titanic_cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Titanic dataset: predicting survived passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense, Activation, LSTM, merge, Conv1D, MaxPooling1D, Flatten, Dropout, Embedding, GlobalMaxPooling1D\n",
    "from keras.models import Model\n",
    "from keras.callbacks import Callback\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import SVG\n",
    "from keras.utils.visualize_util import model_to_dot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, roc_auc_score, log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"./train.csv\")\n",
    "test_df = pd.read_csv(\"./test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = train_df.Survived.values\n",
    "train_df.drop('Survived', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df = train_df.fillna(0)\n",
    "test_df = test_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df[\"Sex\"] = train_df[\"Sex\"].apply(lambda x: 1 if x == \"male\" else 0)\n",
    "test_df[\"Sex\"] = train_df[\"Sex\"].apply(lambda x: 1 if x == \"male\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df[\"Cabin\"] = train_df[\"Cabin\"].apply(lambda x: 1 if x != 0 else 0)\n",
    "test_df[\"Cabin\"] = train_df[\"Cabin\"].apply(lambda x: 1 if x != 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "   Sex   Age  SibSp  Parch            Ticket     Fare  Cabin Embarked  \n",
       "0    1  22.0      1      0         A/5 21171   7.2500      0        S  \n",
       "1    0  38.0      1      0          PC 17599  71.2833      1        C  \n",
       "2    0  26.0      0      0  STON/O2. 3101282   7.9250      0        S  \n",
       "3    0  35.0      1      0            113803  53.1000      1        S  \n",
       "4    1  35.0      0      0            373450   8.0500      0        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numeric_features = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Cabin\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_numeric = train_df[numeric_features].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 7)"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_numeric.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text_features = [\"Name\", \"Ticket\", \"Embarked\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data(symbols):\n",
    "    vocab = {}\n",
    "    words = list(symbols.lower())\n",
    "    for i, word in enumerate(words):\n",
    "        if word not in vocab:\n",
    "            vocab[word] = len(vocab)\n",
    "    print('corpus length:', len(words))\n",
    "    print('vocab size:', len(vocab))\n",
    "    return vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_symbols = \"\"\n",
    "for x in train_df[text_features].values:\n",
    "    all_symbols += \" \".join(map(str, x)) + \" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "corpus length: 33605\n",
      "vocab size: 45\n"
     ]
    }
   ],
   "source": [
    "vocab = load_data(all_symbols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "max_name_length = train_df.Name.apply(len).max()\n",
    "max_ticket_length = train_df.Ticket.apply(len).max()\n",
    "train_df[\"Embarked\"] = train_df.Embarked.apply(lambda x: \"s\" if x == 0 else x.lower())\n",
    "max_embarked_length = train_df.Embarked.apply(len).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_text = np.zeros((891, max_name_length + max_ticket_length + max_embarked_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for e, i in enumerate(train_df[text_features].iterrows()):\n",
    "    name = i[1][\"Name\"].lower()\n",
    "    ticket = i[1][\"Ticket\"].lower()\n",
    "    emb = i[1][\"Embarked\"].lower()\n",
    "    for p, w in enumerate(name):\n",
    "        X_text[e, p] = vocab[w]\n",
    "    for p, w in enumerate(ticket):\n",
    "        X_text[e, p + max_name_length] = vocab[w]\n",
    "    for p, w in enumerate(emb):\n",
    "        X_text[e, p + max_name_length + max_ticket_length] = vocab[w] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 101)"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_text.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 7)"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_numeric.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "split_n = int(0.25 * len(train_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_text_train, X_text_test = X_text[split_n:], X_text[:split_n]\n",
    "X_numeric_train, X_numeric_test = X_numeric[split_n:], X_numeric[:split_n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train, y_test = labels[split_n:], labels[:split_n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.39461883408071746, 0.35135135135135137)"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.mean(), y_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "numeric_input = Input(shape=(7,), name='numeric_input')\n",
    "y = Dense(3)(numeric_input)\n",
    "\n",
    "text_input = Input(shape=(101,), name='text_input')\n",
    "x = Embedding(len(vocab), 64, input_length=101) (text_input)\n",
    "x = Conv1D(16, 4, activation='relu', subsample_length=1)(x)\n",
    "x = GlobalMaxPooling1D()(x)\n",
    "x = Dense(16)(x)\n",
    "x = Dropout(0.5)(x)\n",
    "\n",
    "conv_output = Dense(1, activation='sigmoid', name='conv_output')(x)\n",
    "\n",
    "x = merge([x, y], mode='concat')\n",
    "\n",
    "preds = Dense(1, activation='sigmoid', name='main_output')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adam = Adam(lr=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Model(input=[numeric_input, text_input], output=[preds, conv_output])\n",
    "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=[\"accuracy\"], loss_weights=[1, 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "text_input (InputLayer)          (None, 101)           0                                            \n",
      "____________________________________________________________________________________________________\n",
      "embedding_15 (Embedding)         (None, 101, 64)       2880        text_input[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "convolution1d_57 (Convolution1D) (None, 98, 16)        4112        embedding_15[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "globalmaxpooling1d_6 (GlobalMaxPo(None, 16)            0           convolution1d_57[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "dense_7 (Dense)                  (None, 16)            272         globalmaxpooling1d_6[0][0]       \n",
      "____________________________________________________________________________________________________\n",
      "numeric_input (InputLayer)       (None, 7)             0                                            \n",
      "____________________________________________________________________________________________________\n",
      "dropout_41 (Dropout)             (None, 16)            0           dense_7[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_6 (Dense)                  (None, 3)             24          numeric_input[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "merge_19 (Merge)                 (None, 19)            0           dropout_41[0][0]                 \n",
      "                                                                   dense_6[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "main_output (Dense)              (None, 1)             20          merge_19[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "conv_output (Dense)              (None, 1)             17          dropout_41[0][0]                 \n",
      "====================================================================================================\n",
      "Total params: 7325\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N_EPOCHS = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 21.2 s, sys: 304 ms, total: 21.5 s\n",
      "Wall time: 22.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for epoch in range(N_EPOCHS):\n",
    "    model.fit([X_numeric_train, X_text_train], [y_train, y_train], nb_epoch=1, batch_size=8, verbose=0)\n",
    "    probas = model.predict([X_numeric_train, X_text_train])[0]\n",
    "    a, r, l = accuracy_score(y_train, probas > 0.5), roc_auc_score(y_train, probas), log_loss(y_train, probas)\n",
    "    train_scores.append((a, r, l))\n",
    "    probas = model.predict([X_numeric_test, X_text_test])[0]\n",
    "    a, r, l = accuracy_score(y_test, probas > 0.5), roc_auc_score(y_test, probas), log_loss(y_test, probas)\n",
    "    test_scores.append((a, r, l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "probas = model.predict([X_numeric_test, X_text_test])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35572314"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probas.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_scores = pd.DataFrame([x for x in train_scores], columns=[\"accuracy\", \"roc_auc\", \"log_loss\"])\n",
    "test_scores = pd.DataFrame([x for x in test_scores], columns=[\"accuracy\", \"roc_auc\", \"log_loss\"])\n",
    "train_scores[\"phase\"] = \"train\"\n",
    "test_scores[\"phase\"] = \"test\"\n",
    "scores = pd.concat([train_scores, test_scores])\n",
    "scores[\"epoch\"] = scores.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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F1RAWhxc19L2IiJodQ+s8M5wu4Wv/9kp1KkDAJ2Pr1Stww6bFnLPa4kZKKewZ\n3ofdw/vRnz0GWZCq5XL840rl5M0i0noGWSM35VFSURARVSOIqmFE1Wj1fkCZuKI6KAfgk9TyCmCv\noHu1uLtjeQW4KyuEy8W4XcFBZzwO1Q4gKnuLjmayGMcv+xD3xab9eiIiak4MrfOIadn41pP7qoH1\nmg2L8PHrVrGu6gKX0tLIGrlxNfTk6mKeUT2NPUP78drwPhzLnZjwOsN1YBgmspjapXEBAjoD7egO\ndaI72FVeHd+FzkA7QkqwYauqubqYiIimgqF1Htn+y4M4dtrb0eTj163En21ePrcNooYwbBOH0/14\nffQtvD7yFgaLQzW9fmmkD+vb10ASpXHbP3pFw3XbQEgJIl4tpxSrli5K+OOcI0lERE2Lf6HmiRf2\nnsLzewYAABsv6MCHrlw2xy2i6TBtE4PFYZTKW/eNlUcyoFk63skcwaH04Zq2ihQgYGVsGTZ2XYKN\nnevR5k808BMQERHNDYbWeeDY6Rz+zy8OAgA6437c/eG1EFnGqinotoG8UYAkitUyTpWj5Zg4mR/A\n8fwpnMidwvHcSQwWh6Y8j9QnqViduAAXt12E3nAPrPIOMYZtVksYKaKCtW2rEfNFG/xJiYiI5hZD\na5Mraia+9eQ+mJYDRRbx/3zsEgT9ylw3qyW4rgvN1qu7D41qKYyURpHURpEsjWKkNIqcma/b+8mi\njJ5gF9a2rcbF7RdhZWzZjIq4ExERLST8i9jEHNfFY//1BobTGgDgf31wNZZ2R+a4VQtPydK8EdHc\nSZzIn8JoKYW04QVVwzZmfH4BAjqD7VgSXowlkcXoC/cioobH6niWj9w+koiI6OwYWpvYMy8fw+63\nkwCAay9dhGs2NLYm5ELgui5Kloa0nkFGzyJn5mE59rgyS95OSIZtYKBwGsdzJzFcGpny+QOyHx3+\nNrQH2tEeSKDD346oLwLXdavnth0btmtDgICeUDcWhxfBL/sa+KmJiIgWPobWJvX2iQx2/O4wAGBZ\ndwR3fGD1HLdo9pTMEgrZLAbTo8jrJZQsb3vQklWCbunlYvMTdxfSxgVVwzHP/yZn0RloR3ewE7Hy\njkPx6gp77xZSgnX8pERERDRVDK1NyLIdPP7Mm3Bdb/OAv/7Yeijy9IutzwcFs4g9w/ux6/QevJV6\nu7qveyOIgghZkNAZ7EBfuBdLIt5l+8XhRQjI/oa9LxEREU0fQ2sT+u+XjuJUsgAA2HbjBeiMB+a4\nRY1RsjRwnK3YAAAgAElEQVTsHT6AXUN78MbowfOuqpcFCT7ZB0VUvOL6UrnYvijDL/sQU6PlPdnH\nRkljahSyKEMSvNX9oiCed3tTIiIiaj4MrU1mYKSA//zDEQDARUviuGbD/NpvvGRpSJZGMFwawXAx\nieHSCFJaGrqtV+uRGrYB3TEmXeTUHezE5T0bcWnfRXA0AYrgQ0AOICj7oUismkBERNSqGFqbiOu6\n+Ldn3oJlu5AlAZ+85aKmHhV0XRcDhdN4M3UIb44ewtHsceTNQs3nafe34bLuS3FZ16VYHF4ERZG4\nrScRERFNwNDaRH6/dwBvHU8DAD68eTkWtYfmuEVnyhsFvDF6EG+MHsSbo4eQMbJnfa4kSOgItKHN\nn0BQDkAdV97Ju/mwMrYMSyN9TR3OiYiIaO4xtDaJTMHAf/z2bQDAovZgU23T6rouDqUP4/cnX8bu\n4f2wXfuM5ywOL8Lq+Cp0hzrRGehAZ6AdCX+ctUeJiIioLhham8SPfn0IBc3bb/4vb1kDRZ77sJc3\nCnhp8BW8eOplDBWTE74WUyNY07Yaa9ouxJq2CxFVuekBERERNQ5DaxPY984IXn79NADg+o29WL0k\nPiftcF0XI9ooDqYO443Rg9g7fADWuFHVgBzAlT2X4cpFl2NxeBEv6RMREdGsYWidY7ph49+ffQsA\nEAup+MT1q2b1/VNaGm+m3sah1GEcTB1GSk+f8ZyVsWW4uvdKvKdrA1Su4CciIqI5wNA6x5754zEk\nMxoA4C8+sBpB/+yEwqJZwpNv/xf+MPCnSb/e7k9gfcdaXNV7BRaH51fZLSIiIlp4GFrnkGU7+O1r\nJwEAa5clcPlFnbPyvruH9+M/3noSGSNXfSzui2F1YhVWx1dhdWIV2gNts9IWIiIioqlgaJ1Drx4c\nRrbgFdj/4HuXNHyOaEbP4YmDT+G14X3Vxy7v3og/W/EBdAY6OEeViIiImhZD6xz6zaveKGtHzI9L\nVrY37H1c18VLg7vwk0P/iaJVAuCNrP7Pi27D+o61DXtfIiIionphaJ0jJ4bzOFjeSOD69yyGKDZu\nlPM/Dv4Uz5/8Q/W/r128Gf9j1YcQkP0Ne08iIiKiemJonSOVuayyJODqDY1b6LRneH81sHYFO/AX\nF30CFyZWNuz9iIiIiBqBoXUOlHQLf9g/CAB475ouRINqQ94na+Twgzd3AABiahT/32X/L0JKsCHv\nRURERNRIc7/tUgt66cAgdMMr2n/Dpr6GvIfrutj+xhPImwUAwJ0X387ASkRERPMWQ+ssc10XvylP\nDVjaHcaq3mhD3ufFUy9j/8ibAIDr+67C2rbVDXkfIiIiotnA0DrLDp3I4OSwN/p546a+hpSZGioO\nY8eh/wQA9AS78NFVt9b9PYiIiIhmE0PrLPvNqycAAAGfjCvWdtf9/LZj4/uv/18YjglREPGX6/6c\nW68SERHRvDet0Lp9+3bceOON2LBhA26//Xbs3bv3nM//2c9+ho9+9KPYuHEjrr76avzjP/4j0ukz\n97hf6DJ5HbveGgYAXHVJD3yqVPf3ePbob3AkewwA8OEVH8TSSGPmzBIRERHNpppD69NPP42HHnoI\nn/vc5/Dkk09izZo1uPvuuzE6Ojrp83ft2oV/+Id/wO23346f//znePTRR7Fv3z58+ctfnnHj55vn\n95yC7bgAgBves7ju5z+aPY7/PvJrAMDK2HJ8YNn1dX8PIiIiorlQc2h9/PHHsW3bNmzduhWrVq3C\n/fffD7/fjx07dkz6/D179qCvrw933HEHFi9ejE2bNmHbtm3nHZ1daGzHwXO7TwEA1i5LYFF7qM7n\nt/Fvr/9fOK4Dn6TiLy/eBlHg7A8iIiJaGGpKNaZp4sCBA9i8eXP1MUEQsGXLFuzevXvS12zcuBED\nAwP43e9+BwBIJpN45plncN11182g2fPPnrdHkMrpALwFWPX28uCrGCwOAQA+fsFH0BFo3LawRERE\nRLOtps0FUqkUbNtGR0fHhMfb29vR398/6Ws2bdqEb3zjG/jCF74AwzBgWRZuvPHGaU0PkKT5O3L4\n613eAqxExIfL13ZCEuv3WUzHwtNHfgkA6A1145qlV8zrUdZKP8/n/qapY3+3FvZ3a2F/t5ZG93PD\nd8R6++238cADD+Bv/uZvcNVVV2F4eBgPP/wwvvzlL+OBBx6o6VzRaKBBrWys1/tH8MbRFADgI9es\nREd7pK7nf/rgb5DSvIVtf7FxK9rb6nv+uTJf+5umh/3dWtjfrYX9TfVQU2hNJBKQJAnJZHLC4yMj\nI2eMvlZ8+9vfxqZNm/CpT30KALB69Wrcd999uOOOO/CFL3zhrK+bTDZbgm07tTS5Kfyfp98AAAT9\nMq5a141UqlC3c+uWjh0H/hsAsDy6BBcEL6jr+eeCJImIRgPztr+pNuzv1sL+bi3s79ZS6e9GqSm0\nKoqCdevWYefOnbjpppsAeDs87dy5E3feeeekr9E0DYoysU6oKIoQBAGu69bUWNt2YFnz64f+nVNZ\n7HtnBADw/sv6oEhiXT/Dr468gJyRBwB8eOXNsG0XQG3f12Y1H/ubpo/93VrY362F/U31UPPkg7vu\nugtPPPEEnnrqKRw+fBj33XcfNE3DbbfdBgB45JFHcM8991Sff8MNN+DZZ5/FD3/4Qxw/fhy7du3C\nAw88gEsvvRSdnZ31+yRN6r/+cAQA4FclfOC9S+p67qJZxC+PeQvcLoyvxJrEhXU9PxEREVGzqHlO\n66233opUKoVHH30UyWQSa9euxWOPPYa2tjYAXnWAgYGB6vM/9rGPoVgs4gc/+AH++Z//GZFIBJs3\nb8bf/d3f1e9TNKljp3PY/bY3leKmy/oQ8td3Z6pfHXseJasEAPgfq25pyJawRERERM1AcGu9Rj+H\nUqnCvLq88K0n92HXW8NQFRH//FdbEA2qdTt31sjhvp0Pw7ANrG9fg7+69H/X7dxzTZZFJBKhedff\nND3s79bC/m4t7O/WUunvRmENigY5MZyvbtl6w3sW1zWwAsAvjvwWhm0AAD688pa6npuIiIio2TC0\nNkhlLqsii7jlfUvreu5RLYUXTu4EAGzq2oAlkd66np+IiIio2TC0NsDASAF/esPbneq6S3sRC/vq\nev7/7v81LNeGAAEfXvHBup6biIiIqBkxtDbAz3cehQtAlgR86MpldT33cHEELw2+AgC4ctHl6A51\n1fX8RERERM2IobXOhtIlvHTgNADg6g29SETqO8r6y2PPwXEdiIKIDy2/qa7nJiIiImpWDK119vTO\nI3BcF5Io4NYr6zuXNa1n8PKAN8r6vu5NaA+01fX8RERERM2KobWOdNPGH/Z7o6yb1/egI1bfrcx+\nc+yF6lzWDyy7vq7nJiIiImpmDK11dOh4GlZ5b+Wr1vfU9dwFs4gXTr0EALi0cz16OJeViIiIWghD\nax3t7x8FAPgUCasWx+p67udOvFity3rzshvqem4iIiKiZsfQWkevH/FC65qlcchS/b61mqXjd8df\nBACsbVuNpdG+up2biIiIaD5gaK2TTF7HieECAODiFfVdIPXiqZdRsIoAgA9ylJWIiIhaEENrnbx+\nJFW9v255/UKr6Vj49bHnAQAroktxYXxl3c5NRERENF8wtNZJZT5rIuLDovZg3c77x4FdyBhZAMDN\ny2+EIAh1OzcRERHRfMHQWgeu6+L1o15oXbe8rW7B0nZs/OLYcwCA3lAP1rWvqct5iYiIiOYbhtY6\nOJksIJP3VvZfvCJRt/O+NrwPydIIAG8uqyiwu4iIiKg1MQXVwevlqQEAcPGy+sxndV0Xvzj6WwBA\nh78Nm7o21OW8RERERPMRQ2sd7C+XulraFUY0pNblnHuTr+NkfgAA8P5l10MSpbqcl4iIiGg+Ymid\nIdNycPBYGgCwrk6lrmzHxk8PPw0AiPtiuLLnsrqcl4iIiGi+YmidobdPZmBY3tat9arP+vtTL+N0\ncRgA8JGVN0ORlLqcl4iIiGi+YmidocouWIosYnXfzLduLVklPN3/SwDAknAv3tezacbnJCIiIprv\nGFpnqFKfdXVfDIo883mnzx75LfKmt7PWbRd+mBUDiIiIiMDQOiP5koljgzkAwLoV7TM+30hpFL89\n/gIA4JKOtViduGDG5yQiIiJaCBhaZ+D1I6Nwy/cvXj7z+qw/e+cZWK4NURCxddWfzfh8RERERAsF\nQ+sMVOazRoMK+rrCMzrXkewxvHJ6NwDg6t4r0BPqmnH7iIiIiBYKhtZpcl0XB8rzWS9e3gZxBlu3\nuq6LHYf+CwDgl/y4dcUH6tJGIiIiooWCoXWaTqdKGMnqAGZen3X38H68kzkCALh5+Q2IqDMbtSUi\nIiJaaBhap+nA+K1bl08/tFqOhafKGwm0+RO4oe/qGbeNiIiIaKFhaJ2mynzW3o4QEhHftM/zh1N/\nRLI0AgD46MpbuJEAERER0SQYWqfBsh28cTQFYOZVA/44+CoAYHF4ES7r3jjjthEREREtRAyt0/D2\niQw0wwYArJvB1ICUlkZ/9hgA4L3d74Ewg8VcRERERAsZQ+s07Do4DADwKRLWLpv+SOue4QPV++/p\numTG7SIiIiJaqBhaa+S4Ll4th9YNq9qhKtPfunX38D4AQF+4Fx2Bme+oRURERLRQMbTW6MhADqmc\nV+rqsos6p32enJHH2+l+AMDGTo6yEhEREZ0LQ2uNdh0cAgDIkoBLVk5/dHTP8H645U1g39O1vi5t\nIyIiIlqoGFpr4Loudr3lTQ1Yt7wNAZ887XPtHt4PAOgJdqEn1F2X9hEREREtVAytNTiZLGAoVQIA\nbJrB1ICiWcRbqbcBABu5AIuIiIjovBhaa/BqeZRVFARsvKBj2ufZm3wdjusA4HxWIiIioqlgaK1B\npdTVRUvjiATVaZ+nUjWgw9+GvvCiurSNiIiIaCFjaJ2ioVQRx4fyAIBNq6c/NUCzNLwxegiANzWA\nGwoQERERnR9D6xS9ejBZvT+T0Lp/5E1YjgWAUwOIiIiIpoqhdYoqpa5W9UaRiPimfZ5K1YC4L4Zl\n0b66tI2IiIhooWNonYJUTsfhk1kAM6saYNgmDoy8CQDY2LkeosBvPxEREdFUMDVNwWuHhqv3ZzI1\n4I3Rt2DYBgBODSAiIiKqBUPrFFQ2FOjrDKM7EZz2eV4b8qYGRJQwVsWX16NpRERERC2BofU88iUT\nbx1LAwAum8HUAMuxsH/kdQDApZ3rODWAiIiIqAZMTuex+1ASjusCmFlofSv1NkqWBoC7YBERERHV\niqH1PF4tbyjQnQhgcUdo+ucZ2gsACMoBrI6vqkvbiIiIiFoFQ+s5lHQL+/tHAXhVA6a7EUBKS+OV\nwdcAAJd2rockSnVrIxEREVErYGg9hwP9o7BsBwBw2equaZ/nmaO/geXaECDg/Uuvq1fziIiIiFoG\nQ+s5HD6VAQAEfTKWL4pM6xwjpVHsPPUnAMDl3e9BT2j64ZeIiIioVU0rtG7fvh033ngjNmzYgNtv\nvx179+4963PvvfderFmzBmvXrsWaNWuqt4985CPTbvRs6R/IAQCWL4pAnObUgGeO/Bq2a0MURNy6\n4v31bB4RERFRy6g5tD799NN46KGH8LnPfQ5PPvkk1qxZg7vvvhujo6OTPv+LX/wiXnzxRfz+97/H\niy++iN/97neIxWK45ZZbZtz4RnIcF0cHvdC6YlF0WucYKibx0uAuAMAVPZehK9hRt/YRERERtZKa\nQ+vjjz+Obdu2YevWrVi1ahXuv/9++P1+7NixY9Lnh8NhtLe3V2/79u1DLpfDbbfdNuPGN9LASAG6\naQMAlvdML7Q+c+TXcFwHkiDhQ8tvqmfziIiIiFpKTaHVNE0cOHAAmzdvrj4mCAK2bNmC3bt3T+kc\nP/7xj7F582YsWrSotpbOssrUAABYMY35rIOFIfxx8FUAwObe96I90Fa3thERERG1GrmWJ6dSKdi2\njY6OiZe529vb0d/ff97XDw0N4YUXXsC//Mu/1NbKMkmavXVjR097oTUWVtGZCNRc7uqZo7+CCxey\nIOHPVr0fssw1b1NV6efZ7G+aO+zv1sL+bi3s79bS6H6uKbTO1JNPPoloNIqbbprepfJoNFDnFp3d\nsaE8AOCipW1oawvX9tr0SbwyuAcA8P4LrsGqRYvr3r5WMJv9TXOP/d1a2N+thf1N9VBTaE0kEpAk\nCclkcsLjIyMjZ4y+TuYnP/kJtm7dClmeXlbOZkuwy3VTG8myHfSXy131dQaRShVqev0Pdv8ULlwo\noowbe6+t+fWtTpJERKOBWetvmlvs79bC/m4t7O/WUunvRqkpPSqKgnXr1mHnzp3V0VLXdbFz507c\neeed53ztyy+/jGPHjuETn/jEtBtr2w4sq/E/9P0DWVi2CwBY1h2p6T2P507h1aF9AIBrF29BSArP\nSpsXotnqb2oO7O/Wwv5uLexvqoeaJx/cddddeOKJJ/DUU0/h8OHDuO+++6BpWrUawCOPPIJ77rnn\njNf9+Mc/xqWXXopVq1bNvNUNdmQgW72/vKe2RVg/7/8FAECVVHxg2fX1bBYRERFRy6r5Ov2tt96K\nVCqFRx99FMlkEmvXrsVjjz2GtjZvdXwymcTAwMCE1+TzefzqV7/CF7/4xfq0usEqlQM6Yn5EguqU\nX1c0i9iffAMAcN3iLYiotc2FJSIiIqLJTWty6R133IE77rhj0q89+OCDZzwWDofx2muvTeet5kT/\noDfSWuumAgdTh+HCm1ZwWfeldW8XERERUatiDYp30Q0bp5LewqlaQ+ubqbcBACEliMXh5q5DS0RE\nRDSfMLS+y9HTObjeYGnNmwq8NXoIAHBR4gKIAr+1RERERPXCZPUu/eVFWAKApd1TD60jpRSGSl4p\nsIsSFzSiaUREREQti6H1XSqhdVFHCAHf1Kf8vlWeGgAAa9ourHu7iIiIiFoZQ+u7HClXDlhRY6mr\nt1Le1IB2fxs6Au11bxcRERFRK2NoHSdfMjGULgEAltewCMtxHbxZns+6po1TA4iIiIjqjaF1nCOD\nY5sK1FI5YKBwGnnTqzhwUYJTA4iIiIjqjaF1nMqmApIoYEnX1DcGqFQNALgIi4iIiKgRGFrHqWzf\n2tcVhiJP/VtTqc+6JNyLsBpqSNuIiIiIWhlD6ziVygG1TA2wHAuH0u8AAC5i1QAiIiKihmBoLUvl\ndKTzBoDaKgccyR6HYXuvW8P5rEREREQNwdBaVpkaANQ20lqpGiALElbFl9e7WUREREQEhtaq/nLl\nAFURsagjOOXXVeqzrogtgyqpDWkbERERUatjaC2rVA5Y1h2BJE7t21KyNBzJHgfAXbCIiIiIGomh\nFYDrutXpAbVMDXg7/Q4c1wHA+qxEREREjcTQCmA4XUJBswAAyxdNfRFWZT5rQPZjaWRxQ9pGRERE\nRAytAMamBgA1LsIq12ddHV8FSZTq3i4iIiIi8jC0Ajh62gutQZ+MrnhgSq9J6xkMFk4DYH1WIiIi\nokZjaAWQTJcAAIvagxAEYUqveWv07er9Ndy6lYiIiKihGFoBjGR1AEBb1D/l17xVnhoQ98XQFexs\nSLuIiIiIyMPQCmA0qwEA2qK+KT3fdd3qIqw1iQunPDpLRERERNPT8qHVtBxkCt42rFMdaT2eO4mM\n4ZXIuqiNUwOIiIiIGq3lQ2sqp1Xvt08xtD534kUAgCIqWNe+piHtIiIiIqIxLR9aK/NZgamF1pyR\nx67TuwEAV/RsQkiZ+pavRERERDQ9LR9aK/NZganNaf39yZdguTYA4Lq+qxrWLiIiIiIa0/KhdaQc\nWlVZRDignPO5lmPhhZM7AXgLsHrDPQ1vHxERERExtGJ0XLmr81UBeG1oHzKGtxHB9Us4ykpEREQ0\nWxhayyOt7VOYGlBZgNURaOcCLCIiIqJZ1PKhtTI9IHGeRVj9mWM4kj0GALiubwtEoeW/dURERESz\npqWTl+u61ekB56sc8NyJ3wMAfJKKzYsub3jbiIiIiGhMS4fWgmZBN71KAOeqHJDWM3h1aC8A4MpF\nlyMgB2alfURERETkaenQOr7c1blGWn9/8iU4rgMAuG7xloa3i4iIiIgmaunQOjKF0GraJl44+RIA\n4OL2i9Ad6pqVthERERHRmJYOraPjdsNKRCafHrBraA/yZgEAcH3f1bPSLiIiIiKaqMVDqzfSGg0q\nUBXpjK+7rovnjnsLsLqDnVjbduGsto+IiIiIPC0dWivTA9rOMjWgP3sMx/OnAHhbtrLMFREREdHc\naOkUNn43rMm8MfIWAEAWZVzRc9mstYuIiIiIJmrp0Do20jr5fNZ3MkcBAEsjffDL598xi4iIiIga\no2VDq2U7SOfPvrGA7djoz3qhdVVs+Ww2jYiIiIjepWVDazqvw3W9+5OF1lOFQei2AQBYGVs2m00j\nIiIiondp2dA6vtzVZHNaD2eOVO+v5EgrERER0Zxq2dA6cWOBM+ervpM+AsArdRVWQ7PVLCIiIiKa\nRMuG1kqNVlkSEAmpZ3y9sgiLo6xEREREc6+FQ2u53FXED1EQJnwtpaWR0tMAOJ+ViIiIqBm0bGg9\nV7krzmclIiIiai4tG1pHz7Eb1jvl0BpSgugOds5ms4iIiIhoEi0bWkfOsRtWZRHWytgyCO+aOkBE\nREREs68lQ2tRs1DSLQBnVg7QLA0n8gMAODWAiIiIqFm0ZGgdzY0vdzVxpPVI9jhceLsOMLQSERER\nNYfWDK3jarS+e3pAZT6rLEhYFumbzWYRERER0VlMK7Ru374dN954IzZs2IDbb78de/fuPefzDcPA\nN7/5Tdx444245JJLcNNNN+EnP/nJtBpcDxN3w5o4PaBSn3VJpA+KpMxqu4iIiIhocnKtL3j66afx\n0EMP4atf/SouueQSfP/738fdd9+NZ555Bm1tbZO+5vOf/zxSqRS+/vWvY+nSpRgeHobjODNu/HRV\nyl2F/DL86ti3wHEd9Fc2FYizPisRERFRs6g5tD7++OPYtm0btm7dCgC4//778dxzz2HHjh349Kc/\nfcbzn3/+eezatQu/+tWvEI1GAQC9vb0zbPbMnK3c1an8IDTbG4VdxfmsRERERE2jpukBpmniwIED\n2Lx5c/UxQRCwZcsW7N69e9LX/Pa3v8X69evxne98B9deey1uvvlmPPzww9B1fdLnz4ZKuat3L8J6\nh5sKEBERETWlmkZaU6kUbNtGR0fHhMfb29vR398/6WuOHz+OV155Baqq4lvf+hZSqRS+8pWvIJPJ\n4Otf/3pNjZWk+qwbq4y0dsT9kOWxc/ZnvakBXcEOJILRurwX1a7Sz/Xqb2pu7O/Wwv5uLezv1tLo\nfq55ekCtXNeFKIp45JFHEAqFAAD33nsvPv/5z+MrX/kKVFWd8rmi0cCM22M7LlI5b6S1rzuKRCJU\n/VoltF7cdeGEx2lu1KO/af5gf7cW9ndrYX9TPdQUWhOJBCRJQjKZnPD4yMjIGaOvFZ2dneju7q4G\nVgBYuXIlXNfF4OAgli5dOuX3z2ZLsO2ZLeAazWqwHa8Oa1AVkUoVAAApLYPh4igAoC+4uPo4zT5J\nEhGNBurS39T82N+thf3dWtjfraXS341SU2hVFAXr1q3Dzp07cdNNNwHwRlJ37tyJO++8c9LXbNq0\nCc8++yxKpRICAe+D9Pf3QxRF9PT01NRY23ZgWTP7oR9Klar34yFf9XwHR96pPr48smzG70MzV4/+\npvmD/d1a2N+thf1N9VDz5IO77roLTzzxBJ566ikcPnwY9913HzRNw2233QYAeOSRR3DPPfdUn//h\nD38Y8Xgc9957Lw4fPow//elP+MY3voGPf/zjNU0NqJeJGwuM1WitLMIKygF0Bztnu1lEREREdA41\nz2m99dZbkUql8OijjyKZTGLt2rV47LHHqjVak8kkBgYGqs8PBoP413/9V3zta1/DJz7xCcTjcXzo\nQx/C3/7t39bvU9SgsrGAKAiIh88MrStjyyAKnDBORERE1EymtRDrjjvuwB133DHp1x588MEzHlux\nYgW++93vTuet6q6ysUAiokIUBQCAZuk4kfeCNktdERERETWflhtSnGxjgaPZ43Bcb64NQysRERFR\n82m50FoZaR2/scA75a1bJUHCsuiSOWkXEREREZ1dy4XWypzW8SOtlfqsfZFeqJIyJ+0iIiIiorNr\nqdCqGzbyJRMA0F6uHOC6Lo5kjgEAVkaXzVnbiIiIiOjsWiq0jubGl7vyRlqHisMoWEUAwIrY1Dc6\nICIiIqLZ01KhdWRcjdbKnNb+7LHqY8s50kpERETUlFoqtFbmswJjI6395UVYMTWCNn98TtpFRERE\nROfWYqHVG2n1qxKCfq9EbWWkdUVsGQRBmLO2EREREdHZtVRofXe5K83ScCo/CABYHuV8ViIiIqJm\n1VKhtTI9IFGuHHA0ewIuXADeSCsRERERNaeWCq2pXLlGa2TiIixRELE00jdn7SIiIiKic2up0Jop\neKE1HlYBjC3C6gtzUwEiIiKiZtYyoVU3bJR0GwAQD/u8TQXGLcIiIiIioubVMqE1XRgrdxULqxgu\njSBvFgAAK7kIi4iIiKiptU5ozY2F1njYV50aAADLOdJKRERE1NRaJrRmCkb1fjzsq04NiKhhtPsT\nc9UsIiIiIpqClgmt6bwXWgUA0ZBSHWldEeWmAkRERETNroVCqzc9IBJSYbkWTha8TQVWxDiflYiI\niKjZtUxozZRDazyk4lj2OBzXAeCNtBIRERFRc2uZ0FqZHhCP+NCfGbepQJSbChARERE1uxYKrd5I\nayykVnfCWhxeBJ+kzmWziIiIiGgKWii0eiOtsZA6YREWERERETW/lgitummjpFsAACWoIWfmAXAR\nFhEREdF80RKhtbIICwA0JVm9z5FWIiIiovmhJUJrZWoAAKSd0wCAsBJCR6BtrppERERERDVokdA6\nNtI6ZJwC4E0N4KYCRERERPNDS4TWTGU3LNHGYKm8qQCnBhARERHNGy0RWisjraG2wtimAlyERURE\nRChJKlsAABkFSURBVDRvtEho9UZaffEcAECAgKWRJXPZJCIiIiKqQYuEVm+kVQilAHibCvhl31w2\niYiIiIhq0BKhNVPwRlpN1Quty6IcZSUiIiKaT1ojtOZ1QDZgCAUAwJJI7xy3iIiIiIhqseBDq2Ha\nKGgWxECu+lhfePEctoiIiIiIarXgQ2tlaoAYygLwFmEtDvfMZZOIiIiIqEYLPrRWF2EFvdDaHeyE\nKqlz2SQiIiIiqtGCD62VjQXEoDc9oI/zWYmIiIjmnQUfWlN5HRBsCAFvEVZfmKGViIiIaL5Z8KE1\nkzcgBPMQBBcAsCTCRVhERERE882CD63pvA6xPJ8V4EgrERER0Xy04ENrZlxojftiCKuhOW4RERER\nEdVqwYfWdN6olrvipgJERERE89OCD62pvAbh/2/v/oOjKu89jn82uwv5RcgmIWj4VQiYZPIDaWsp\nKYwzMO0tVNKICuNUKFT6ywrlzpRemTqGH4IwiiiMndsMlGAbLXIZyhUjrfV29A9TEBRTU7FTCAgS\niJssJCGETXbP/WPZlW2iZkPC7jn7fs0ws3n2nOw38wX8+PCc50lql8TSAAAAALOydGjt6vbpii7J\nZvdJkkbzEBYAAIApWTq0Xmr3hj2ENYaZVgAAAFOydGi92O6VLSVwqMDQhERlJLqiXBEAAAD6w+Kh\n9dOdA25NvkU2my3KFQEAAKA/LB1aPW2dodA6bvjoKFcDAACA/rJ0aG267JHN2SVJGpfGQ1gAAABm\nZenQeuHK+dDr0ezRCgAAYFr9Cq3V1dWaOXOmSkpKNH/+fNXV1X3mtYcPH1Z+fn7Yr4KCAjU3N/e7\n6L5q6W6SJNmMBN2SnD3onwcAAIDB4Yj0hpqaGm3cuFHr1q1TcXGxdu3apaVLl+rgwYPKyMjo9R6b\nzaY//elPSkn59AjVzMzM/lfdR5cVCMZJfpfsCfZB/zwAAAAMjohnWquqqrRgwQKVl5crNzdXa9as\nUWJiovbu3fu592VkZCgzMzP062bwOi9KkoY7RtyUzwMAAMDgiCi0dnV1qb6+XtOmTQuN2Ww2lZaW\n6tixY595n2EY+u53v6vp06frBz/4gd55553+V9xHlzovS0M6JEnZQ0cO+ucBAABg8ES0PMDj8cjn\n8ykrKytsPDMzUw0NDb3eM2LECK1du1ZFRUXyer166aWXtGjRIu3Zs0cFBQURFWu39z1jn2w5E3o9\nNm2UHA5LP3NmKcE+R9JvmBf9ji/0O77Q7/gy2H2OeE1rpMaPH6/x48eHvr799tt15swZVVVVadOm\nTRF9r7S0pD5fe/bDwM4BhiEVj5kolyvlC+5ArImk3zA/+h1f6Hd8od8YCBGFVpfLJbvdLrfbHTbe\n3NzcY/b18xQXF/driUBr6xX5fP4+XXvCfVqSZHQmK9nulMdzOeLPQ3TY7QlKS0uKqN8wL/odX+h3\nfKHf8SXY78ESUWh1Op0qLCxUbW2tZs2aJSmwXrW2tlYLFy7s8/c5fvy4srMj34LK5/Oru7tvv+mb\nrlyQJPk70pSa6OjzfYgdkfQb5ke/4wv9ji/0GwMh4uUBixcv1qpVq1RUVBTa8qqzs1Pz5s2TJG3e\nvFlNTU2hf/rftWuXRo8erUmTJunq1at66aWXdOjQIf32t78d2J/kOl3+brX6WyRJTm+6nA62uwIA\nADCziEPrnDlz5PF4tHXrVrndbhUUFGj79u2hPVrdbrcaGxtD13d1dWnTpk1qampSYmKi8vLyVFVV\npTvuuGPgfop/09h+XoYC/0eXopuzvRYAAAAGj80wDCPaRfSVx3O5T/+88Na5w6o+/j+SpLGflOu/\nFpQOdmkYQA5HglyulD73G+ZGv+ML/Y4v9Du+BPs9WCy5B8WZtnOSJMM7VJkp6VGuBgAAADfKkqH1\nbHsgtPo70pSeOjTK1QAAAOBGWS60+g2/zrYFQ+swDU8dEuWKAAAAcKMsF1rPtZ+X1++VJPnbhzPT\nCgAAYAGWC60NradDr/3t6UpnphUAAMD0LBdaT14KhFZ/Z7LUPVTDmWkFAAAwPcuF1oZgaG0P7BqQ\nnsJMKwAAgNlZKrS2edv1yZVmSYHQmjzUoSFOTsMCAAAwO0uF1uAsq3RtPeswlgYAAABYgaVCa3A9\nq83vkNGRquEsDQAAALAES4XW4M4BjqsuSQlsdwUAAGARlgmtPr9Pp1vPBl63XXsIi+2uAAAALMEy\nofVs+zl1+bskSZ2eNEliphUAAMAiLBNaGy59FHrtax8uScpII7QCAABYgWVC68lLpyRJLmeG5Ass\nC3ANS4xiRQAAABgolgmtDa2BmdYMx62hMWZaAQAArMESofXi1Utq6fRIkpJ9IyRJ9gSb0pJ5EAsA\nAMAKLBFar1/Par+SKSmwc0BCgi1aJQEAAGAAWSS0BvZnTXIkqrMtSZLkSmM9KwAAgFVYIrQGT8L6\nUtpYeVqvSpIyOMIVAADAMkwfWrv83TrTFjhUYPzwcfK0BUKri9AKAABgGaYPrWfbPla34ZMkjUsd\no9bLXklSBttdAQAAWIbpQ2twaYBNNqUnjJRxbZyZVgAAAOswfWgNPoR1a8pIdXR8ultABg9iAQAA\nWIapQ6thGKGZ1vHDx4bWs0rMtAIAAFiJqUOr5+pFXfK2SpLGD/+SWto6JQUOFhiewsECAAAAVmHq\n0BqcZZWkCddtd8XBAgAAANZi6tAaXM+a4khWdvIItYS2u2I9KwAAgJWYPLQGjm8dP3ysbDabPNeW\nB7CeFQAAwFpMG1pPt57RmfaPJQUOFZAUmmnNSCO0AgAAWIkj2gVE6uLVS/rfEwd16PzR0Fiea6K6\nfX61tgcOFmB5AAAAgLWYJrRe7fbqlROv6WDD/8nr75IkJdqHam7utzV++Di5L10JHSyQwfIAAAAA\nSzFNaF3x6mo1d3gkBU6/Ks25Q3dN+A+lDRkmSeF7tLI8AAAAwFJME1qDgfW29FzdM2muRg/LCXu/\npfXT0JrB8gAAAABLMU1oHZV2i8omfFuFrgLZbD33YA3OtCbYOFgAAADAakwTWrfMrpDHc1nd3f5e\n329pDWx3lT6MgwUAAACsxrRbXv07T+hgAdazAgAAWI1lQmtoj1bWswIAAFiOhUIrp2EBAABYlSVC\n6/UHC7BHKwAAgPVYIrReavd+erBAGssDAAAArMYSoTW4NEBieQAAAIAVWSK0Xn8aFjOtAAAA1mOJ\n0Bo8DYuDBQAAAKzJGqH12vKA4akcLAAAAGBFlgitweUBGWmsZwUAALAiS4TW4PIAFwcLAAAAWJIl\nQqvn2vIA9mgFAACwJtOH1m6fX5c4WAAAAMDSTB9arz9YwMV2VwAAAJbUr9BaXV2tmTNnqqSkRPPn\nz1ddXV2f7jt69KgKCwt199139+djexW2RyszrQAAAJYUcWitqanRxo0btXz5cu3bt0/5+flaunSp\nWlpaPve+trY2PfLII5o2bVq/i+0Np2EBAABYX8ShtaqqSgsWLFB5eblyc3O1Zs0aJSYmau/evZ97\nX0VFhebOnavbb7+938X2JrhzgM0W2KcVAAAA1hNRaO3q6lJ9fX3YbKnNZlNpaamOHTv2mfft3btX\nZ8+e1cMPP9z/Sj9DcHlAeupQ2RNMv0QXAAAAvXBEcrHH45HP51NWVlbYeGZmphoaGnq959SpU9qy\nZYteeOEFJdxgqLTbe95/sT14sECiHA5CqxUE+9xbv2E99Du+0O/4Qr/jy2D3OaLQGim/369f/OIX\nWrZsmcaOHStJMgzjC+76bGlpST3GWju6JEm3ZKbI5Urp9/dG7Omt37Au+h1f6Hd8od8YCBGFVpfL\nJbvdLrfbHTbe3NzcY/ZVki5fvqz3339fx48f19q1ayUFgqxhGCoqKtKOHTs0derUPn9+a+sV+Xz+\nsLEmT4ckKTXJIY/nciQ/DmKU3Z6gtLSkXvsN66Hf8YV+xxf6HV+C/R4sEYVWp9OpwsJC1dbWatas\nWZICM6e1tbVauHBhj+tTU1N14MCBsLHq6modOnRI27Zt06hRoyIq1ufzq7v709/0Pr8/tDwgPWVI\n2Hswv3/vN6yNfscX+h1f6DcGQsTLAxYvXqxVq1apqKhIxcXF2rVrlzo7OzVv3jxJ0ubNm9XU1KRN\nmzbJZrNp4sSJYfdnZmZq6NChys3NveHiL7V7FVxtkMHBAgAAAJYVcWidM2eOPB6Ptm7dKrfbrYKC\nAm3fvl0ZGRmSJLfbrcbGxgEvtDfB7a4k9mgFAACwMptxI09G3WQez+Wwf144/MEF/ff+eknSUw+V\nMttqEQ5HglyulB79hjXR7/hCv+ML/Y4vwX4PFlPvQRHco5WDBQAAAKzN1KE1uDyAgwUAAACszdRJ\nz9PWKYn1rAAAAFZn8tB67TQsQisAAIClmTq0tlwLra5hPIAFAABgZaYNrdcfLJCRxkwrAACAlZk2\ntF5/sABrWgEAAKzNtKG1sbkj9HpE+uCdcwsAAIDoM21oPX2hTZKUYLNp9IjB28gWAAAA0Wfe0Ho+\nEFpzspLldNijXA0AAAAGk2lD60fXZlrHjRwW5UoAAAAw2EwZWq9c7dYFzxVJ0thbCK0AAABWZ8rQ\nGpxllZhpBQAAiAcmDa3toddjslOjWAkAAABuBlOG1uDOASMzkpU01BHlagAAADDYTB1ax41klhUA\nACAemC60ert8anQHDhZgPSsAAEB8MF1oPfvJZfmvnd/KzgEAAADxwXSh9TQ7BwAAAMQd84XWaydh\nZaYlKjXJGeVqAAAAcDOYLrQG92gdy0NYAAAAccNUobXb59fZTwJ7tI5jPSsAAEDcMFVoPee+rG5f\n4CEs1rMCAADED1OF1uB6VkkaS2gFAACIG6YKraeuhda0lCFKTx0S5WoAAABws5grtDa2SgosDbDZ\nbFGuBgAAADeLaUKrz2/oowvBh7DYOQAAACCemCa0NrrbdbXLJ0kam816VgAAgHhimtB64uyl0Gu2\nuwIAAIgv5gmtHwdCa/JQh7KGJ0a5GgAAANxMpgmtJz++KCkwy8pDWAAAAPHFNKE1uDyA41sBAADi\nj2lCa/uVLkmchAUAABCPTBNag3gICwAAIP6YKrQOcSZopCs52mUAAADgJjNVaB03cpgSEngICwAA\nIN6YK7SyNAAAACAumSq0fumWtGiXAAAAgCgwVWhlphUAACA+mSa05mSlaNSIlGiXAQAAgCgwTWjd\n8p93ymE3TbkAAAAYQKZJgcmJzmiXAAAAgCgxTWgFAABA/CK0AgAAIOYRWgEAABDzCK0AAACIeYRW\nAAAAxDxCKwAAAGIeoRUAAAAxj9AKAACAmNev0FpdXa2ZM2eqpKRE8+fPV11d3Wdee/ToUd1///2a\nOnWqJk+erNmzZ6uqqqq/9QIAACAOOSK9oaamRhs3btS6detUXFysXbt2aenSpTp48KAyMjJ6XJ+c\nnKyFCxcqLy9PSUlJOnr0qB577DGlpKTovvvuG5AfAgAAANYW8UxrVVWVFixYoPLycuXm5mrNmjVK\nTEzU3r17e72+oKBAc+bMUW5urnJycjR37lxNnz5dR44cueHiAQAAEB8iCq1dXV2qr6/XtGnTQmM2\nm02lpaU6duxYn77HP/7xD7377rv62te+FlmlAAAAiFsRLQ/weDzy+XzKysoKG8/MzFRDQ8Pn3nvn\nnXeqpaVFfr9fDz/8sO65556Ii7XbeW4sHgT7TL/jA/2OL/Q7vtDv+DLYfY54TWt/vfDCC+ro6NCx\nY8f01FNPady4cZozZ05E3yMtLWmQqkMsot/xhX7HF/odX+g3BkJEodXlcslut8vtdoeNNzc395h9\n/XejRo2SJE2aNElut1vbtm2LOLQCAAAgPkU0j+t0OlVYWKja2trQmGEYqq2t1ZQpU/r8fXw+n7xe\nbyQfDQAAgDgW8fKAxYsXa9WqVSoqKgptedXZ2al58+ZJkjZv3qympiZt2rRJUmBP15ycHE2YMEGS\ndPjwYe3cuVPf//73B/DHAAAAgJVFHFrnzJkjj8ejrVu3yu12q6CgQNu3bw/t0ep2u9XY2Bi63jAM\nPf300zp79qwcDofGjBmjX/7yl1qwYMHA/RQAAACwNJthGEa0iwAAAAA+D3tQAAAAIOYRWgEAABDz\nCK0AAACIeYRWAAAAxDxCKwAAAGIeoRUAAAAxL+ZDa3V1tWbOnKmSkhLNnz9fdXV10S4JA+A3v/mN\n7r33Xn35y19WaWmpfvazn6mhoaHHdc8++6ymT5+uyZMna8mSJTp9+nQUqsVAq6ysVH5+vp544omw\ncfptHRcuXNDKlSs1depUTZ48WWVlZaqvrw+7hn5bg9/v1zPPPKNZs2Zp8uTJ+uY3v6lf//rXPa6j\n3+Z05MgR/eQnP9GMGTOUn5+v119/vcc1X9Rbr9erNWvWaOrUqZoyZYqWL1+u5ubmiGuJ6dBaU1Oj\njRs3avny5dq3b5/y8/O1dOlStbS0RLs03KAjR47ogQce0J49e7Rz5051d3frwQcfVGdnZ+iayspK\nVVdXa926ddqzZ4+SkpL04IMPcgSwydXV1Wn37t3Kz88PG6ff1tHa2qr7779fQ4YM0Y4dO1RTU6NH\nHnlEaWlpoWvot3VUVlZq9+7dqqio0KuvvqqVK1dq+/bt+v3vfx92Df02p46ODhUUFKiiokI2m63H\n+33p7fr16/XGG29o27Ztqq6uVlNTk5YtWxZ5MUYMu++++4x169aFvvb7/caMGTOMysrKKFaFwdDc\n3Gzk5eUZb7/9dmjsG9/4hrFz587Q121tbUZxcbHxyiuvRKFCDIT29nbjW9/6lvHWW28ZDzzwgLFh\nw4bQe/TbOp588knje9/73udeQ7+t48c//rHxq1/9Kmxs2bJlxsqVK0Nf029ryMvLM/7yl7+EjX1R\nb9va2ozCwkLjz3/+c+iaEydOGHl5ecZ7770X0efH7ExrV1eX6uvrNW3atNCYzWZTaWmpjh07FsXK\nMBja2tpks9mUnp4uSTpz5ozcbre+/vWvh65JTU3V5MmT6b+JrV27VjNnzgz7cy3Rb6v561//qqKi\nIv385z9XaWmp7r77bu3Zsyf0Pv22lilTpqi2tlanTp2SJB0/flzvvPOO7rzzTkn028r60tu///3v\n8vl8YX/vT5gwQTk5OXr33Xcj+jzHwJQ98Dwej3w+n7KyssLGMzMze137CPMyDEMbNmzQV77yFU2c\nOFGS5Ha7ZbPZeu2/2+2ORpm4Qa+88oo++OAD7d27t8d79Ntazpw5oxdffFFLlizRT3/6U9XV1enx\nxx+X0+lUeXk5/baYH/3oR2pvb9fs2bNlt9vl9/u1YsUKfec735HEn28r60tvm5ub5XQ6lZqa+pnX\n9FXMhlbEj9WrV+tf//qXXnzxxWiXgkFy/vx5bdiwQTt37pTT6Yx2ORhkfr9fJSUlWrFihSQpPz9f\n//znP/WHP/xB5eXlUa4OA62mpkYHDhzQ008/rYkTJ+qDDz7Q+vXrlZ2dTb8xoGJ2eYDL5ZLdbu+R\nwpubm3skepjX2rVr9eabb+p3v/udsrOzQ+NZWVkyDIP+W8T777+vlpYWzZs3T4WFhSosLNTbb7+t\n559/XkVFRfTbYrKzs5Wbmxs2lpubq8bGRkn8+baaJ598Uj/84Q81e/ZsTZo0SWVlZVq8eLEqKysl\n0W8r60tvs7Ky1NXVpfb29s+8pq9iNrQ6nU4VFhaqtrY2NGYYhmprazVlypQoVoaBsnbtWr3++ut6\n/vnnlZOTE/bemDFjlJWVpb/97W+hsfb2dr333nv034RKS0v18ssv649//KP279+v/fv3q6ioSGVl\nZdq/fz/9tpgpU6b0WMbV0NAQ+nNOv63lypUrstvtYWMJCQny+/2S6LeV9aW3RUVFstvtYXnu5MmT\nOnfuXMT9t69evXr1gFQ+CFJSUrR161bdeuutcjqdeuaZZ/Thhx9q/fr1SkpKinZ5uAGrV6/WgQMH\ntHXrVo0YMUIdHR3q6OiQ3W6XwxFYteLz+VRZWanc3Fx5vV49/vjj8nq9evTRR3v8BYnY5nQ6lZGR\nEfbr5Zdf1pgxY1RWViaJfltJTk6OnnvuOdntdmVnZ+vNN9/Uc889pxUrVui2226TRL+t5OTJk9q3\nb5/Gjx8vp9OpQ4cOacuWLSorKws9fEO/zaujo0MnTpzQJ598ot27d6ukpESJiYnq6urSsGHDvrC3\nQ4YMUVNTk6qrq5Wfn6+LFy+qoqJCOTk5euihhyKqxWYYhjFIP+eAqK6u1o4dO+R2u1VQUKBHH31U\nxcXF0S4LNyg/P7/X/d6eeOKJsDVQ27Zt0+7du9XW1qavfvWreuyxxzRu3LibWSoGyaJFi1RQUKBV\nq1aFxui3dbzxxht66qmn9NFHH2n06NFasmSJ7r333rBr6Lc1dHR06Nlnn9Vrr72mlpYWZWdn6667\n7tJDDz0UmoSQ6LdZHT58WIsWLerx3+zy8vLQATFf1Fuv16tNmzbpwIED8nq9mjFjhioqKpSZmRlR\nLTEfWgEAAICYXdMKAAAABBFaAQAAEPMIrQAAAIh5hFYAAADEPEIrAAAAYh6hFQAAADGP0AoAAICY\nR2gFAABAzCO0AgAAIOYRWgEAABDzCK0AAACIef8PbPU3UB5ik98AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b463914a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(scores[scores.phase==\"train\"].epoch, scores[scores.phase==\"train\"].roc_auc)\n",
    "plt.plot(scores[scores.phase==\"test\"].epoch, scores[scores.phase==\"test\"].roc_auc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g3mUpGFvM5BQbKa9tA6Ch2UxDs7nf+351qKrHKG96vJ7cEhPf5FQDMDUmkNuX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\nAEag2AIAAMAIFFsAAAAY4RvEBXC6k7bHrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b46c5c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(scores[scores.phase==\"train\"].epoch, scores[scores.phase==\"train\"].accuracy)\n",
    "plt.plot(scores[scores.phase==\"test\"].epoch, scores[scores.phase==\"test\"].accuracy)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FAgAAyDU5EVqL+kZaTVPqjjCvFQAAINeMKLRu2LBBy5cv18KFC7V69Wpt3779rMc/++yz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\nRLTo/X/6HD+d5z3+OwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b4c0dd6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(scores[scores.phase==\"train\"].epoch, scores[scores.phase==\"train\"].log_loss)\n",
    "plt.plot(scores[scores.phase==\"test\"].epoch, scores[scores.phase==\"test\"].log_loss)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_numeric_submit = test_df[numeric_features].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 7)"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_numeric_submit.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_text_submit = np.zeros((418, max_name_length + max_ticket_length + max_embarked_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for e, i in enumerate(test_df[text_features].iterrows()):\n",
    "    name = i[1][\"Name\"].lower()\n",
    "    ticket = i[1][\"Ticket\"].lower()\n",
    "    emb = i[1][\"Embarked\"].lower()\n",
    "    for p, w in enumerate(name):\n",
    "        X_text_submit[e, p] = vocab[w]\n",
    "    for p, w in enumerate(ticket):\n",
    "        X_text_submit[e, p + max_name_length] = vocab[w]\n",
    "    for p, w in enumerate(emb):\n",
    "        X_text_submit[e, p + max_name_length + max_ticket_length] = vocab[w] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 101)"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_text_submit.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 7)"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_numeric_submit.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "submit_probas = model.predict([X_numeric_submit, X_text_submit])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35645933014354064"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(submit_probas > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39461883408071746"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>0</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>0</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name  Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    0   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)    0   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    0   \n",
       "3          895       3                              Wirz, Mr. Albert    0   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare  Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292      0        Q  \n",
       "1  47.0      1      0   363272   7.0000      1        S  \n",
       "2  62.0      0      0   240276   9.6875      0        Q  \n",
       "3  27.0      0      0   315154   8.6625      1        S  \n",
       "4  22.0      1      1  3101298  12.2875      0        S  "
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open(\"/data/pavel/submit.out\", \"w\") as f:\n",
    "    print(\"PassengerId,Survived\", file=f)\n",
    "    for i, s in zip(test_df.PassengerId.values, submit_probas):\n",
    "        print(i, 1 if s > 0.5 else 0, file=f, sep=\",\")"
   ]
  }
 ],
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