 3\n",
    "Y_train = keras.utils.to_categorical(Y_train.values, num_classes)\n",
    "#y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "#print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(169307, 3)\n"
     ]
    }
   ],
   "source": [
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC11</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC10</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>...</th>\n",
       "      <th>cat_3_6</th>\n",
       "      <th>cat_2_2</th>\n",
       "      <th>cat_5_0</th>\n",
       "      <th>cat_20_510</th>\n",
       "      <th>cat_23_1</th>\n",
       "      <th>cat_10_0</th>\n",
       "      <th>cat_2_10</th>\n",
       "      <th>cat_1_3</th>\n",
       "      <th>cat_23_255</th>\n",
       "      <th>cat_23_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.526098</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>-0.032908</td>\n",
       "      <td>0.118693</td>\n",
       "      <td>0.361203</td>\n",
       "      <td>0.254473</td>\n",
       "      <td>-0.020681</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.079467</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.526095</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004877</td>\n",
       "      <td>-0.030896</td>\n",
       "      <td>0.118764</td>\n",
       "      <td>0.360939</td>\n",
       "      <td>0.254585</td>\n",
       "      <td>-0.020675</td>\n",
       "      <td>0.052368</td>\n",
       "      <td>-0.081447</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.526098</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>-0.032908</td>\n",
       "      <td>0.118693</td>\n",
       "      <td>0.361203</td>\n",
       "      <td>0.254473</td>\n",
       "      <td>-0.020681</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.079467</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.526098</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>-0.032908</td>\n",
       "      <td>0.118693</td>\n",
       "      <td>0.361203</td>\n",
       "      <td>0.254473</td>\n",
       "      <td>-0.020681</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.079467</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.526098</td>\n",
       "      <td>-0.272538</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>-0.032908</td>\n",
       "      <td>0.118693</td>\n",
       "      <td>0.361203</td>\n",
       "      <td>0.254473</td>\n",
       "      <td>-0.020681</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.079467</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2      PC11       PC8      PC10       PC5       PC9  \\\n",
       "0 -1.526098 -0.272538  0.004865 -0.032908  0.118693  0.361203  0.254473   \n",
       "1 -1.526095 -0.272538  0.004877 -0.030896  0.118764  0.360939  0.254585   \n",
       "2 -1.526098 -0.272538  0.004865 -0.032908  0.118693  0.361203  0.254473   \n",
       "3 -1.526098 -0.272538  0.004865 -0.032908  0.118693  0.361203  0.254473   \n",
       "4 -1.526098 -0.272538  0.004865 -0.032908  0.118693  0.361203  0.254473   \n",
       "\n",
       "        PC6       PC3       PC4      ...       cat_3_6  cat_2_2  cat_5_0  \\\n",
       "0 -0.020681  0.052632 -0.079467      ...             0        0        1   \n",
       "1 -0.020675  0.052368 -0.081447      ...             0        0        1   \n",
       "2 -0.020681  0.052632 -0.079467      ...             0        0        1   \n",
       "3 -0.020681  0.052632 -0.079467      ...             0        0        1   \n",
       "4 -0.020681  0.052632 -0.079467      ...             0        0        1   \n",
       "\n",
       "   cat_20_510  cat_23_1  cat_10_0  cat_2_10  cat_1_3  cat_23_255  cat_23_other  \n",
       "0           0         0         1         1        1           1             0  \n",
       "1           0         0         1         1        1           1             0  \n",
       "2           0         0         1         1        1           1             0  \n",
       "3           0         0         1         1        1           1             0  \n",
       "4           0         0         1         1        1           1             0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_train.drop('connection_id',axis=1,inplace=True)\n",
    "X_train_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_model_architecture(input_sample=None, target=None, input_dim=1000, num_batch=50, classes=1, act_func_el='relu',act_func_last='sigmoid'):\n",
    "    \n",
    "    # loss history\n",
    "    hist=LossHistory()\n",
    "    \n",
    "    # number of neurons in each layer\n",
    "    \n",
    "    l1_num_neuron=500\n",
    "    l2_num_neuron=200\n",
    "    l3_num_neuron=50\n",
    "    l4_num_neuron=100\n",
    "    l5_num_neuron=50\n",
    "    output_neuron=classes\n",
    "    \n",
    "    # define dense layers with activation functions\n",
    "    layer_1 = Dense(l1_num_neuron, activation=act_func_el,\n",
    "                    kernel_regularizer='l2', input_dim=input_dim)\n",
    "    layer_2 = Dense(l2_num_neuron, activation=act_func_el,\n",
    "                    kernel_regularizer='l2')\n",
    "    layer_3 = Dense(l3_num_neuron, activation=act_func_el,\n",
    "                    kernel_regularizer='l2')\n",
    "    layer_4 = Dense(l4_num_neuron, activation=act_func_el,\n",
    "                    kernel_regularizer='l2')\n",
    "    layer_5 = Dense(l5_num_neuron, activation=act_func_el,\n",
    "                    kernel_regularizer='l2')\n",
    "    layer_out = Dense(output_neuron, activation=act_func_last,\n",
    "                    kernel_regularizer='l2')\n",
    "    \n",
    "    # define MLP\n",
    "    \n",
    "    mlp=Sequential()\n",
    "    mlp.add(layer_1)\n",
    "    mlp.add(Dropout(0.5))\n",
    "    mlp.add(layer_2)\n",
    "    mlp.add(Dropout(0.5))\n",
    "    mlp.add(layer_3)\n",
    "    mlp.add(Dropout(0.4))\n",
    "    #mlp.add(layer_4)\n",
    "    #mlp.add(Dropout(0.2))\n",
    "    #mlp.add(layer_5)\n",
    "    mlp.add(layer_out)\n",
    "    \n",
    "    # define optimizer (optional)\n",
    "    opts = opt.SGD(lr=0.001, momentum=0.9, decay=1e-6,nesterov=True)\n",
    "    mlp.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "    \n",
    "    def get_loss(y_true, y_pred, regularization=0.1):\n",
    "        return K.mean(K.square(y_true - y_pred)) + regularization * additional_fea\n",
    "    \n",
    "    mlp.fit(input_sample, target, batch_size=num_batch,epochs=20, verbose=2, callbacks=[hist], validation_split=0.3)\n",
    "    \n",
    "    return mlp,hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(169307, 45)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 118514 samples, validate on 50793 samples\n",
      "Epoch 1/20\n",
      " - 11s - loss: 1.0214 - acc: 0.7688 - val_loss: 0.5940 - val_acc: 0.7772\n",
      "Epoch 2/20\n",
      " - 10s - loss: 0.5962 - acc: 0.7758 - val_loss: 0.5828 - val_acc: 0.7773\n",
      "Epoch 3/20\n",
      " - 10s - loss: 0.5911 - acc: 0.7756 - val_loss: 0.5787 - val_acc: 0.7772\n",
      "Epoch 4/20\n",
      " - 10s - loss: 0.5887 - acc: 0.7756 - val_loss: 0.5793 - val_acc: 0.7770\n",
      "Epoch 5/20\n",
      " - 10s - loss: 0.5882 - acc: 0.7754 - val_loss: 0.5816 - val_acc: 0.7771\n",
      "Epoch 6/20\n",
      " - 10s - loss: 0.5877 - acc: 0.7751 - val_loss: 0.5788 - val_acc: 0.7768\n",
      "Epoch 7/20\n",
      " - 10s - loss: 0.5877 - acc: 0.7751 - val_loss: 0.5812 - val_acc: 0.7772\n",
      "Epoch 8/20\n",
      " - 10s - loss: 0.5866 - acc: 0.7753 - val_loss: 0.5789 - val_acc: 0.7772\n",
      "Epoch 9/20\n",
      " - 10s - loss: 0.5870 - acc: 0.7754 - val_loss: 0.5787 - val_acc: 0.7764\n",
      "Epoch 10/20\n",
      " - 10s - loss: 0.5870 - acc: 0.7752 - val_loss: 0.5784 - val_acc: 0.7770\n",
      "Epoch 11/20\n",
      " - 11s - loss: 0.5870 - acc: 0.7753 - val_loss: 0.5779 - val_acc: 0.7762\n",
      "Epoch 12/20\n",
      " - 10s - loss: 0.5865 - acc: 0.7754 - val_loss: 0.5782 - val_acc: 0.7770\n",
      "Epoch 13/20\n",
      " - 10s - loss: 0.5862 - acc: 0.7754 - val_loss: 0.5783 - val_acc: 0.7765\n",
      "Epoch 14/20\n",
      " - 10s - loss: 0.5872 - acc: 0.7754 - val_loss: 0.5771 - val_acc: 0.7766\n",
      "Epoch 15/20\n",
      " - 10s - loss: 0.5870 - acc: 0.7751 - val_loss: 0.5755 - val_acc: 0.7770\n",
      "Epoch 16/20\n",
      " - 10s - loss: 0.5866 - acc: 0.7753 - val_loss: 0.5817 - val_acc: 0.7770\n",
      "Epoch 17/20\n",
      " - 10s - loss: 0.5861 - acc: 0.7755 - val_loss: 0.5783 - val_acc: 0.7771\n",
      "Epoch 18/20\n",
      " - 10s - loss: 0.5865 - acc: 0.7755 - val_loss: 0.5773 - val_acc: 0.7770\n",
      "Epoch 19/20\n",
      " - 10s - loss: 0.5855 - acc: 0.7754 - val_loss: 0.5797 - val_acc: 0.7768\n",
      "Epoch 20/20\n",
      " - 10s - loss: 0.5858 - acc: 0.7754 - val_loss: 0.5804 - val_acc: 0.7765\n"
     ]
    }
   ],
   "source": [
    "nn, nn_hist = build_model_architecture(input_sample=X_train_new.values,target=Y_train,input_dim=X_train_new.shape[1],num_batch=200,classes=3,act_func_el='relu',act_func_last='softmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#X_test.drop('connection_id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   32/91166 [..............................] - ETA: 8:38\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  352/91166 [..............................] - ETA: 1:00\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  672/91166 [..............................] - ETA: 39s \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  960/91166 [..............................] - ETA: 32s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 1248/91166 [..............................] - ETA: 28s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 1536/91166 [..............................] - ETA: 26s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 1824/91166 [..............................] - ETA: 24s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 2144/91166 [..............................] - ETA: 23s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 2464/91166 [..............................] - ETA: 21s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 2688/91166 [..............................] - ETA: 21s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 2944/91166 [..............................] - ETA: 21s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 3232/91166 [>.............................] - ETA: 20s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 3520/91166 [>.............................] - ETA: 20s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 3840/91166 [>.............................] - ETA: 19s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 4160/91166 [>.............................] - ETA: 19s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 4448/91166 [>.............................] - ETA: 19s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 4672/91166 [>.............................] - ETA: 19s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 4928/91166 [>.............................] - ETA: 19s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5184/91166 [>.............................] - ETA: 19s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5472/91166 [>.............................] - ETA: 18s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5760/91166 [>.............................] - ETA: 18s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 6048/91166 [>.............................] - ETA: 18s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 6336/91166 [=>............................] - ETA: 18s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 6560/91166 [=>............................] - ETA: 18s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 6880/91166 [=>............................] - ETA: 17s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 7168/91166 [=>............................] - ETA: 17s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 7488/91166 [=>............................] - ETA: 17s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 7808/91166 [=>............................] - ETA: 17s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8128/91166 [=>............................] - ETA: 17s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8320/91166 [=>............................] - ETA: 17s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8608/91166 [=>............................] - ETA: 16s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8896/91166 [=>............................] - ETA: 16s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 9216/91166 [==>...........................] - ETA: 16s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 9536/91166 [==>...........................] - ETA: 16s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 9856/91166 [==>...........................] - ETA: 16s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "10080/91166 [==>...........................] - ETA: 16s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "10368/91166 [==>...........................] - ETA: 16s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "10656/91166 [==>...........................] - ETA: 16s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "10976/91166 [==>...........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11296/91166 [==>...........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11616/91166 [==>...........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11872/91166 [==>...........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "12160/91166 [===>..........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "12448/91166 [===>..........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "12736/91166 [===>..........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13056/91166 [===>..........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13376/91166 [===>..........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13696/91166 [===>..........................] - ETA: 15s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13984/91166 [===>..........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14272/91166 [===>..........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14528/91166 [===>..........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14816/91166 [===>..........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15136/91166 [===>..........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15424/91166 [====>.........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15712/91166 [====>.........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "16000/91166 [====>.........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "16320/91166 [====>.........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "16640/91166 [====>.........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "16960/91166 [====>.........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17216/91166 [====>.........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17504/91166 [====>.........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17792/91166 [====>.........................] - ETA: 14s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "18080/91166 [====>.........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "18368/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "18656/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "18976/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "19296/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "19584/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "19904/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20224/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20544/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20800/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "21088/91166 [=====>........................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "21376/91166 [======>.......................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "21696/91166 [======>.......................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "21984/91166 [======>.......................] - ETA: 13s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "22272/91166 [======>.......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "22560/91166 [======>.......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "22880/91166 [======>.......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "23200/91166 [======>.......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "23520/91166 [======>.......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "23840/91166 [======>.......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "24096/91166 [======>.......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "24320/91166 [=======>......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "24608/91166 [=======>......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "24896/91166 [=======>......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "25152/91166 [=======>......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "25440/91166 [=======>......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "25728/91166 [=======>......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "26016/91166 [=======>......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "26336/91166 [=======>......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "26656/91166 [=======>......................] - ETA: 12s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "26976/91166 [=======>......................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27296/91166 [=======>......................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27616/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27904/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28160/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28448/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28768/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29088/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29376/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29664/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29920/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "30240/91166 [========>.....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "30560/91166 [=========>....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "30848/91166 [=========>....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "31168/91166 [=========>....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "31488/91166 [=========>....................] - ETA: 11s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "31808/91166 [=========>....................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "32032/91166 [=========>....................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "32352/91166 [=========>....................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "32640/91166 [=========>....................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "32960/91166 [=========>....................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "33248/91166 [=========>....................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "33568/91166 [==========>...................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "33856/91166 [==========>...................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "34144/91166 [==========>...................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "34464/91166 [==========>...................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "35072/91166 [==========>...................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "35360/91166 [==========>...................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "35552/91166 [==========>...................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "36128/91166 [==========>...................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "36416/91166 [==========>...................] - ETA: 10s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "40064/91166 [============>.................] - ETA: 9s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40352/91166 [============>.................] - ETA: 9s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40640/91166 [============>.................] - ETA: 9s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40928/91166 [============>.................] - ETA: 9s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "41216/91166 [============>.................] - ETA: 9s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "41504/91166 [============>.................] - ETA: 9s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "41792/91166 [============>.................] - ETA: 9s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42080/91166 [============>.................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42400/91166 [============>.................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42720/91166 [=============>................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43040/91166 [=============>................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43360/91166 [=============>................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43680/91166 [=============>................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43872/91166 [=============>................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "44160/91166 [=============>................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "44768/91166 [=============>................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "45056/91166 [=============>................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "45344/91166 [=============>................] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "45664/91166 [==============>...............] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "45984/91166 [==============>...............] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "46272/91166 [==============>...............] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "46560/91166 [==============>...............] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "46816/91166 [==============>...............] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "47104/91166 [==============>...............] - ETA: 8s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "47424/91166 [==============>...............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "47744/91166 [==============>...............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "48032/91166 [==============>...............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "48352/91166 [==============>...............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "48672/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "48896/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "49216/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "49504/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "49824/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "50112/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "50432/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "50720/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "51008/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "51328/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "51616/91166 [===============>..............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "51904/91166 [================>.............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "52192/91166 [================>.............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "52448/91166 [================>.............] - ETA: 7s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "52768/91166 [================>.............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53088/91166 [================>.............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53408/91166 [================>.............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53696/91166 [================>.............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "54016/91166 [================>.............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "54240/91166 [================>.............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "54528/91166 [================>.............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "54816/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "55136/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "55424/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "55744/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "56032/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "56352/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "56640/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "56960/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57248/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57536/91166 [=================>............] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57824/91166 [==================>...........] - ETA: 6s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "58112/91166 [==================>...........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "58432/91166 [==================>...........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "58752/91166 [==================>...........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "59072/91166 [==================>...........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "59360/91166 [==================>...........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "59680/91166 [==================>...........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "59904/91166 [==================>...........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "60192/91166 [==================>...........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "60512/91166 [==================>...........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "60832/91166 [===================>..........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "61120/91166 [===================>..........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "61440/91166 [===================>..........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "61728/91166 [===================>..........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "62016/91166 [===================>..........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "62336/91166 [===================>..........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "62624/91166 [===================>..........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "62944/91166 [===================>..........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "63232/91166 [===================>..........] - ETA: 5s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "63520/91166 [===================>..........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "63776/91166 [===================>..........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "64064/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "64384/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "64704/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "64992/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "65312/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "65632/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "65888/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "66208/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "66528/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "66816/91166 [====================>.........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "67136/91166 [=====================>........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "67456/91166 [=====================>........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "67744/91166 [=====================>........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "68032/91166 [=====================>........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "68320/91166 [=====================>........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "68608/91166 [=====================>........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "68896/91166 [=====================>........] - ETA: 4s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "69184/91166 [=====================>........] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "69472/91166 [=====================>........] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "69760/91166 [=====================>........] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "70048/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "70336/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "70624/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "70944/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71264/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71584/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71904/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "72224/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "72448/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "72736/91166 [======================>.......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "73024/91166 [=======================>......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "73312/91166 [=======================>......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "73600/91166 [=======================>......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "73888/91166 [=======================>......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "74208/91166 [=======================>......] - ETA: 3s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "74496/91166 [=======================>......] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "74784/91166 [=======================>......] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "75104/91166 [=======================>......] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "75392/91166 [=======================>......] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "75712/91166 [=======================>......] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "76000/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "76288/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "76576/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "76864/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "77152/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "77440/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "77760/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "78080/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "78400/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "78720/91166 [========================>.....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79040/91166 [=========================>....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79264/91166 [=========================>....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79552/91166 [=========================>....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79840/91166 [=========================>....] - ETA: 2s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "80128/91166 [=========================>....] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "80416/91166 [=========================>....] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "80704/91166 [=========================>....] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "80960/91166 [=========================>....] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "81248/91166 [=========================>....] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "81536/91166 [=========================>....] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "81824/91166 [=========================>....] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "82112/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "82432/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "82720/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83008/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83328/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83616/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83904/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "84192/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "84480/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "84768/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85088/91166 [==========================>...] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85408/91166 [===========================>..] - ETA: 1s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85728/91166 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86048/91166 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86368/91166 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86592/91166 [===========================>..] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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      "91166/91166 [==============================] - 16s 179us/step\n"
     ]
    }
   ],
   "source": [
    "predicted_values = nn.predict(X_test_new.values, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91166, 3)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.71539772,  0.00815362,  0.27644855], dtype=float32)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_values[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_ann=np.argmax(predicted_values, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_ann=pd.DataFrame(pred_ann,columns=['target_ann'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    72617\n",
       "1    18549\n",
       "Name: target_ann, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_ann['target_ann'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1.],\n",
       "       [ 1.,  0.,  0.],\n",
       "       [ 1.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frames = [predictions_rf, predictions_et,predictions_ada ,predictions_xgb,predictions_gb]\n",
    "#frames = [predictions_xgb,predictions_ann]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions=pd.concat(frames,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions['target_ann']=predictions['target_ann'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    63891\n",
       "0.0    27127\n",
       "2.0      148\n",
       "Name: target_xgb, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions['target_xgb'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target_rf</th>\n",
       "      <th>target_et</th>\n",
       "      <th>target_ada</th>\n",
       "      <th>target_xgb</th>\n",
       "      <th>target_gb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_rf  target_et  target_ada  target_xgb  target_gb\n",
       "0        1.0        1.0         1.0         0.0        1.0\n",
       "1        0.0        0.0         0.0         1.0        2.0\n",
       "2        0.0        0.0         0.0         1.0        2.0\n",
       "3        1.0        0.0         0.0         1.0        2.0\n",
       "4        0.0        0.0         0.0         1.0        2.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final=predictions.mode(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final2=predictions.max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91166,)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_final2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final=pd.DataFrame(pred_final2.values,columns=['Final'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    53735\n",
       "2.0    19044\n",
       "1.0    18387\n",
       "Name: Final, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_final['Final'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final.drop([1],axis=1,inplace=True)\n",
    "#pred_final['Final'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    74022\n",
       "1.0    17018\n",
       "2.0      126\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_final[0].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sub = pd.read_csv('sample_submission.csv')\n",
    "sub['target'] = pred_final[0]\n",
    "sub['target'] = sub['target'].astype(int)\n",
    "sub.to_csv('submission19.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sub = pd.read_csv('sample_submission.csv')\n",
    "sub['target'] = predictions['target_xgb']\n",
    "sub['target'] = sub['target'].astype(int)\n",
    "sub.to_csv('submission20.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final3=(predictions_mean.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pred_final3' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-ea150722a6ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpred_final3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pred_final3' is not defined"
     ]
    }
   ],
   "source": [
    "pred_final3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final3=pd.DataFrame(pred_final3.ravel(),columns=['Final'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final3['Final']=pred_final3['Final'].round"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_final3['Final'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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