Titanic_Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run Data_Helpers.ipynb\n",
    "%run Keras_Helpers.ipynb\n",
    "\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thanks, https://www.kaggle.com/dmilla/introduction-to-decision-trees-titanic-dataset\n",
    "def get_title(name):\n",
    "    title_search = re.search(r'\\b([A-Za-z]+)\\.', name)\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return ''\n",
    "\n",
    "def prepare_data(df):\n",
    "    df = df.filter(items=[\n",
    "        'name',\n",
    "        'sex',\n",
    "        'age',\n",
    "        'sibsp',\n",
    "        'parch',\n",
    "        'ticket',\n",
    "        'fare',\n",
    "        'pclass',\n",
    "        'cabin',\n",
    "        'embarked',\n",
    "        'home.dest',\n",
    "        'survived'\n",
    "    ])\n",
    "    extract_unknown(df, 'age')\n",
    "    extract_unknown(df, 'fare')\n",
    "    split_categories(df, 'sex', ['male', 'female'])\n",
    "    split_categories(df, 'pclass', ['1', '2', '3'])\n",
    "    split_categories(df, 'embarked', ['S', 'C', 'Q'])\n",
    "    df['family_size'] = df['sibsp'] + df['parch'] + 1\n",
    "    df['alone'] = df['family_size'] == 1\n",
    "    df['title'] = df['name'].apply(get_title)\n",
    "    df['title_Rare'] = df['title'].isin([\n",
    "        'Lady', 'Countess', 'Dona',\n",
    "        'Sir', 'Don',\n",
    "        'Col', 'Capt', 'Rev', 'Dr', 'Major', 'Jonkheer',\n",
    "    ]).astype(int)\n",
    "    split_categories(df, 'title', [\n",
    "        'Mrs', 'Ms', 'Miss', 'Mme', 'Mlle', 'Dona', 'Lady', 'Countess',\n",
    "        'Mr', 'Master',                     'Sir', 'Don',\n",
    "        'Col', 'Capt', 'Rev', 'Dr', 'Major', 'Jonkheer',\n",
    "    ])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = pd.read_csv('data/titanic.csv')\n",
    "[raw_train_data, raw_test_data, raw_validate_data] = divide_data(raw_data, [0.8, 0.1, 0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_data = prepare_data(raw_train_data)\n",
    "test_data = prepare_data(raw_test_data)\n",
    "validate_data = prepare_data(raw_validate_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
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       "      <th>sex_female</th>\n",
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       "      <th>survived</th>\n",
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       "      <th>alone</th>\n",
       "      <th>title_Mrs</th>\n",
       "      <th>title_Ms</th>\n",
       "      <th>title_Miss</th>\n",
       "      <th>title_Mme</th>\n",
       "      <th>title_Mlle</th>\n",
       "      <th>title_Dona</th>\n",
       "      <th>title_Lady</th>\n",
       "      <th>title_Countess</th>\n",
       "      <th>title_Mr</th>\n",
       "      <th>title_Master</th>\n",
       "      <th>title_Sir</th>\n",
       "      <th>title_Don</th>\n",
       "      <th>title_Col</th>\n",
       "      <th>title_Capt</th>\n",
       "      <th>title_Rev</th>\n",
       "      <th>title_Dr</th>\n",
       "      <th>title_Major</th>\n",
       "      <th>title_Jonkheer</th>\n",
       "      <th>title_Rare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
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       "      <td>B42</td>\n",
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       "      <th>790</th>\n",
       "      <td>Elias, Mr. Joseph</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2675</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2292</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Syria Ottawa, ON</td>\n",
       "      <td>0</td>\n",
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       "      <th>892</th>\n",
       "      <td>Johansson, Mr. Karl Johan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
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       "      <td>347063</td>\n",
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       "      <th>133</th>\n",
       "      <td>Goldenberg, Mr. Samuel L</td>\n",
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       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>Canavan, Miss. Mary</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "                             name  sex_male  sex_female  age_known   age  \\\n",
       "137  Graham, Miss. Margaret Edith         0           1          1  19.0   \n",
       "790             Elias, Mr. Joseph         1           0          1  39.0   \n",
       "892     Johansson, Mr. Karl Johan         1           0          1  31.0   \n",
       "133      Goldenberg, Mr. Samuel L         1           0          1  49.0   \n",
       "702           Canavan, Miss. Mary         0           1          1  21.0   \n",
       "\n",
       "     sibsp  parch  ticket  fare_known     fare  pclass_1  pclass_2  pclass_3  \\\n",
       "137      0      0  112053           1  30.0000         1         0         0   \n",
       "790      0      2    2675           1   7.2292         0         0         1   \n",
       "892      0      0  347063           1   7.7750         0         0         1   \n",
       "133      1      0   17453           1  89.1042         1         0         0   \n",
       "702      0      0  364846           1   7.7500         0         0         1   \n",
       "\n",
       "    cabin  embarked_S  embarked_C  embarked_Q                     home.dest  \\\n",
       "137   B42           1           0           0                 Greenwich, CT   \n",
       "790   NaN           0           1           0              Syria Ottawa, ON   \n",
       "892   NaN           1           0           0                           NaN   \n",
       "133   C92           0           1           0  Paris, France / New York, NY   \n",
       "702   NaN           0           0           1                           NaN   \n",
       "\n",
       "     survived  family_size  alone  title_Mrs  title_Ms  title_Miss  title_Mme  \\\n",
       "137         1            1   True          0         0           1          0   \n",
       "790         0            3  False          0         0           0          0   \n",
       "892         0            1   True          0         0           0          0   \n",
       "133         1            2  False          0         0           0          0   \n",
       "702         0            1   True          0         0           1          0   \n",
       "\n",
       "     title_Mlle  title_Dona  title_Lady  title_Countess  title_Mr  \\\n",
       "137           0           0           0               0         0   \n",
       "790           0           0           0               0         1   \n",
       "892           0           0           0               0         1   \n",
       "133           0           0           0               0         1   \n",
       "702           0           0           0               0         0   \n",
       "\n",
       "     title_Master  title_Sir  title_Don  title_Col  title_Capt  title_Rev  \\\n",
       "137             0          0          0          0           0          0   \n",
       "790             0          0          0          0           0          0   \n",
       "892             0          0          0          0           0          0   \n",
       "133             0          0          0          0           0          0   \n",
       "702             0          0          0          0           0          0   \n",
       "\n",
       "     title_Dr  title_Major  title_Jonkheer  title_Rare  \n",
       "137         0            0               0           0  \n",
       "790         0            0               0           0  \n",
       "892         0            0               0           0  \n",
       "133         0            0               0           0  \n",
       "702         0            0               0           0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_wide(train_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model_output(df):\n",
    "    return df.filter(items=['survived'])\n",
    "\n",
    "def make_model(df_in, df_out):\n",
    "    # https://keras.io/models/sequential/\n",
    "    model = tf.keras.Sequential([\n",
    "        # https://keras.io/layers/core/\n",
    "        tf.keras.layers.Dense(units=16, activation='relu', input_dim=df_in.shape[1]),\n",
    "        tf.keras.layers.Dense(units=16, activation='relu'),\n",
    "        tf.keras.layers.Dense(units=df_out.shape[1], activation='sigmoid')\n",
    "    ])\n",
    "    model.compile(\n",
    "        loss      = tf.keras.losses.binary_crossentropy, # https://keras.io/losses/\n",
    "        optimizer = tf.keras.optimizers.Nadam(), # https://keras.io/optimizers/\n",
    "        metrics   = ['accuracy']\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting none...\n",
      "  train: loss = 0.65926445  accuracy = 0.6294    test: loss = 0.66918685  accuracy = 0.6107\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete\n"
     ]
    }
   ],
   "source": [
    "resultsNone = train_neural_networks(make_model, train_data, test_data, [\n",
    "    InputFn('none', lambda df: df.filter(items=[]).assign(One=1)),\n",
    "], make_model_output, max_epochs = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex...\n",
      "  train: loss = 0.52678925  accuracy = 0.7765    test: loss = 0.49479942  accuracy = 0.8015\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting age known...\n",
      "  train: loss = 0.65360421  accuracy = 0.6294    test: loss = 0.66890919  accuracy = 0.6107\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting age...\n",
      "  train: loss = 0.63704866  accuracy = 0.6523    test: loss = 0.7182547  accuracy = 0.5954\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete\n"
     ]
    }
   ],
   "source": [
    "models1 = train_neural_networks(make_model, train_data, test_data, [\n",
    "    InputFn('sex', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "    ])),\n",
    "    InputFn('age known', lambda df: df.filter(items=[\n",
    "        'age_known',\n",
    "    ])),\n",
    "    InputFn('age', lambda df: df.filter(items=[\n",
    "        'age_known',\n",
    "        'age',\n",
    "    ])),\n",
    "], make_model_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / embarked...\n",
      "  train: loss = 0.51342809  accuracy = 0.7765    test: loss = 0.4580018  accuracy = 0.8015\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / class...\n",
      "  train: loss = 0.46444055  accuracy = 0.7861    test: loss = 0.48856214  accuracy = 0.7481\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / family...\n",
      "  train: loss = 0.48751706  accuracy = 0.7908    test: loss = 0.46337983  accuracy = 0.8321\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / embarked / class...\n",
      "  train: loss = 0.45630595  accuracy = 0.7994    test: loss = 0.47963564  accuracy = 0.7786\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / embarked / class / family...\n",
      "  train: loss = 0.39042285  accuracy = 0.8223    test: loss = 0.68886773  accuracy = 0.7252\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / age...\n",
      "  train: loss = 0.48776174  accuracy = 0.7956    test: loss = 0.69675118  accuracy = 0.7939\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pdhx3X+juZ+bynBEzVnoDnVENEznoPDWw/W85Ff53163LL1kLO71/YMcWkZ7OAd4DLCFIwA8Gzh6kY8cIxpA/HDgV+KWZjehZyczONrO5ZjYXYOl+Xw42fOnJQQpDOOJiDc4gUiby3TfkauABd/+kmVUB3cb0Cvs3bnL3DWllU929qcdxfgtcA9zQY/9e+1QS3M10WY9jnNGfr2QjZoyx9bS0rMh1F+nLQPq6p5Jw6aity/9nKVTVb/txRSSrsN08ZRt2XQKk34gyMSxL1ww84e6dwBtm9hpBMv9UjxhmA7MBpu8Q9Q07HAYf/+42hCQiUv5ySt7DmfSa3b3dzA4H9gVucPe1WfZpBN5PMCMf7t4BdPSo9gHgHDM7Jjz2WcCJwNHpldz9H2Y2pZen6bVPpbtfBgxoCLOu+xuHLbx/IIeRwfDATHj8Z1uXX7JWowKJ5JmZ1QBnAnsBm2dEcvcz+tj1KWCame1EkLSfAvQcSeYPBFfcf2NmYwi60fTZJceiGm1GRIauXLvN3AkkzWwqwdWPScDNfeyzE7CCoFF+xsx+ZWbdLpG6+x3Ag8BtZvYZ4AzgU/2IP5c+lZuZ2Wgz+zlwgJnNzFDnODObvam1FYBEXKMaFEWiA244IejX3jNx/8brwde7StxFCuFGYHvgI8DfCa6gb8i6B+DuCeBcgjb+FeB2d3/JzC41s+PDag8Cq8zsZeBhggmgVvV1bIsoeReRoSvXbjMpd0+Y2ceBn7r7T83smRyOfSDwZXd/wsyuBs4HLkqv5O5XhlfMrwN2cfeWfp5DzsJ/Cuf0Uede4N699jvgrLmpEYyta2DHfAVUqdYtgY4WGLsbvP0cPHoVfOJXue//f/8FT/VS/4J3IK57EEQKbKq7f8rMTnD335nZzcCjuezo7vcD9/couzht2YGvhz85M40GJiJDWK4tYKeZnQqcDhwXlsX72KeZoKvNE+H6HILkvRszOwzYG7gbuITgSk2uculTuU0iGB0eo33N24NxuKHlR3sGj7PWwS/CG0jf/01YMi/7fnPOhBd7GQjoM3Ng2lGDG6OI5KozfFxrZnsD7wB9jseeT3U1VcV8ehGRoso1ef88wRXr77n7G2Efxhuz7eDu75jZYjPbzd3nA0fQfXxfzOwAgm44xwJvADeZ2Xfd/cIc48qlT+U2iceMfSMLGWZtsH4pNOwwGIetHC0r4IdTg0mQph4F3x0LDRPhK09vqZM+lOPP35v5WP/3DXjql1uXf3Mh1I8evJhFZFvMNrORwIXAPQQzJF2UfZf82mGkujOKyNCVU/Iezoj3FYCwER/u7lfksOuXCRLyKoKbkD7fY3sdcJK7LwiPfRrhDa7pzOwWgqHExphZM3CJu/867MrT1acyClzv7i/lck59iZgFiTvA63+Gg7YKa+j580WQ7ISjL4e/fScouzXts9L6ZvhuDhfkxu8HiXa4+SRY+MjW27/RBMPGDkrIIrLtzCwCrA8nUfoHsHORQwrUbDWapIjIkJHraDOPAMeH9ecBy83sX+6etZ+iuz8LTM+y/V891juBrS7BuvupWY6xVZ/KQde2Pq+HLwvu8O+fBMtPXJf7fhcuh8smQVUdnPciXDYh6AffW5I/sxmqhw9OvCIyYOFsqt8Cbi92LJuN2wsaM45LICJS8XLtNtPo7uvN7AsEQ0ReYmbP5zOwkvKXi+C9Xyl2FIW1aQ3EaqF11ZY+7JkMHw97HAdPzoa9ToTjfgw1aV1mLupjeP3T/gg7Hz7QiEUkP/5qZt8AbgM2dhW6++qiRBNVf3cRGdpyTd5jZjYeOAm4II/xlKZJBxc7gsJ56e6ga8xdZ2Wv94WHYPz+EE37FTrmB30f/8LlcNlE2Ock+Ni1A4tVRArh5PDxS2llTql0oRERGWJyTd4vJehX/i93f8rMdgZez19YpWHmsO9yWcuF8L5+jWJWXt5+HqLxoO/5A1sNBtTdjMth1xkwaqdtf75YNVykWWtFyoW7D+APXkREBluuN6zeAdyRtr4Q+ES+gioVrbGw60eqM3vFctO6Gh793+BDyS8O67v+uXNhzLT8xyUiJSccSGAr7n5DoWMREZHcb1idCPwU6Brv71Hgq+7enK/ASkEkFg5ln+wobiCD7d6vwiv3wGPXZK7z/x6F7ffRLKYi8q605RqCYX+fBpS8i4gUQa7dZn4D3Ax8Klz/bFhW0TPnxOLVwUIyUdxABmrpMzD7cNjzY3D8T4PEvTfn/Au23ztvYbg7J/3iMc54704cvc/4vD2PiAwed/9y+rqZjQBuLVI4IiJDXiTHemPd/Tfungh/fgtU/EDcNTU1wcKfy/Qe3dbVwbCMsw8P1l/+A1w+qXudHQ6Ai9cEs6HmMXHv8tSiNXzxpqf7rigipWojoH7wIiJFkuuV91Vm9lnglnD9VGBVfkIqHbU1tcHCxjK8wXJlE1xzUObt0z4Cp94KkVw/v4nIUGRm9xKMLgPBBZ89KaVx30VEhphck/czCPq8/4igEf83vcyEWmmGV5dpYtuxcevE/T/+EJQvehSOzmVy3MHn3ncdESk5P0xbTgBv5nq/k5nNAK4mmAH7V+5+eYZ6nwDmAO9y97kDjFdEpKLlOtrMmwQzrG5mZucBP85HUKXi+dXRYofQPyvmw7Xv3rr8krVbbjzd49jCxiQi5e4t4G13bwMws1ozm+Lui7LtZGZR4FqCe6OagafM7B53f7lHveHAV4En8hG8iEilGcil5Qoe/Dzwt9fXFDuE3LSuhlmNvSfus9ZpxBgRGYg7gFTaepK0oYOzeDfQ5O4L3b2D4CbXE3qp9x3gCqBtoIGKiAwFuXab6U3FZ4Tv33UcvFnsKDJwh1QSvjO69+3/sxSq6gsbUx/Ua0akLMXC5BsAd+8ws6oc9psALE5bbwa6TVdtZgcCk9z9/8zsm4MSrYhIhRtI8l7xudhXjpgG1xc7il78/Up4+Htblx94Ohz/k8LHIyKVbIWZHe/u9wCY2QnAyoEe1MwiwFXkcP+UmZ0NnA0wefLkgT61iEhZy5q8m9kGek/SDajNS0QlpDoWZWFqe2p3PJCSGJX8j+fCMzduXb79vnDmnyFe8W+JiBTeOcBNZtY1q1sz0Ousqz0sAdLHpp0YlnUZDuwNPGJB177tgXvCDwrdblp199nAbIDp06dX/IUjEZFssibv7j68UIGUoqpYhBQRUslkcQNpXQ1X9jKs8swlEKuGaLzwMW0D13AzImXH3RcAh5jZsHC9JcddnwKmmdlOBEn7KcCn0467DhjTtW5mjwDf0GgzIiLZlelYiIVRHYuQJIKnipS8u8PsD26duO97SjCCTPWwskncRaQ8mdn3zWyEu7e4e4uZjTSz7/a1n7sngHOBB4FXgNvd/SUzu9TMjs++t4iIZDKQPu8VLx6NsFukGd7JaUjjwfXyH+H2Ht9MH/KfMOOywsciIkPZ0e7+P10r7r7GzI4BLuxrR3e/H7i/R9nFGeoePsA4RUSGBCXvWVTFivTFxOzDYekzW9YbJsLXXypOLINInWZEylLUzKrdvR2Ccd6B6iLHJCIyZCl5z6ImXuDkPZWCS0d2LyvBIR9FZEi5CXjIzH5DMFjB54DfFTUiEZEhTMl7FjWxAs6w2rYeLk8bmGGP4+GkGypqgiXdrypSftz9CjN7DjiS4Au0B4EdixuViMjQpRtWs4hECpQ4t67unri/9zw4+caKStxFpKwtI0jcPwV8iOAGVBERKQJdee/D3+Pv49DEU+QyneA2ce8+mszZf4cd9s/Xs4mI5MTMdgVODX9WArcB5u4fLGpgIiJDnJL3Pnyg85/5O7g7fHvElvULlkG8Jn/PV2SuW1ZFysmrwKPAse7eBGBmXytuSCIiom4zxZSeuH/9lYpO3EWk7JwIvA08bGa/NLMjCG5YFRGRIlLy3odmH9N3pW0xq3HL8rfegIYd8vM8IiLbwN3/4O6nALsDDwPnAduZ2XVm9uHiRiciMnQpee/DRFsZLKRSg3fQ9MT9uKuhbtTgHbuEabQZkfLj7hvd/WZ3Pw6YCDwD/HeRwxIRGbKUvPfhqVHHBgvJjoEfbOPK7on7R74PB31u4McVESkAd1/j7rPd/YhixyIiMlTphtU+rKvfGVYDibaB9Um/+WR47YEt6x+8AA790oDjExEREZGhQ8l7H2JVQcLe2dFGvHYbDtC6uvtQkACfuROmHTnw4ERERERkSFHy3ofxba8D0La6mXjj9v3b+fnb4a6zupddvAYi6q0kIiIiIv2nLLIPq8e9B4DWTe3923HBw90T9zP/ArPWKXEXkSHDzGaY2XwzazKz83vZ/nUze9nMnjezh8xsx2LEKSJSTpRJ9iFSPxaA5LKXct/p7efgxo9tWb94DUx69yBHVn402ozI0GFmUeBa4GhgT+BUM9uzR7VngOnuvi8wB7iysFGKiJQfJe99iA/fDoDE2qW57bDsJfjF+7es62q7iAxN7waa3H2hu3cAtwInpFdw94fdvTVcfZxgKEoREclCWWUfxu84FYBVrYm+K8/9DVz3ni3rs9blKary5OjSu8gQMgFYnLbeHJZlcibwp942mNnZZjbXzOauWLFiEEMUESk/St77sN2YoNvM1EW3ZK845wy477wt60rcRURyYmafBaYDP+htezi2/HR3nz527NjCBiciUmI02kwfotHg883C+FT2663CprVwRY97rC5alfe4RERK3BJgUtr6xLCsGzM7ErgA+IC793NkABGRoUfJew4W1uyJt23YesO1h8CKV7asTz8Djv1R4QIrM7phVWRIeQqYZmY7ESTtpwCfTq9gZgcAvwBmuPvywocoIlJ+lLznoHXkHuz/9p0wqxG+uQB+sMvWlb72MjRm684pIjJ0uHvCzM4FHgSiwPXu/pKZXQrMdfd7CLrJDAPuMDOAt9z9+KIFLSJSBpS856DqPefAnXcGK70l7urfLiKyFXe/H7i/R9nFacuaalpEpJ90w2oOpu39Lg5uu2ar8u8c9G9+dvg87nt+Kf9esJJ5b67hheZ1PPTKMprXtLJ07SbWt3XS1pnE3UmlnNaOBKmU43nuQ+Lu3Pf8UjZ1JAHY2L5ltJy3121i5l0v0NKeYGN7gmQqt1ialm/g2cVrN693JlO91kulnPZEcvP6ig1BN1b1mhEREREZmCF15d3Mdia4MarR3T/Zj/14/LLPcvvcw7n3+aW8s66NxWtaafvXopyfOxYxUu70zJOrohH2ntDAyLoqIhGjsTZOPBqhOhYhFjGiUQOH4TUxEimnrTPFpFG1rNzQwcaOBLuMrWfVxg5G1FaxYkM7u48fzrDqGI8tWMU1Dzdx+G5jmbHX9px/1wv86OT9+Nj+E7jlibe45cm32HdiIzPveoG9dmigvirGmOFVvGvKKEbUxbnmb03sN3EEV528/+ZYj7zqHwAsuvyjNK9p5X1XPMyPT96fjx0wge/f/woHTh7JjL2357/vfJ475jXzxmXH8M+mlfzHr5/kpi8czH6TRuT8eomIiIjI1vKavJvZImADkAQS7j59G49zPXAssNzd9+6xbQZwNUGfyl+5++WZjuPuC4EzzWzONsTASe+axEnv2jJ4QtPyFh5buIqoGfXVUeYuWgPAU4tWk0g5h+48mk2dSToSKRpqYzTUxPnZIwu6HbcjmSIWibB0XRtvrGxhRG0ViVSKjkSKjmSKVCqo05t41OhMZr+e/cj8FTwyPxgX+Wu3Pcd/z3lh8/Fm3vUCAC8tXb+5/v0vvLN5ecGKjUQjRn1191+Tq/48n8cXrgbgoj++SFtnktn/WAjA7854N3fMawbggRff4fa5wTDPNz72JuMaqjcfY2VLOzXxKPGoURWNEPZ3FREREZEsLJ/dN8Lkfbq7r8ywfTtgk7tvSCub6u5NPeq9H2gBbkhP3sPpt18DjiKYAOQp4FSCRP6yHk93RtdoBmY2p68r79OnT/e5c+fmdJ75lko5HckUETMcZ2VLB6Prq4iYsXhNK421cVraEgyrifHmqlYSyRSbOpMMr4mzZmMH0ahRE4vy7OK1rN3UQSrlLF69iYbaGItWtTK6vooXlqxj3aZOOpMp2jq7f1hoqImRTDkbO7Z0hYkYpBzMBj6KTMQgFo0Qj1jwGH7zEI9axqQ+5Y6hyA9tAAAQiElEQVQ7Yb0IZtCeSBGPRohGoDoWJRoxauLR4BuMiNEZvobJlBONGLGIEY9GiEWDx3jUiEW2rHdtj0eNMcOq2b6xhpF1VQyrCT6INdbGqYqp55kUl5nN29YLI+WolNpmEZFs8tU+F7vbzAeAc8zsGHdvN7OzgBOBo9Mrufs/zGxKL/tvnn4bwMxuBU5w98sIrtT3m5kdBxw3derUbdk9LyIRoyYS3bw+YUTt5uVdxg4DYMyw6m6PvTl0l9EDjiXoz56iJh5cLV/f1smydW1s11DD+k2dLFm7ieE1MWriUZqWt1AbjzJpVB2PL1yFO7Qngg8VG9o6aU+k6Ay/YehMOp3JFIlkio5k0Gc+0ce3CsmUk0w5iVSKlENNPEJHIkUyjDGZctZt6iSVCo5dFYuQcidqRtKdRNdzppzORIrOlJNIpkgkgw9LifD42dRVRWmoidNQG6OxNh4uxxlVX8XZ79+ZcQ01A37NRURERLrkO3l34M9m5sAv3H12t43ud4RjAN9mZncAZxBcRc9Vb9NvH5ypspmNBr4HHGBmM8Mkv3vA7vcC906fPv2sfsQxZEQiRm3Vlg8SDTVBwgrQWBtn0qi6zdu6PlgA7DSmvnBBDqJUyulMpVi+vp3lG9pYs7GTjR0J1rclWLOxg/WbOlnf1sn6TYngg8yGNl5+ez1vr2vj1/98g2/N2I2P7jOeHUeX5/mLiIhIacl38v4+d18Sdo/5i5m96u7/SK/g7leGV8yvA3Zx95Z8BePuq4Bz8nV8qTyRiFEdCb49SP9g0pe7n2nmgrtf5MoH5nPlA/P50O7b8dUjprH3hEY2dSapr4qqn7+IiIj0W16Td3dfEj4uN7O7Cbq5dEvezewwYG/gbuAS4Nx+PEVO02+LFNrHD5jIxw+YyFurWvnjs0v4+d8XcMKry6mKRUgkU0wcWUdtPMrk0XWbRxha29rB2OHV1MSj7DZuOBNG1lIbj1JbFaUjkWJkfRXxqAVl8aBPf6YPAJs6krz7e3/l0o/txccPmFjgsxcREZF8yVvybmb1QMTdN4TLHwYu7VHnAGA2Qf/0N4CbzOy77n5hjk/T5/TbIsU0eXQdXz5iGv9x6I48PH85r7y9gdeWbWBNaycLl7ewbEMba1s7t/n4w6tjVMejVEWDm31jUSMeiTB/WXAP+Ndue46v3fZct30O3mkUh+w8mt23H07KYVxDNas3dmBmVMUi/PyRBVx83J786cV3+Og+45k8qo41rR3UVUUZUVdFW2eSV9/ZwP6TRtDWmeT1ZS3sM7FxQK+TiIiI5CafV97HAXeHVwZjwM3u/kCPOnXASe6+AMDMTgM+1/NAZnYLcDgwxsyagUvc/deZpt/O0/mIbLMRdVXh1fittyXDG2rbw1F+Xly6jupYcPNtWyLJmo2dtLQniEWNF5esZ1xDNbc+uZh9JjYyvrGm282+XTfdptx5fXnvPdCeeGM1T7yxOmu8R1/9KAA/eej1buXVsQjtia2HLu0afagqjLun7RtqeGd9GxB8WGhpSzCuoYbhNTGa12ziiD22I2LGXU8vIRoxdtt+OI21capjERpq4xjByEaJlLNgeQvV8SgHTh7J+MYazGDRylbGDq/miTdW8b6pY+gIv92Yt2g1DbVxpm43jE0dSW6bu5hPHTSJHUfXsXDlRh59bQWH7TqWvXZoIGLG02+uIeXOPhMaWbepk7qqGKPqq6iKRXhk/nJ2GTuMncbU8+bqVuqqoqRSzpjh1SxY3sLI+iqi4VwNmzqS7Dy2no5EiqVr26iKRdiuoZo3V7YyaVQtjbVxmpa3MLwmuLk5HjVa2hOsagk+JI0ZVk1HMkVVNMKGtgQNtTFaw9Ge0oduXbgib70MRUSkROV1qMhypuHIpFK4O2ZGeyLJ2tbgg8Da1g4iFiSMrR1J6qtirG/rZN2mTua/s4GG2jg/eeh1Tpo+kZb2BJ1JZ3xjDfFohJUt7fzx2aWYwf6TRrBgeQuH7Dyad9a30VAT559NK6mvinYbWnR0fRWrNnYAwYhI69s6qYpG6EimqI5FqKuKsmx9e7e4G2vjrNu07d9KlLJc5miAYHK2DW0JhlfH2NSZJBIxxjfW0NKWIOnO2tZO3rziWA0VKSJSgip1qEgRybOufvHVsSjjGqKMy3G/rx+1a8ZtV5/Sy1cIeZZKOWaEw4MGw3l2JlK0JVKkUs6w6hirNrZTWxWjJhZh+Yb24Gp90qmripJMOVWxCC8tXc+o+ioaa+OMHlZF07IWVm3sYFNnkl3GDmP1xuDqtwHtyRTrNwXDmo4ZVkV1LErzmlY2dSSpiUepjkfoTARJ+IqWdp54YzVH7rEd0YjxQvM6GuuC+QAWr26loSbO4jWtbN9QSzxmLFyxkVjEmDy6jmTSWbRqI2taO1nT2sHBO43i4VdXMGVMHW+uamWP8Q3UVQX3OSRTTkN4df9vry4v+PsgIiLFpeRdRMpCJBJ8CIlFjVgUauLRreqMrK/avDw6w5wHPYft3G744I3F/6UPpq1kHLR2cNmswjzPtuhrBmwzqwZuAA4CVgEnu/uiQscpIlJOND2kiIgMunAG7GsJJt3bEzjVzPbsUe1MYI27TwV+BFxR2ChFRMqPkncREcmHzTNgu3sHcCtwQo86JwC/C5fnAEeYJkAQEclKybuIiORDbzNgT8hUx90TwDpgdEGiExEpU+rznsG8efNazGx+seMYBGOAlcUOYoAq4RxA51FqKuU8dit2APlmZmcDZ4er7Wb2YjHjGSSV8vun8ygtOo/Skpf2Wcl7ZvMrYfg1M5tb7udRCecAOo9SU0nnUewYMshlBuyuOs1mFgMaCW5c7cbdZxNM6FdR75vOo3ToPEpLJZ1HPo6rbjMiIpIPm2fANrMqghmw7+lR5x7g9HD5k8DfXJOPiIhkpSvvIiIy6DLNgG1mlwJz3f0e4NfAjWbWBKwmSPBFRCQLJe+ZzS52AIOkEs6jEs4BdB6lRueRZ+5+P3B/j7KL05bbgE/187Ale779pPMoLTqP0qLzyML0DaWIiIiISHlQn3cRERERkTKh5L0HM5thZvPNrMnMzi92PD2Z2SQze9jMXjazl8zsq2H5KDP7i5m9Hj6ODMvNzH4Sns/zZnZg2rFOD+u/bmanZ3rOPJ5L1MyeMbP7wvWdzOyJMNbbwpvcMLPqcL0p3D4l7Rgzw/L5ZvaRQp9DGMMIM5tjZq+a2Stmdmi5vR9m9rXw9+lFM7vFzGrK5f0ws+vNbHn68IGD+fqb2UFm9kK4z0/MBn8SoQzn8IPwd+p5M7vbzEakbev1dc7UfmV6L8tNpvMrBVZBbXMYQ9m3z1YBbXP4/GXZPmdo18qqbc5yHsVtn91dP+EPwU1VC4CdgSrgOWDPYsfVI8bxwIHh8nDgNYKpx68Ezg/LzweuCJePAf4EGHAI8ERYPgpYGD6ODJdHFvhcvg7cDNwXrt8OnBIu/xz4Yrj8n8DPw+VTgNvC5T3D96ga2Cl876JFeE9+B3whXK4CRpTT+0EwUc4bQG3a+/C5cnk/gPcDBwIvppUN2usPPBnWtXDfowt0Dh8GYuHyFWnn0OvrTJb2K9N7WU4/2c6vFH6ooLY5jKPs22fKvG0On79s22cqoG3Och5FbZ8L9kdUDj/AocCDaeszgZnFjquPmP8IHAXMB8aHZeMJxqkH+AVwalr9+eH2U4FfpJV3q1eAuCcCDwEfAu4L//hWpv0xbH4vCEarODRcjoX1rOf7k16vgOfRSNCwWo/ysnk/2DLL5ajw9b0P+Eg5vR/AlB4N66C8/uG2V9PKu9XL5zn02PZx4KZwudfXmQztV7a/rXL6yXR+xY4rS7xl2TaHz1n27TMV0DaHz1fW7XPPdm2wXn8K2Db3dh49thW8fVa3me5ymc67ZIRfhx0APAGMc/e3w03vAOPC5UznVOxz/THwLSAVro8G1nowRXrPeDJNoV7sc4Dgk/UK4DfhV8y/MrN6yuj9cPclwA+Bt4C3CV7feZTn+9FlsF7/CeFyz/JCO4PgyhL0/xyy/W2Vk1L6/cqqzNtmqIz2uezbZqjI9rnS2mYoQvus5L1Mmdkw4E7gPHdfn77Ng49vXpTAcmBmxwLL3X1esWMZBDGCr9Ouc/cDgI0EXwVuVgbvx0jgBIJ/djsA9cCMogY1iEr99e+LmV0AJICbih2L9K2c22aoqPa57NtmqOz2uRxe/74Uq31W8t5dLtN5F52ZxQn+Odzk7neFxcvMbHy4fTywPCzPdE7FPNf3Aseb2SLgVoKvZq8GRlgwRXrPeDbHat2nUC+F96sZaHb3J8L1OQT/MMrp/TgSeMPdV7h7J3AXwXtUju9Hl8F6/ZeEyz3LC8LMPgccC3wm/EcH/T+HVWR+L8tJKf1+9aoC2maonPa5EtpmqLz2uSLaZihu+6zkvbtcpvMuqvBu6l8Dr7j7VWmb0qcZP52gv2VX+WnhndyHAOvCr6weBD5sZiPDT/YfDsvyzt1nuvtEd59C8Br/zd0/AzxMMEV6b+fQdW7pU6jfA5wS3l2/EzCN4AaWgnH3d4DFZrZbWHQE8DJl9H4QfB17iJnVhb9fXedQdu9HmkF5/cNt683skPC1OS3tWHllZjMIui4c7+6taZsyvc69tl/he5PpvSwnJd0+V0LbDJXTPldI2wyV1z6XfdsMJdA+56tzf7n+ENzx/BrBXcEXFDueXuJ7H8HXTM8Dz4Y/xxD0m3oIeB34KzAqrG/AteH5vABMTzvWGUBT+PP5Ip3P4WwZzWDn8Je8CbgDqA7La8L1pnD7zmn7XxCe23zydKd5DuewPzA3fE/+QHBHfFm9H8C3gVeBF4EbCe6UL4v3A7iFoC9oJ8HVtjMH8/UHpoevywLgGnrcAJfHc2gi6CPZ9Xf+875eZzK0X5ney3L7yXR+pfBDhbXNYRyHU8btMxXQNofPX5btc4Z2raza5iznUdT2WTOsioiIiIiUCXWbEREREREpE0reRURERETKhJJ3EREREZEyoeRdRERERKRMKHkXERERESkTSt5FBoGZXWBmL5nZ82b2rJkdbGbnmVldsWMTERmq1DZLJdJQkSIDZGaHAlcBh7t7u5mNAaqAfxOMVbuyqAGKiAxBapulUunKu8jAjQdWuns7QPgP4ZPADsDDZvYwgJl92MweM7OnzewOMxsWli8ysyvN7AUze9LMphbrREREKojaZqlISt5FBu7PwCQze83MfmZmH3D3nwBLgQ+6+wfDKz4XAke6+4EEs/59Pe0Y69x9H4JZ4n5c6BMQEalAapulIsWKHYBIuXP3FjM7CDgM+CBwm5md36PaIcCewL/MDIKvbh9L235L2uOP8huxiEjlU9sslUrJu8ggcPck8AjwiJm9AJzeo4oBf3H3UzMdIsOyiIhsI7XNUonUbUZkgMxsNzOblla0P/AmsAEYHpY9Dry3q8+kmdWb2a5p+5yc9ph+1UdERLaB2mapVLryLjJww4CfmtkIIAE0AWcDpwIPmNnSsG/l54BbzKw63O9C4LVweaSZPQ+0h/uJiMjAqG2WiqShIkWKzMwWoWHLRERKitpmKVXqNiMiIiIiUiZ05V1EREREpEzoyruIiIiISJlQ8i4iIiIiUiaUvIuIiIiIlAkl7yIiIiIiZULJu4iIiIhImVDyLiIiIiJSJv4/c9WZZoTUvLYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / age / fare...\n",
      "  train: loss = 0.42176324  accuracy = 0.808    test: loss = 0.59276152  accuracy = 0.7786\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / age / embarked...\n",
      "  train: loss = 0.48084912  accuracy = 0.7889    test: loss = 0.51393181  accuracy = 0.8168\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / age / embarked / fare...\n",
      "  train: loss = 0.38908142  accuracy = 0.8348    test: loss = 0.75216547  accuracy = 0.7328\n"
     ]
    },
    {
     "data": {
      "image/png": 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YXVcdYsBE4MGo4mlKJKN6axGRyGWaYF9I0Af7pwQN+HPA+TmKqXuUYIuIAPxn2nICWJHJfTJmNhW4EYgDd7j7tZ1sdzbwEHCsu8/b23ETSf3CKCKlK9NRRFYAH0svM7PLgBtyEVT3qBEXEQHeAt529yYAM6s2s7HuvryzHcwsDtxCcD/NKmCumc1y98UdtusPfA14MdNghtdUdb8GIiJFItM+2HvyjaxFAZhZXzO728x+YWafyXhHV4ItIkIwlGoqbT1J2vCqnZgE1Lv7MndvIbgpctoetvshcB3QlGkwZTH9sigipWtfEuy9tp5mdqeZrTWzhR3Kp5rZEjOrN7PLw+KzgIfc/WI6XC3vmhJsERGgLEySAQiXK/ayz/7AyrT1VWHZTmZ2NDDa3f8nW4GKiBS7fUmwM8ls7wKmphek/SR5GsFNONPNbCIwil0NfeZ3x+gKtogIwDoz23lxwsymAev35YBmFgN+AvxLhtvPMLN5ZjZv3bp1+/LWIiIFrcsE28wazGzrHh4NwMi9HdzdnwY2diju7CfJVQRJdpdxpTfgwZukOttURKSUfBH4rpm9ZWZvAd8GvrCXfVYDo9PWR4VlbfoDRwBPmtly4DhglpnV7elg7j7T3evcvW7o0DwYyVVEJCJd3uTo7v1z8J57+klyMnAT8DMz+yjwcBcxzQRmAtSNjLu6iIiIgLu/ARxnZv3C9W0Z7DYXmGBm4wgS63OBT6cdcwswpG3dzJ4EvpnJKCIiIqVsX7qIZJW7b3f3C9z9S+5+b+b76Qq2iIiZ/cjMat19m7tvM7OBZvZvXe3j7gngEuBR4FXgQXdfZGbXpHc3ERGR7sl0HOxs2ttPkt2jPtgiIgCnuft321bcfZOZnQ5c2dVO7j4bmN2h7KpOtp2ShThFRIpeFFewd/4kaWYVBD9JzurpwVwJtogIQNzMKttWzKwaqOxiexERyZGcJthmdj/wPHCIma0ys4s6+0mym8c908xmghJsEZHQvcDjZnaRmX0eeAy4O+KYRERKUk67iLj79E7Kd/tJspvHfRh4uG5k/GJPZT6in4hIsXL368xsAXAKwd3fjwIHRBuViEhpypubHHtK169FRHZ6l6BZPAc4ieBXQhER6WVR3OSYVZ5Sii0ipcvMDgamh4/1wG8Ac/cTIw1MRKSEFeQV7HZ9sNEwfSJS0l4juFp9hrt/0N1vpjuz4YqISNYV5BXs9n2wdQVbJHKpJLRsh3gFJBoh0QKNG6G5AVq2wYD94a0XoLI/NG+F1kZY/zq07IBVc2HQOKj/W9S1KFRnEYzG9ISZ/YVgdlyLNiQRkdJWkAl2e0qwRfYqlQJPgqegcTPcfkKQ7NYeABuXwaY3o41v4xvRvn8Bc/c/An80s77ANOAyYD8zuw34g7v/NdIARURKUMEn2J5SFxEpAqkUJJoglQiS3Y1vwtK/wssZT2rafdvehQ31uTu+9Cp33w7cB9xnZgMJbnT8NqAEW0SklxV8gq2ZHKVXtDYF3R+2rISKvvDW8/DyfbCkx6NNiuSMu28CZoYPERHpZQWZYJvZmcCZx4yIQeu2qMORQrFyDvzjHnjpV1FHUnj6DYfB42HwgXDI6TD0UOg7BMr7gMXA1OV3r36gz0hEpFQUZIKdfpNj2Qs/g2k3Rh2SZEtrY3DDXCweXDHethbefDq4Ia7+MXh7QdQR5pdYORw4BUZPhuFHwJjjoHpg1FGJiIiUtIJMsNOVv3SXEux81rIDdmyAdxfB/Z+KOpreVVULR5wNh50Jw4+EqhqIF/w/OREREdkL/W8ve+YOK1+ETcvhD1+IOprcO/kqOPzjMOjAoO7q8iAlwsymAjcCceAOd7+2w+vfAD4PJIB1wIXuvqLXAxURKSBKsItJshU2vwUv/Rr+/pOoo4nOlO/Ch/4VYj2cR0nJtZQIM4sDtwCnAquAuWY2y90Xp232ElDn7jvM7EvA9UCJ/RwlItI9SrBzrW2Uk+3r4aELYPkz0caTj8aeAFMuD/oRx8ujjkaklEwC6t19GYCZPUAwlvbOBNvdn0jb/gXgs70aoYhIASrIBLvdKCIAW9fAgJG9G8QDn4HXHund9ywUx18Ck78YzN7X06vIItIb9gdWpq2vAiZ3sf1FwJ87e9HMZgAzAMaMGZON+EREClJBJtjpo4gA8JPD4OotuXmzZCvccQq8/XJujp8PRk2CA94fjNix4L6g7OSroO4iqK6NNjYRyQtm9lmgDvhwZ9u4+86xt+vq6jRJgYiUrIJMsHNq7atw63FRR9F9p10PdRfuexeLj9+WnXhEpBCsBkanrY8Ky9oxs1OAK4APu3tzL8UmIlKwiifB/st3YOp/9Gxfd7j1eFj3anZjysT7PgsnfQ/6DWt/c51GshCR3JsLTDCzcQSJ9bnAp9M3MLP3AbcDU919be+HKCJSeIonwX7h1uDxjVcz64+97Cm452PZee/T/xOatgQTokz+Aux/TJAwQ8+TZCXXIpJj7p4ws0uARwmG6bvT3ReZ2TXAPHefBfwY6Af81oJ26S13z1LjKSJSnMy9cLvJ1Y4Y45u/sIe+199evufZ7FIpuPMjsGpO99/s2M8H3TBi8e7vKyIlz8zmu3td1HH0lrq6Op83b17UYYiIdClXbXNBX8FupHLPL1w3Nnj+8gsweAL8cHD3DjzpC3D69fsUm4iIiIiUpoJMsNuG6Rs/fjyTmm5hTtVX9rxhd25WPOsOOPKcrMQnIiIiIqWrIBPsncP01dVdPOfazzL28lqWV30m8wNU9IPp9wcTnKivs4iIiIhkUUEm2B0tv/YMYAtfvnc+Cxcu4OnKrwMwrunXOO0nOpl96QlMHDkggihFREREpBQURYLd5tbPHAMcA1zIc/Xr8Tte3G2b02/qfKry2j7lfP/MiUw9fATVFbqZUURERES6r6gS7HTvHz+E5dd+FAB354yb/86iNVu73Gfzjla+/psFfJ0FGb3HUaNquGbaERw2YgAVZZoSXERERESKOMFOZ2b8z6Un7Fbu7qxraObCu+eycHXXyfeeLFi1hWm3PNvt/T49eQwDqso5bER/3n/QEAb1rSAeU19wERERkWJQEgl2Z8yM/QZU8chXd0++O9PUmuTaP7/GXc8t7/H73vfiWz3eNxOXnTKBvy56l/eOqeWoUTUMqCqnT2UZBw/rRyLpDKgup7o8TlnMiMUMd8d0s6eIiIhIVhT0RDP5PpFBKuU0J1Js2N7MrAVr+N38VbyxbjuVZTGaE6mow8uqc48dzRvrtjF3+aZu7ztsQCXvbm1mwn79iJnxnlE1zP7n20wY1p/DhvenoSnBxJEDmHLIUJpak1TE49RUl9PYmmRIvwr6VJSRdKdPeRwHYob+YJC8o4lmRETyT67aZiXYBcjdaUmm2N6c5LW3t1K/bhvzlm9iR0uCxWu2cvxBQ3hs8TuMG9KXETXVLF3bwBvrtu/c/+RD96MsbmzY1kLSnZfe2hxhbXKnoixG3IzG1uRur00cMYBYDBav2UoqT/4JnHnUSEYPrKYsZpTFY6xraOZXL6yIOixJc+jw/rz2TsPO9eryOCl3qsrjNLYmmTxuEPVrtzG0fyXL1m1nv/6VDO1fyeEja/j+xw5Xgi0ikmeUYKdJm2jm4qVLl0YdTslrO4eSqaCrydbGVhqaEpjBxu0tzHlzI4P6VvC/S9by96Xred+YWt7Z0sT+tdU8/tpaDh85oN0NqP0ry2hoTuz2PhNHDMCBV9/eyiHD+rPk3YZ2r08eN4gX39wIwJenHETSnVTK+fPCd1i1qbHdtqccNgx3Z2tTa4+uuudKWcxI5EvGL1m14rozlGCLiOQZTZWeJn2imahjkV3dMcriwfPAvhUM7FsBwOhBfThqdC0AZx8zKpL4rvjoxEjeN1Ntf6C0fY7uTsohkUqRTDnl8RiJpGMGrcmga1F5PEZDU4KymNGUSGIYDU2t9KksozxmbNjeEuyXSpFIOg1NCSrKYmxrTtDYkgScptYUrckUL63czLFjB/LG2u2s3txIIuX0qyyjsSXBO1ubdl7hd3cSYTxz3tzI/rXVQPC9N7emeLehicF9K6gsi5NMOY2tScrCWCriMVqSxdUtSkREpDMFmWCLFJOO/cXNjLhBPLZrLPbycLGqfFdZ+jLA8Jqqncv7DagiU+fUje5OuNJDdl3UEeyZmU0FbgTiwB3ufm2H1yuBewgmGdgAfMrdl/d2nCIihUSDN4uIlCgziwO3AKcBE4HpZtbxJ5+LgE3uPh74KZCnfyqIiOQPJdgiIqVrElDv7svcvQV4AJjWYZtpwN3h8kPAyaZhekREuqQEW0SkdO0PrExbXxWW7XEbd08AW4DBvRKdiEiBKug+2PPnz99mZkuijqMXDQHWRx1EL1J9i1up1feQqAPINTObAcwIV5vNbGGU8fSiUjuXVd/iVmr1zUnbXNAJNrCklIa9MrN5qm/xUn2Lm5nl45h1q4H0u1xHhWV72maVmZUBNQQ3O+7G3WcCM6G0vt9SqiuovsWuFOubi+Oqi4iISOmaC0wws3FmVgGcC8zqsM0s4Lxw+RPA/3ohTqAgItKLCv0KtoiI9JC7J8zsEuBRgmH67nT3RWZ2DTDP3WcB/w38yszqgY0ESbiIiHSh0BPsmVEH0MtU3+Km+ha3vKyvu88GZncouyptuQk4pweHzsv65kgp1RVU32Kn+mZBQU6VLiIiIiKSr9QHW0REREQkiwo2wTazqWa2xMzqzezyqOPJlJndaWZr04evMrNBZvaYmS0NnweG5WZmN4V1fMXMjk7b57xw+6Vmdl5a+TFm9s9wn5uinhDCzEab2RNmttjMFpnZ18LyoqyzmVWZ2RwzWxDW9wdh+TgzezGM8TfhDWWYWWW4Xh++PjbtWN8Jy5eY2UfSyvPq3DezuJm9ZGaPhOtFW1cAM1senm8vt919Xqznc0/k43eWKSuh9tnUNhd92wyl1T5bvrXN7l5wD4Kbcd4ADgQqgAXAxKjjyjD2DwFHAwvTyq4HLg+XLweuC5dPB/4MGHAc8GJYPghYFj4PDJcHhq/NCbe1cN/TIq7vCODocLk/8DrBlMxFWecwhn7hcjnwYhjbg8C5YfnPgS+Fy18Gfh4unwv8JlyeGJ7XlcC48HyP5+O5D3wDuA94JFwv2rqG8S4HhnQoK8rzuQefTV5+Z92Iv2TaZ9Q2F33bHMZbMu0zedY2R/rF78OHeDzwaNr6d4DvRB1XN+IfS/sGfAkwIlweQTC+N8DtwPSO2wHTgdvTym8Py0YAr6WVt9suHx7An4BTS6HOQB/gH8BkgkH7y8LynecvwegNx4fLZeF21vGcbtsu3859gnGTHwdOAh4JYy/KuqbFsZzdG/GiP58z/Gzy8jvrZh3GUoLtM2qbi669osTaZ/KsbS7ULiKZTO9bSIa5+9vh8jvAsHC5s3p2Vb5qD+V5IfzJ6X0EVw6Kts7hT3IvA2uBxwj+yt/swTTT0D7Gzqah7u7nEJUbgG8BqXB9MMVb1zYO/NXM5lswcyEU8fncTfn6ne2Lov9u1TYXbXtVau1zXrXNhT5MX9FxdzczjzqObDOzfsDvgMvcfWt616Viq7O7J4H3mlkt8Afg0IhDygkzOwNY6+7zzWxK1PH0og+6+2oz2w94zMxeS3+x2M5n2aUYv1u1zcWpRNvnvGqbC/UKdibT+xaSd81sBED4vDYs76yeXZWP2kN5pMysnKABv9fdfx8WF3WdAdx9M/AEwU9ptRZMMw3tY9xZL2s/DXV3P4cofAD4mJktBx4g+BnyRoqzrju5++rweS3Bf9KTKIHzOUN5+Z3to6L9btU2F23bDCXYPudd2xxlf5l96GdTRtDxfBy7OtcfHnVc3Yh/LO37+P2Y9p3wrw+XP0r7TvhzwvJBwJsEHfAHhsuDwtc6dsI/PeK6GnAPcEOH8qKsMzAUqA2Xq4FngDOA39L+xpIvh8tfof2NJQ+Gy4fT/saSZQQ3leTluQ9MYddNNEVbV6Av0D9t+TlgarGezz34fPLuO+tBHcZSAu0zaptLom0OY55CkbfP5GHbHPkXvw8f5ukEdz2/AVwRdTzdiPt+4G2glaAPz0UE/ZweB5YCf0v7Mg24JazjP4G6tONcCNSHjwvSyuuAheE+PyOcTCjvYi/tAAACeUlEQVTC+n6QoF/UK8DL4eP0Yq0zcCTwUljfhcBVYfmB4T/O+rCBqwzLq8L1+vD1A9OOdUVYpyWk3a2cj+c+7Rvwoq1rWLcF4WNRW0zFej738DPKq++sm7GXTPuM2uaSaJvDuKZQ5O0zedg2ayZHEREREZEsKtQ+2CIiIiIieUkJtoiIiIhIFinBFhERERHJIiXYIiIiIiJZpARbRERERCSLlGBLyTCzK8xskZm9YmYvm9lkM7vMzPpEHZuISClT+yzFRsP0SUkws+OBnwBT3L3ZzIYQDI7/HMH4l+sjDVBEpESpfZZipCvYUipGAOvdvRkgbLA/AYwEnjCzJwDM7P+Y2fNm9g8z+62Z9QvLl5vZ9Wb2TzObY2bjo6qIiEiRUfssRUcJtpSKvwKjzex1M7vVzD7s7jcBa4AT3f3E8KrJlcAp7n40MA/4Rtoxtrj7ewhmcLqhtysgIlKk1D5L0SmLOgCR3uDu28zsGOAE4ETgN2Z2eYfNjgMmAs+aGQQ/UT6f9vr9ac8/zW3EIiKlQe2zFCMl2FIy3D0JPAk8aWb/BM7rsIkBj7n79M4O0cmyiIjsA7XPUmzURURKgpkdYmYT0oreC6wAGoD+YdkLwAfa+u+ZWV8zOzhtn0+lPadfORERkR5S+yzFSFewpVT0A242s1ogAdQDM4DpwF/MbE3Yz+984H4zqwz3uxJ4PVweaGavAM3hfiIisu/UPkvR0TB9Ihkws+VouCgRkbyj9lnykbqIiIiIiIhkka5gi4iIiIhkka5gi4iIiIhkkRJsEREREZEsUoItIiIiIpJFSrBFRERERLJICbaIiIiISBYpwRYRERERyaL/D8dYwBKMPbViAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / age / class...\n",
      "  train: loss = 0.39732784  accuracy = 0.8223    test: loss = 0.79784335  accuracy = 0.7328\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / age / family...\n",
      "  train: loss = 0.43731394  accuracy = 0.8185    test: loss = 0.95560177  accuracy = 0.8244\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / age / family / class...\n",
      "  train: loss = 0.36481893  accuracy = 0.8405    test: loss = 0.84083647  accuracy = 0.771\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting sex / age / family / class / embarked / fare...\n",
      "  train: loss = 0.22639416  accuracy = 0.9064    test: loss = 1.731664  accuracy = 0.7176\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x141.732 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete\n"
     ]
    }
   ],
   "source": [
    "models2 = train_neural_networks(make_model, train_data, test_data, [\n",
    "    InputFn('sex / embarked', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'embarked_S',\n",
    "        'embarked_C',\n",
    "        'embarked_Q',\n",
    "    ])),\n",
    "    InputFn('sex / class', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'pclass_1',\n",
    "        'pclass_2',\n",
    "        'pclass_3',\n",
    "    ])),\n",
    "    InputFn('sex / family', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'sibsp',\n",
    "        'parch',\n",
    "    ])),\n",
    "    InputFn('sex / embarked / class', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'embarked_S',\n",
    "        'embarked_C',\n",
    "        'embarked_Q',\n",
    "        'pclass_1',\n",
    "        'pclass_2',\n",
    "        'pclass_3',\n",
    "    ])),\n",
    "    InputFn('sex / embarked / class / family', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'embarked_S',\n",
    "        'embarked_C',\n",
    "        'embarked_Q',\n",
    "        'pclass_1',\n",
    "        'pclass_2',\n",
    "        'pclass_3',\n",
    "        'sibsp',\n",
    "        'parch',\n",
    "    ])),\n",
    "    InputFn('sex / age', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'age_known',\n",
    "        'age',\n",
    "    ])),\n",
    "    InputFn('sex / age / fare', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'age_known',\n",
    "        'age',\n",
    "        'fare_known',\n",
    "        'fare',\n",
    "    ])),\n",
    "    InputFn('sex / age / embarked', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'age_known',\n",
    "        'age',\n",
    "        'embarked_S',\n",
    "        'embarked_C',\n",
    "        'embarked_Q',\n",
    "    ])),\n",
    "    InputFn('sex / age / embarked / fare', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'age_known',\n",
    "        'age',\n",
    "        'embarked_S',\n",
    "        'embarked_C',\n",
    "        'embarked_Q',\n",
    "        'fare_known',\n",
    "        'fare',\n",
    "    ])),\n",
    "    InputFn('sex / age / class', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'age_known',\n",
    "        'age',\n",
    "        'pclass_1',\n",
    "        'pclass_2',\n",
    "        'pclass_3',\n",
    "    ])),\n",
    "    InputFn('sex / age / family', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'age_known',\n",
    "        'age',\n",
    "        'sibsp',\n",
    "        'parch',\n",
    "    ])),\n",
    "    InputFn('sex / age / family / class', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'age_known',\n",
    "        'age',\n",
    "        'sibsp',\n",
    "        'parch',\n",
    "        'pclass_1',\n",
    "        'pclass_2',\n",
    "        'pclass_3',\n",
    "    ])),\n",
    "    InputFn('sex / age / family / class / embarked / fare', lambda df: df.filter(items=[\n",
    "        'sex_male',\n",
    "        'sex_female',\n",
    "        'age_known',\n",
    "        'age',\n",
    "        'sibsp',\n",
    "        'parch',\n",
    "        'pclass_1',\n",
    "        'pclass_2',\n",
    "        'pclass_3',\n",
    "        'embarked_S',\n",
    "        'embarked_C',\n",
    "        'embarked_Q',\n",
    "        'fare_known',\n",
    "        'fare',\n",
    "    ])),\n",
    "], make_model_output)"
   ]
  }
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