07_dense_networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artificial Intelligence in Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dense Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pylab import plt, mpl\n",
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['savefig.dpi'] = 300\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "pd.set_option('precision', 4)\n",
    "np.set_printoptions(suppress=True, precision=4)\n",
    "os.environ['PYTHONHASHSEED'] = '0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://hilpisch.com/aiif_eikon_id_eur_usd.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol = 'EUR_USD'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw = pd.read_csv(url, index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HIGH</th>\n",
       "      <th>LOW</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>CLOSE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-01 00:00:00</th>\n",
       "      <td>1.0899</td>\n",
       "      <td>1.0897</td>\n",
       "      <td>1.0897</td>\n",
       "      <td>1.0899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01 00:01:00</th>\n",
       "      <td>1.0899</td>\n",
       "      <td>1.0896</td>\n",
       "      <td>1.0899</td>\n",
       "      <td>1.0898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01 00:02:00</th>\n",
       "      <td>1.0898</td>\n",
       "      <td>1.0896</td>\n",
       "      <td>1.0898</td>\n",
       "      <td>1.0896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01 00:03:00</th>\n",
       "      <td>1.0898</td>\n",
       "      <td>1.0896</td>\n",
       "      <td>1.0897</td>\n",
       "      <td>1.0898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01 00:04:00</th>\n",
       "      <td>1.0898</td>\n",
       "      <td>1.0896</td>\n",
       "      <td>1.0897</td>\n",
       "      <td>1.0898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       HIGH     LOW    OPEN   CLOSE\n",
       "Date                                               \n",
       "2019-10-01 00:00:00  1.0899  1.0897  1.0897  1.0899\n",
       "2019-10-01 00:01:00  1.0899  1.0896  1.0899  1.0898\n",
       "2019-10-01 00:02:00  1.0898  1.0896  1.0898  1.0896\n",
       "2019-10-01 00:03:00  1.0898  1.0896  1.0897  1.0898\n",
       "2019-10-01 00:04:00  1.0898  1.0896  1.0897  1.0898"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 96526 entries, 2019-10-01 00:00:00 to 2019-12-31 23:06:00\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   HIGH    96526 non-null  float64\n",
      " 1   LOW     96526 non-null  float64\n",
      " 2   OPEN    96526 non-null  float64\n",
      " 3   CLOSE   96526 non-null  float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 3.7 MB\n"
     ]
    }
   ],
   "source": [
    "raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(raw['CLOSE'].loc[:])\n",
    "data.columns = [symbol]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.resample('1h', label='right').last().ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2208 entries, 2019-10-01 01:00:00 to 2020-01-01 00:00:00\n",
      "Freq: H\n",
      "Data columns (total 1 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   EUR_USD  2208 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 34.5 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_lags(data, symbol, lags, window=20):\n",
    "    cols = []\n",
    "    df = data.copy()\n",
    "    df.dropna(inplace=True)\n",
    "    df['r'] = np.log(df / df.shift())\n",
    "    df['sma'] = df[symbol].rolling(window).mean()\n",
    "    df['min'] = df[symbol].rolling(window).min()\n",
    "    df['max'] = df[symbol].rolling(window).max()\n",
    "    df['mom'] = df['r'].rolling(window).mean()\n",
    "    df['vol'] = df['r'].rolling(window).std()\n",
    "    df.dropna(inplace=True)\n",
    "    df['d'] = np.where(df['r'] > 0, 1, 0)\n",
    "    features = [symbol, 'r', 'd', 'sma', 'min', 'max', 'mom', 'vol']\n",
    "    for f in features:\n",
    "        for lag in range(1, lags + 1):\n",
    "            col = f'{f}_lag_{lag}'\n",
    "            df[col] = df[f].shift(lag)\n",
    "            cols.append(col)\n",
    "    df.dropna(inplace=True)\n",
    "    return df, cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, cols = add_lags(data, symbol, lags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2183"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.iloc[:10, :14].round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1445\n",
       "1     738\n",
       "Name: d, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = data['d'].value_counts()\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cw(df):\n",
    "    c0, c1 = np.bincount(df['d'])\n",
    "    w0 = (1 / c0) * (len(df)) / 2\n",
    "    w1 = (1 / c1) * (len(df)) / 2\n",
    "    return {0: w0, 1: w1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weight = cw(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.755363321799308, 1: 1.4789972899728998}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1091.5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weight[0] * c[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1091.5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weight[1] * c[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import logging\n",
    "import tensorflow as tf\n",
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam\n",
    "from sklearn.metrics import accuracy_score\n",
    "tf.get_logger().setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seeds(seed=100):\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    tf.random.set_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = Adam(lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(hl=1, hu=128, optimizer=optimizer):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(hu, input_dim=len(cols),\n",
    "                    activation='relu'))\n",
    "    for _ in range(hl):\n",
    "        model.add(Dense(hu, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer=optimizer,\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seeds()\n",
    "model = create_model(hl=1, hu=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 7.39 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1707d08edf0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(data[cols], data['d'], epochs=50,\n",
    "          verbose=False, class_weight=cw(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 0s 2ms/step - loss: 0.5975 - accuracy: 0.5932\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5974940657615662, 0.5932203531265259]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(data[cols], data['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['p'] = np.where(model.predict(data[cols]) > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1468\n",
       "0     715\n",
       "Name: p, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['p'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = int(len(data) * 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data.iloc[:split].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = data.iloc[split:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seeds()\n",
    "model = create_model(hl=1, hu=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 16.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "h = model.fit(train[cols], train['d'],\n",
    "          epochs=50, verbose=False,\n",
    "          validation_split=0.2, shuffle=False,\n",
    "          class_weight=cw(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 [==============================] - 0s 1ms/step - loss: 0.6096 - accuracy: 0.5934\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6096417903900146, 0.5933562517166138]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(train[cols], train['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 1ms/step - loss: 0.5931 - accuracy: 0.6133\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5931044816970825, 0.6132723093032837]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test[cols], test['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['p'] = np.where(model.predict(test[cols]) > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    284\n",
       "0    153\n",
       "Name: p, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['p'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.DataFrame(h.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res[['accuracy', 'val_accuracy']].plot(figsize=(10, 6), style='--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu, std = train.mean(), train.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ = (train - mu) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_.std().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seeds()\n",
    "model = create_model(hl=2, hu=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 13.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "h = model.fit(train_[cols], train['d'],\n",
    "          epochs=50, verbose=False,\n",
    "          validation_split=0.2, shuffle=False,\n",
    "          class_weight=cw(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 [==============================] - 0s 1ms/step - loss: 0.5126 - accuracy: 0.8654\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5126276612281799, 0.8654066324234009]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(train_[cols], train['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ = (test - mu) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1863 - accuracy: 0.6819\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.186348557472229, 0.681922197341919]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_[cols], test['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['p'] = np.where(model.predict(test_[cols]) > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    329\n",
       "1    108\n",
       "Name: p, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['p'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.DataFrame(h.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res[['accuracy', 'val_accuracy']].plot(figsize=(10, 6), style='--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropout "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(hl=1, hu=128, dropout=True, rate=0.3,\n",
    "                 optimizer=optimizer):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(hu, input_dim=len(cols),\n",
    "                    activation='relu'))\n",
    "    if dropout:\n",
    "        model.add(Dropout(rate, seed=100))\n",
    "    for _ in range(hl):\n",
    "        model.add(Dense(hu, activation='relu'))\n",
    "        if dropout:\n",
    "            model.add(Dropout(rate, seed=100))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer,\n",
    "                 metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seeds()\n",
    "model = create_model(hl=1, hu=128, rate=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 14.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "h = model.fit(train_[cols], train['d'],\n",
    "          epochs=50, verbose=False,\n",
    "          validation_split=0.15, shuffle=False,\n",
    "          class_weight=cw(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 [==============================] - 0s 2ms/step - loss: 0.4428 - accuracy: 0.7772\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.44277796149253845, 0.7772050499916077]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(train_[cols], train['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 1ms/step - loss: 0.5767 - accuracy: 0.6476\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5766616463661194, 0.6475972533226013]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_[cols], test['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.DataFrame(h.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res[['accuracy', 'val_accuracy']].plot(figsize=(10, 6), style='--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.regularizers import l1, l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(hl=1, hu=128, dropout=False, rate=0.3, \n",
    "                 regularize=False, reg=l1(0.0005),\n",
    "                 optimizer=optimizer, input_dim=len(cols)):\n",
    "    if not regularize:\n",
    "        reg = None\n",
    "    model = Sequential()\n",
    "    model.add(Dense(hu, input_dim=input_dim,\n",
    "                    activity_regularizer=reg,\n",
    "                    activation='relu'))\n",
    "    if dropout:\n",
    "        model.add(Dropout(rate, seed=100))\n",
    "    for _ in range(hl):\n",
    "        model.add(Dense(hu, activation='relu',\n",
    "                        activity_regularizer=reg))\n",
    "        if dropout:\n",
    "            model.add(Dropout(rate, seed=100))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer,\n",
    "                 metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seeds()\n",
    "model = create_model(hl=1, hu=128, regularize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 11.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "h = model.fit(train_[cols], train['d'],\n",
    "          epochs=50, verbose=False,\n",
    "          validation_split=0.2, shuffle=False,\n",
    "          class_weight=cw(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 [==============================] - 0s 1ms/step - loss: 0.3641 - accuracy: 0.8940\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.36406561732292175, 0.894043505191803]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(train_[cols], train['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 2ms/step - loss: 0.9213 - accuracy: 0.6705\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.9212585091590881, 0.6704805493354797]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_[cols], test['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.DataFrame(h.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res[['accuracy', 'val_accuracy']].plot(figsize=(10, 6), style='--');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seeds()\n",
    "h = model = create_model(hl=2, hu=128,\n",
    "                     dropout=True, rate=0.3,\n",
    "                     regularize=True, reg=l2(0.001),\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 15.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "h = model.fit(train_[cols], train['d'],\n",
    "          epochs=50, verbose=False,\n",
    "          validation_split=0.2, shuffle=False,\n",
    "          class_weight=cw(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 [==============================] - 0s 2ms/step - loss: 0.4535 - accuracy: 0.7835\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.45345136523246765, 0.7835051417350769]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(train_[cols], train['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 2ms/step - loss: 0.6433 - accuracy: 0.6751\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6433439254760742, 0.6750572323799133]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_[cols], test['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.DataFrame(h.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s7ESn0/H000/x4x//D2orzeY7UNI29k3ax35J29i3sdo+f3z3JKl5dfz6rrmE+NnnVAnDNZy28fXt+2lLGZMlzvPcc3/CYHBlxozEUQ9YQggh7IdFUfD3dEEVpRq3AWskScgaQbW1NRw5crjXe15e3sybd6mNKhqYhx/+qa1LEEIIYQfUKhW3XRGLnd30GjMkZI0gb28fVq68xtZlCCGEEIN2LlipVCq7nyrBXsm9ICGEEEKc5+usan716rGLLtAsLk5ClhBCCCHO8+XJUoqqjLg4aW1dypglIUsIIYQQvVTWt5JWUM+kEHe7XhvQ3knIEkIIIUQv+06WAbA4MdjGlYxtErKEEEII0cNktnDgTDl6JwdmTxmfk4+OFglZQgghhOhxIrua5tYuLpseiNZBY+tyxjSZwkEIIYQQPRJjfLj76jhiQtxtXcqYJyFLCCGEED10Wg2XTQ+0dRnjgtwuFEIIIQQAZ0saaG032bqMcUNClhBCCCHoMpn5y/tnePyVo1hkGR2rkJAlhBBCCL7OqsbY1sWcOD/UsoyOVUjIEkIIISY4i6Lw0VeFqFSwODHI1uWMGxKyhBBCiBHW0Wmmy2SxdRl9OpFVTUl1C/Om+uPn6WLrcsYNebpQCCGEGAHGti5Ssqs5nlVNekEdMaEe/OSmGTho7Kt/w6IobDuYj0oF11wWaetyxhUJWUIIIYQVpRfUsetIERmF9Zgt/xlAPmuKv12OdWpp68LNRUd4vCsBXtKLZU0SsoQQQohhaG7tJL+8iYRoH6C7Bys1v46IAFfmTPFj1mRffD2c8fNzo7q62cbVns/VRcf/3Jpk17czxyoJWUIIIcQQGNu6eGlHOql5dahU8KcN8zE4a5kR48NTDyTj6+F83mdMZgu7jxWzKDEIvZPWBlX31mWyoHXovn157v+F9chXVAghhBiCvSmlnM6tJdTPwJpF0ai/uRPoqNVcMGABHDhTzua9ubz+SRbKMOaiKq4y8vu3UjiTVzvkY1gsCr/51zFe2ZkxrFpE3yRkCSGEEENwLLMKjVrF/9yayJWXhOEygJ6pBQmBxAS7czSjisNpFUM+d1uHiYzCev7y/hmqGtqGdIyjmZWUVLcAoLLDsWLjgYQsIYQQYpAq6loprjIyLdJrQOHqHI1azb3XTMVJp+GN3dlDDkixoR7cvDQGk9nC37amDno8lcWi8OHBAjRqFasuixhSDaJ/ErKEEEKIQaqqb8XgrGX2FL9Bf9bXw5nbl8fS3mnmxQ/TMFsGFpCKKpt5+p2TtLR3AbBibhjzEwIprGjmnc/PDqqGIxmVlNe2cum0APz6uLUphk9ClhBCCDFICdE+/GnDZVwy1X9In0+OD2BunB+5pU2cyavrd//CimZ+/1YK6fl1ZBc19Lx/2xWxBPvq+fxEKUczKgd0brPFwvZverGuuTRiSPWLgZGQJYQQQgyBRq0e8sSiKpWK762YzA+/k0BijM9F9y2oaOIPb6fQ2m7irqvjSIr17dnmqNXw4PXTmBTiTri/64DOnV/eTE1DG5dND8RHerFGlEzhIIQQQgzCwTPlNLd2sXBGEC5OQ/816uLUPd0DgKIomMzKedMo5Jc38fTbJ2nrMHH3qjgunRZ43nECvfU8etvMAQ9ejwl257f3zUNrZzPPj0fyFRZCCCEGYdfRIrbsy8NaD+S1dZh4bssZ/rUrs9f77Z0m/vzeKdo6TdyzauoFA9Y55wJWYUUz2w7k93tOPw9nPF0dh1e46Jf0ZAkhhBADVFrTQml1C0mTfHB2tM6vUK2DmvrmDlLO1jA9yrtnnJeTzoE7lk/GZLYwLz6g3+MoisIbu7PILWvCz9OZ5P/6jNli4eUdGSyYEURcuKdVahcXJz1ZQgghxAAdz6wCYM4Qnirsi4NGzf3XxqPTqnntkyxO59bQZTIDMHuK34ACFnT3Zt3zzfQQr+3Kory2pdf2w6mVfJVe2XMNYuRJyBJCCCEG6FhmFQ4adc9YKmvx93Lhu5fH0tZh4s/vneb5D1KHNAu7v6cLd62Mo6PLzPNbU+no6g5rZouFHYcKcNCouDo53Kq1i75JyBJCCCEGoLTaSFlNC9OjvKx2q/DbFiQEMmeKHxq1iuRpAUOehX32FD+WzQyhtLqFN3dnA3AotYKqhjYWzAjCy83JmmWLixjQv5JDhw6xe/duvL29UalUrF+/vtf2n//85xQXF/e8zsrKYsuWLYSEhLB06VKCg4MB8PPz4+mnn7Zi+UIIIcToMJkVEqK9B3z7brBUKhX3XxdPa7sJg/PwFo++aWkMuWWNZBbV02js4MOD3/RizZNerNHUb8hqa2vj8ccfZ+fOneh0OjZs2MDhw4dJTk7u2Wf+/PmsXLkSAKPRyKOPPkpISAgAq1evZsOGDSNUvhBCCDE6wgNc+dGNM0b0HGqVatgBC7oH06+/YTqOOg0nsqupaWxn2cwQ6cUaZf2GrJMnTxIUFIROpwNg5syZ7N27t1fIOhewADZv3syaNWt6Xh87dowXX3yRlpYWFi5cyMyZM61ZvxBCCCEu4Fygmj3ZjwZjJ/On9z0FhBgZ/Yas2tpa9Hp9z2uDwUBtbe0F97VYLOzfv5+1a9f2vPfII4+QkJBAW1sbq1ev5oUXXiA8vO/uSk9PFxwcNIO5hiHz9R3Y7Lhi9Enb2DdpH/slbTMy3tuTTU5JA/dcOx1fz6HPkm6r9rkrRKZs6M9ItE2/Icvb25uWlv88Bmo0GvH29r7gvnv27GHJkiW9BuslJCQA4OzsTFxcHCdOnLhoyKqvbx1w8cPh6+tKdXXzqJxLDI60jX2T9rFf0jYjQ1EUdn9VSF1TO7dfPolqk2lIx5H2sV/DaZuLhbN+ny5MTEykrKyMzs5OAE6cOMHixYtpaGjAaDT22nfLli2sXr265/Xhw4fZt29fz+vCwkJCQ0MHfQFCCCGErZRUt1BR10pCtDdOOpnDWwxcv/9anJ2d2bhxI0888QSenp5MnjyZ5ORkNm3ahIeHB/fddx8AGRkZRERE9Lq16OXlxXPPPUd6ejpVVVWsWLGC2bNnj9zVCCGEEFZ27NwEpHH+Nq5EjDUqZSiznY2g0epKlW5b+yVtY9+kfeyXtE3fjG1d/OHtFIJ9DNx19RQ06oFNE6koCj9/8Qj1ze08s2EBjrqhjxmW9rFfNrtdKIQQQgyXoijsO1VGblnjqJ/boii8tCOdokojh9Mq+OfHmVgG2L9QXGWksq6VGdE+wwpYYmKSm8tCCCFGVFuHidc/yeKr9ErC/A08vm4OAK0dJvROw58Tqj81DW3klTUxNcKTtg4TB89U4OKo5ZZlMf3Oqu6m17F6QSRRwe4jXqcYfyRkCSGEGDFFlc38bWsqlfVtRAe5cf918ahUKj48mM++U2U8fHMigd76/g80DH6eLmy8cw4ODmrUKhX/780T5Jc30WWyoNNevHfKw+DINZdFjmh9YvySkCWEEMLqFEVhb0opb+3JwWS2cOXcMG5YFIWDpnuUilqtorapgyffOMGPbpxBVJCb1Wtoau1+Kt7NRddrpvNHbknEWefQb8Dq6DKj/SaYCTEUMiZLCCGE1XWaLHxyrBhHrZoffieBm5bG9AQsgKuTI1h31RRa2rv4/VsppOZdeJLrobIoCi9+mM7GV45S09jWa5uHwbFnfFV6QR1H0isveIwPDxbwP88foqTKeMHtQvRHerKEEEJYTVuHCWdHBxy1GjbcMB1nR4c+18tbOCMIV2ctf9+exjObT3P31XFWW3x556EC0vLrSIj27vP8HV1m/vFhOi1tXTjpNMyI8enZpigKxzIraW034TeMGd7FxCY9WUIIIYZNURQ+O17MT/9+mIq67pU7gn0N/S5InBTry09uTkSn1ZBWUGeVWjIK69l6IB8vN0fuWTW1z9t9jloND14/DY1axfNbU8kubujZVljZTHVDO4mTfPq9rShEXyRkCSGEGJbW9i7++kEq//7sLCoVNLV0DurzsaEe/HLtbNZeOaXnvaFO4dho7OCF7WmoVSoeuG4aBueLP70YG+rBg6unY7EoPLP5FIUV3XMlHcv4ZgLSKX5DqkMIkJAlhBBiGCrqWnnita85kV3NlDAPNt45l9hQj0Efx9/LpWfM1hcppfzz40zMFsugj/PPjzNpaunkO4ujiRngtAsJ0d7cs2oq7R1m/vjuScprWziWWYWjTsP0KK9B1yDEOTImSwghxJDklDTy5/dO0dph4sq5YXxncTRq9fCexDNbLBw4XUZ+eTM1je0smxXC9CgvtA4Du2X3ncXR+Hu5sHzO4NbJvWSqP60dJj4/UUJFXSs1je3Mi/cf8HmFuBAJWUIIIYbE290JFycHbr18EpdND7TKMTVqNY/cksTzW1NJy68jo7AeR52GxBgfrr0sot85tYJ9DdyybNKQzr0kKZj50wNRq+EntyRiGIWJUsX4JrcLhRBCDJjJbKG8tgUAT1dHfnvvPKsFrHOcHR14+KYZPLZ2NlfNC8PNRcuR9Eo03/SSWSwKX2dV09FlBqC+uYOn3jxBafXwp1rQOqjRqNXER3gRHtD3mnRCDIT0ZAkhhBgQY1sXz39whrKaFh5bOwdvdye0DiPzt7pKpSIy0I3IQDe+syiaspoW/DxdADhb0sBfPziDTqsmIdqHuqZ28sqayCxqINjXMCL1CDEUErKEEEL0q7TayLPvn6a6oZ1Zsb79PrVnTSqVqld48nZz4urkcI5lVnE8s/spwNmTfVk6M3jUahJiICRkCSGEuKhTOTW8sD2N9k4z11wawXULIm261IyPhzNrFkVzw8IoiquM5JU1MS/ev9/FnoUYbRKyhBBC9OnA6XJe/SgDBwc1D1wXz9w4f1uX1EOlUhHm70qYv4ydEvZJQpYQQog+TQp1J9hXz50r44gMtP4izkKMZxKyhBBC9HIiuxpXFy2TQjzw93Rh411zbXp7UIixSkKWEEIIAJpbO3nz02yOZlQR4qvnV3fNRaVSScASYogkZAkhhOB4ZhWv786iubWL6CA37ro6TgaSCzFMErKEEGICa2nv4l+7sjieWYXWQc3NS2O4YnbosJfHEUJIyBJCiAnNQaOmqLKZmBB37loZR4CXi61LEmLckJAlhBATTKOxg6IqI9OjvHHUavjfW5PwMDhK75UQViZrFwohxARS29jOL185yvMfpFLX1A6Al5uTBCwhRoCELCGEmCAUReHVjzNobu1iZXI4Hq6Oti5JiHFNbhcKIcQE8eXJMtIL6kmI9mZVcrg8PSjECJOeLCGEmACqG9p45/McXBwdWHvlFAlYQowCCVlCCDEBbDuQT0eXme9eMQlPuU0oxKiQ24VCCDEB3L48luhgd5LjA2xdihAThoQsIYSwsqr6VpQLvK930mJw1gLQYOxAo1ahd9aO6LI1iqKgUqlw0jmwJCl4xM4jhDifhCwhhBim5tZOnHQatA4aAH7x0lFMZst5+11zaQSrF0YB8NquLE7m1KBSgcFZi6uLDldnLZGBbty0NAaA8toWiquMRAa64evhPOi6LIrCs5tPkzTJh4UzgmQclhCjTEKWEEIMgaIo5JU18fmJUo5lVvG9FZOZnxAIwPzpAZgt5/dlhQe49vx3XIQnarWK5tZOmlu7aDR2UFbT0mu+qlM5tbz7RQ4OGjW3LIthSVLwoILSnuMlnM6tRatRs3BG0DCuVggxFBKyhBBiEDo6zRzJqOTzEyUUVRoB8PdyQaf9z3NE37tySr/HuWJ2KFfMDu31nslsobPrPz1gUyM8uWlJDB99Vcgbu7PJLKxn3VVxuDj1/6O7sq6V97/MxeCs5fYVk6UXSwgbkJAlhBCD8MzmU2QWNaBWqZgV68uSmcHEhXtaJcQ4aNQ4aP4T1sL8XQnzd2VunB8vbE/jeFY1hZXN/GD1dML8Xfs8jsWi8PJHGXSaLNx1dRzuet2waxNCDJ6ELCGEuAiLolDb2N4zJmpxUjCxoR4snBGEl5vTqNTg5ebE/343iW0H8tnzdSmOOs1F9//0eDE5JY3MnuLH3Dj/UalRCHE+CVlCCHERnx0vYcuXuTy4ehoJ0T42Cy0atZobFkZzxexQXF26e6bKalpwN+jQO2l79lMUheziBlxdtNy+PNYmtQohuknIEkKMKx2dZlg/R1cAACAASURBVGqa2gn20Q/7WMVVRjbv7Z4lPTzAzQrVDd+5gNXWYeKZzaewWOCB6+KJDnYHQKVSsf6G6dQ0tuPmIrcJhbAlmfFdCDGuvPtFDo+9dIQDp8uHdZzOLjP/2J6GyazY5bgmR62GS6cFUtfUzv978wS7jhRR29gGdAetoUz5IISwLglZQohxZc4UPwD++XEmWUX1Qz7Oe3tzKa1pYenMYBKifaxVntWo1Squmx/JI7cmYXDW8u4XOdz5m91s3Z9n69KEEN+QkCWEGFemhHvyyC2JqFTw3JYzlNe2DPoYZ/Jq2fN1CYHeLty0JGYEqrSeuHBPfnXXXOIjPFEB4Rd56lAIMbokZAkhxo2mlk4sFoWpEV6svXIKLe0m/vzeKZpaOwd1nHB/V2ZN9uX+a+PRaS/+JJ89cNPrePjmRF7/1VUkxfrauhwhxDckZAkhxo2Xd2bww2f309rexfyEQK65NILqhnZO5dQM6jhuel2/c1HZG5VKhZudjRsTYqKTpwuFEONCl8lCVlE9vh7OuHwzpcH1CyKZGuHJ5DDPAR3j4JlyDM5aZsTY3xgsIcTYIz1ZQohx4WxJA50mC/GRXj3vqVSqnoBlURTO5NX2+fnSmhZe+ySLl3dm0NZhGvF6hRDjn4QsIcS4kJZfB9ArZH3b5i9y+dO7p/jyZOl527pMFv6xPY0uk4V1V03B2VE6+YUQwychSwgxLqTl1+GgURMb6nHB7YuSgjA4a3n9k2xS/6tHa8u+XIqrjCycEcRMGTguhLASCVlCiDGvsaWToiojsaHuOPbxNKC/pwsPrUlArVbx/NZUiquMAKQV1PHJ0WL8vVy4ddmk0SxbCDHOScgSQox5BmcHfn7HLK6bH3nR/WJC3LlnVRztnWb+/N4p6ps72HO8BI1axX3XTO134WUhhBgMGXgghBjzNGo1Md+s3defuXH+VDe08f6XeRzNqOTB1dPIKWkkMtA+1iYUQowfErKEEGOaoihU1bfh5+mMSqUa0GdWzgsnIsCtZ5D8lPCBTfEghBCDIbcLhRBjWkl1Cz/7x1e89dnZAX9GpVL1+RSiEEJYi4QsIcSYdm7qhojAsTM7uxBiYhjQ7cJDhw6xe/duvL29UalUrF+/vtf2n//85xQXF/e8zsrKYsuWLYSEhLBt2zYyMjJQq9WEhYVxyy23WPcKhBATWmp+93QM8RHSMyWEsC/9hqy2tjYef/xxdu7ciU6nY8OGDRw+fJjk5OSefebPn8/KlSsBMBqNPProo4SEhFBRUcErr7zC1q1bUalUrFmzhnnz5hERETFiFySEmDg6usxkFzcS6mfA3eBo63KEEKKXfm8Xnjx5kqCgIHS67oVHZ86cyd69e3vtcy5gAWzevJk1a9YAsH//fuLj43sGoyYlJbFv3z5r1S6EmODOFjdgMltkfJUQwi7125NVW1uLXq/veW0wGKitvfD6XxaLhf3797N27VoA6urqen1Wr9f3+dlzPD1dcHAYnblqfH1lDIe9kraxb/bSPnmHCwGYnxhiNzXZmnwd7Ju0j/0aibbpN2R5e3vT0tLS89poNOLt7X3Bfffs2cOSJUt6eq68vLwoLCzs2d7S0kJYWNhFz1df3zqgwofL19eV6urmUTmXGBxpG/tmT+2zfFYw4b56fF21dlOTLdlT24jzSfvYr+G0zcXCWb+3CxMTEykrK6OzsxOAEydOsHjxYhoaGjAajb323bJlC6tXr+55vWDBAtLS0lAUBYCUlBQWLlw4pIsQQoyM6oY2Xvski+qGNluXMmhOOgdmxPigHaXebyGEGIx+e7KcnZ3ZuHEjTzzxBJ6enkyePJnk5GQ2bdqEh4cH9913HwAZGRlERET0uj0YEBDAXXfdxe9+9zs0Gg033nijDHoXwo6YzBb+tjWVgopmSquN/PS2magHOKGnrVU1tOGk1eCm19m6FCGEuCCVcq6byU6MVleqdNvaL2mb0fP+l7nsPFyIo05DR6eZ762YzOKk4It+xl7a5+/bUjmaUcWT98/D39PF1uXYBXtpG3Fh0j72y2a3C4UQ41N7p4nDaRX4uDvxy7WzcdPr6DJbbF3WgFgUhfSCejxdHfHzcLZ1OUIIcUGydqEQE5STzoGNd86lwdhBoLeeTQ8ko9OOjbFNhRXNGNu6mJ8QOOD1CoUQYrRJT5YQE4yiKBjbugAwOGsJ8TUA9AQsk9lCeW1Ln5+3B+eW0pkm82MJIeyYhCwhJpjDaRX87IXDZBTWn7fNYlH47etf84e3T9LWYbJBdQOTll+HCpgqS+kIIeyYhCwhJpCqhjbe2J2N2aLg4+503na1WkVijA/1zR1s/jLXBhX2r6PLTG5ZIxGBrhictbYuRwgh+iQhS4gJwmyx8OKHabR3mrl9eSy+fQwYXzkvnEBvF/aeKCWnpHGUq+yfo1bDpu9fytorp9i6FCGEuCgJWUJMEDsPFZJb2sTcOD+S4wP63E/roGbdVVNQgH/uyqTLZH9PHHoYHAnzl+VJhBD2TUKWEBNATmkj2w8W4O3myPdWTO73ibxJIR4sSQqmrKaFj48UXnTf0ZZRUEdHl9nWZQghRL8kZAkxAbjpdUQHu3HPqqm4OA1sHNOaRdHEBLsTGeg2wtUNXHVDG79/+yT/2J5m61KEEKJfMk+WEBOAn4czj942c1BzSrk4OfCz2wf3mZGWVtA9dYM8VSiEGAukJ0uIcexkTg25pd2D14cSls59psHY0TM3lS3J/FhCiLFEQpYQ41RtYzsvfZjOH989RWv70Oe8Mpkt/Pa14zy/NZUGY4cVKxwcs8VCRkE9Pu5O+HnKUjpCCPsnIUuIcaikysgf3z1Ja4eJm5fG4OI09JEBDho1K+eF09Zh4t+fZluxSmhp78JiGdga9QXlzbR2mJgW6WVXtzCFEKIvMiZLiHFEURT2ny7nzU+z6TJZuHJuGAsSAod93EVJwRxOr+R4VjVbvjhLuK+eIB89DprB/Z1mbOsiu7iBzKJ6sosaKK4y8ou1s4kMdENRFJ568wQero4EeXcfP8hHj5+nMw4aNdklDQDEy61CIcQYISFLiHHk8xOlvPlpNi6ODjxwbTxJsb5WOa5apWLtlVP41atHeXVHOgBP3HMJQT56TGYL2w7kE+SjJ9hHT4CXy3kLTdc2tvPM5tOUVBt73nPQqJkc5oHZ3N2T1dphorDKSPZ/TYCqUav47uWTuHJuGIkxPni6OlrlmoQQYqRJyBJiHEmOD+BsSQPfWRyNj7t1xy0F++jZeOdcCmtaOFtQ1zMuqrKulZ2H/zOXlkoFvh7dvU/rb5hOgJcL7gYdDcYO4sI9mRzqweQwD6KC3NA6/CeM6Z20/PXHC6lv6qC0poWyc/+rbcHb3QmVSkWgt96q1ySEECNJpSjKwAZEjJLq6uZROY+vr+uonUsMjrTNwCmKwt6UUlxddMye4jcq5/zv9unoNJNX3tQTis4FpM4uMw9cP43EGB+ge/FptVrGUo0k+d6xb9I+9ms4bePr2/fqE9KTJcQY1dpu4p+7MjmeWYWvhxOJk3wGPUbKGhx1GuLCPYkL9+z1vqIovQaoS8ASQkw0ErKEGIPyy5v4+7ZUqhvamRTizv3XxtskYF2MPAEohJjoJGQJMYYoisJnX5fw7uc5WCwKqy4N57r5kWjU9hWwhBBCSMgSYkxRFPg6qxoXJwfuuyZepjMQQgg7JiFLiDGg0diBu8ERtVrFA9fFoyjIVAZCCGHn5B6DEHbMoijsOlLE//ztMFlF9QB4GBwlYAkhxBggPVlC2CljWxcv7UjndG4t7nqdrcsRQggxSBKyhLBD2cUNvLA9jfrmDuIjPLnnmngJWkIIMcZIyBLCzpzOreXZzadRUFi9MIqrk8NRy3QIQggx5kjIEsLOTA7zYGqEJ6sujSA21MPW5QghhBgiCVlC2JjZYuFoRhUqYF58AI5aDQ/fnGjrsoQQQgyThCwhbMRssXAkvZIPDxZQWd+Gs6OG6dHe6J20ti5NCCGEFUjIEmKUmS0Wvkqr5MNDBVTVt6FRq1iUGMTV88IlYAkhxDgiIUuIUVZQ3szLOzPQqFUsTgpm5bwwfNydbV2WEEIIK5OQJcQIM5ktHEqtIDbUgwAvF6KD3bl12SRmTfbFy83J1uUJIYQYIRKyhBhBp3NreGN3NjWN7Vw6LYB7Vk0F4Io5oTauTAghxEiTkCXECKlv7uBv29IwmxWWzQph5bxwW5ckhBBiFEnIEmKEvPP5WTo6zay7agoLZwTZuhwhhBCjTBaIFmIEpBfUcTSjiqggN+YnBNq6HCGEEDYgIUuIEeDs6EC4vyt3LJ8sS+IIIcQEJbcLhRgBkYFu/HLdbFQSsIQQYsKSniwhrKi+uYPKulYACVhCCDHBScgSwor+/Wk2j718hKLKZluXIoQQwsYkZAlhJWfyavk6u5qIQDdC/Ay2LkcIIYSNScgSwgq6TGbe/DQbtUolg92FGEUmi4kj5V9jsphsXYoQ55GQJYQV7DpSRFV9G8tmhRAqvVhCjJoP8z7htYx3+Cj/M1uXIsR5JGQJMUzVDW3sOFyIu17HdfMjbV2OEBNKam0mAHmNBbYtRIgLkCkchBgmnVbDrMm+JER54+Ik31JCjKbWru6neVdGXm7jSoQ4n/xGEGKY3PU67rsm3tZlCDHh1LXX09TZzAzfacR6xti6HCHOI7cLhRiizi4zp3NrbV2GEBNWh7mTad5TiPOKtXUpQlyQhCwhhmjn4UL+/N4p9p8qs3UpQkxIgXp/vj/jLrydPPnNkac5XpFi65KE6EVClhBDUFnfysdHCvF0dWROnJ+tyxFiQtNpdFS0VFLUXGrrUoToRUKWmBAsFoWaxjarHEtRFN78NBuTWeGWZZNw0snQRiFGW5upjVdS3+RUdSrBhkAAio3Sqyzsi4QsMa6ZLRYUReGvH5zhd69/TV1T+7CP+cH+fFLz6oiP8GT2ZF8rVCmEGKy8xiK+rjpFUVMJzg5O+Dh7U9pchqIoti5NiB4D+hP80KFD7N69G29vb1QqFevXr++1XVEUXn/9dQBKS0tpamriySefBGDp0qUEBwcD4Ofnx9NPP23N+oXoU1uHicdfOcqixCAmhXiQcraGZzaf5tHbZuLsOLTep7SCOnYcKsDPw5l7Vk2VRaCFsJG8hnwAojy656YLNQSRUn2Gho5GPJ08bFmaED36/U3T1tbG448/zs6dO9HpdGzYsIHDhw+TnJzcs8+2bdtwc3Pj+uuvByAzM7Nn2+rVq9mwYcMIlC7ExX12vJiaxnZMZoWV80Kpamhjb0opf9uWyg+/k4BGPfiO3KnhnnxncTRzpvjhbnAcgaqFEAOR21iAChVR7mEAhLh2h6zi5lIJWcJu9Ptb5uTJkwQFBaHT6QCYOXMme/fu7bXPhx9+SENDA6+99hp//OMf0ev1PduOHTvGiy++yJ///GdOnDhh3eqF6IOxrYtdR4swOGtZPicUlUrFbVdMYnqUN6l5dbz56dlB3VaorO+e8FClUrFyXji+Hs4jVboQoh8mi4mCpmKCDAE4O3R/L072jGFRyGUSsIRd6bcnq7a2tldoMhgM1Nb2nhuorKwMo9HI+vXryc/P55577uGjjz5Co9HwyCOPkJCQQFtbG6tXr+aFF14gPDy8z/N5errg4KAZxiUNnK+v66icRwzecNtm54402jrM3H3tFMJCPHve/8Xdl/DoXw+wN6WU5ckRTI/26fdYp7Kr2fjSUW6+IpZbrpg8rLrGC/nesV8ToW3O1ubTZekiPmBSz/X6+k5jbsw0G1fWv4nQPmPVSLRNvyHL29ublpaWntdGoxFvb+9e+xgMBmbMmAFAZGQkRqOR8vJyQkJCSEhIAMDZ2Zm4uDhOnDhx0ZBV/02PwUjz9XWlurp5VM4lBme4bVPf3MGH+/PwdHVkbqzPecf6wfXTSCuoI8DNsd/z5JU18fu3UgCFQA8n+TeDfO/Ys4nSNjUNzUS7RxDqFDamrneitM9YNJy2uVg46/d2YWJiImVlZXR2dgJw4sQJFi9eTENDA0ajEYDk5GSKi4uB7hBmNpvx9fXl8OHD7Nu3r+dYhYWFhIaGDukihBiolLPVdJosXHtZBNoL9Ip6uTmxICEI6H5oo68nDktrWvjTuyfpNJm5/9ppTI3wGtG6hRADE+MRycOzHmSmX0Kv9/eVHOIPx//as56hELbWb0+Ws7MzGzdu5IknnsDT05PJkyeTnJzMpk2b8PDw4L777uPee+/l97//PX//+98pKiriqaeewtHRES8vL5577jnS09OpqqpixYoVzJ49ezSuS0xgS2eGEO7vSnjAxbt+FUXhnx9ncjKnhl98b3avcVY1DW08/XYKLe0m7lw5hVkyVYMQdq+uvYH8pkJKjOXEekbbuhwhUCl2NqnIaHWlSret/RrNtvn8RAlv7M4m0NuFn98xC72TFoC395xl97Fiblkaw/K5YaNSy1gh3zv2ayK0TU1bHbsLv2BuwExivpm+4ZzjlSd5Ne3frIlZxdKwhTaqsG8ToX3GKpvdLhRirCipNvK3ralU1g38VsHSmSEsnxNKeW0rf91yBpPZAsBNS2LYcMN0CVhC2Jmz9bkcLDtCqbH8vG0hhu5hACUX2CaELUjIEuPGB/vyOJZZ1TPdwkDdtDSGmbG+ZBY18JO/HkRRFNRqFUmxcotQCHuT21gAQLR7xHnb/Fx80Km1FMsahsJOSMgS40JuWSMpZ2uICXFnepR3/x/4FrVKxb3XTCUy0I3m1i7O5NWNUJVCiOHKayzASeNEkCHgvG1qlZpgQyAVrVV0mbtsUJ0QvcnKtmJc2PJlHgBrFkYNaakbR62Gh2+eQXpBPVMjPPv/gBBi1DV3GqlsrSbOKxa16sJ9BNN9puLn4ku7uQOtRjvKFQrRm4QsMealF9SRUVjPtCgvJocNPSDpnbTMmeJnxcqEENaU13OrMLLPfVZELB2laoTon9wuFGPe9oMFANywMMq2hQghRpSiKATpA4jxiLB1KUIMiPRkiTHvvmumcjq3logAN1uXIoQYQYl+00n0m97vfjvydtNp6eSGmFWjUJUQfZOeLDHmebk5sTgp2NZlCCHsxMnqMxwo/QqLYrF1KWKCk5Al7EpjSyc7D+RxOLWC1PxaWtv7fkLodG4Np3NrsLP5dIUQI6C4uZQtOTsuOD/WfwsxBNFh7qSmrXYUKhOib3K7UNgNRVF4YVsqmUUNPe/93x2ziA52R1EUfvSXAzjpNLi66HBz0ZFf3kRLexebvn8pHgZHG1YuhBhpabVZ7CnaR6RbOMGGwIvuG+IaxLHKFEqM5fi5yHx3402bqR1nBycAusxdNHUaL7ifm85g8ydMJWQJu3HgdDmZRQ0kTvIlIcqL5tZOfD271xPsMlnwMDjS3NpJYUUzZkt379WVc8MkYIlxy6JYqGqtJkDvb+tSbC63MR+AqAtMQvrfemZ+by47bxFpMbZVtFTypxN/56kFjwOQ31TEMykvXHDfn8x6cED/XkaShCxhFxpbOnn3ixwcdRoeujkJTKZe23VaDb+6ay7Q3ePV1mGitd2El5uTLcoVYlScqk7jpdTXuTH2OhaHXGbrcmzGoljIbyzEx9kbd8eLL/wO3T1ZAMVGmfl9PFEUhbezPqClq5Watjp8nL1w0xm4JGDWBfc3aPWjXOH5JGQJu+Cs07BkZjCerk74ejpfdKFOlUqFi5MWFyeZaFCMXxbFwkf5n6JCRZxXLF0WE1r1xPyRXd5SSZupnQSf+AHtb9DqiXQLx0MnTxyPJ8crT3K2IY9p3nH4OHsBEKD353tTb7ZxZX2bmN+xwu7otBpuWBht6zKEsBspVWcoa6lgjn8Sr6e/A8Ajs9fbuCrbyG0oACB6EPNjPTL7ByNTjBgwRVHYW3IQlUo17J7YNlMbW3J2oFU7cGPsdVaqcOTJ04XCpto7TXyRUorFIk8I2rvUmoyeX3ZiZFkUCx8VfIZapWZl5BU4qB0oaCqmsaPvHt7xzsvJ84KLQgv7pCgKH+TsZPPZ7fg4eQ37eDvzPqWps5kV4Ut7erHGAunJEja1ZV8enx0vwWJRWDYrxNbliD60drXxRuZ7dJg6mBb2JBbF0ufacWL4TlSdpqKlknmBs/Fz8WG6z1TONuSRVpvBpUFzbV3eqFsYkszCkORBTdfS3Gnkq/Lj+Lv4kuA7sNuMwjoUReH9nA/5ovgA/i5+hLoO72e7sbOFg2VH8HX25vKwRVaqcnTIT0lhM3llTew5XoK/lwsLZ1z8kWxhWzvyd9PcaWSaTxyP7fkDnxR8YeuSxrX69gZ0Gh1XRSwDYLpPHACna9JtWVaf2kztFDWVjPicdYNZ/L3D3MnW3I84VpkyYvUoikKZsQKzxTxi5xhrFEVh89ntfFF8gAC9Pz+aeT/ujq6UGSv4x+l/0W5qH/QxDTo9P5v7Y9ZOvdXmUzIMloQsYRMms4V/fpyJAqy7cjJaB42tSxJ9KG4uZV/JIfxcfLgx9jrKjVWcqbXPX/bjxRXhi/ndZb/Ax9kbAD8XX/xd/MisO0unue8Jekeboii8lPoGjx74NU8df5aU6jNWP8fZ+jw+zt9DbVv9oD7n7eSJs4MzJcYyq9d0zqeFe/lzyt+pba8bsXOMJYqi8N7ZbewtOUig3p8fJd2Pm677adCjFSc4VZPGW1lbhhTG/Vx8iHQPs3bJI05ClrCJT44WUVJtZOGMQCaHedq6HNEHi2Lh7awPUFC4OXY1bjpX4nxjKGwqprGjydbljTsWxdLzC+jcZIvnJPhMpcvSRVb9WVuUBkBrVytflR8nuz4H6O5ZMlm6esLgwdIjVj9nSvUZduR/QmNn46A+p1KpCDEEUt1aS7upw+p1na3PZXveLhw1jrg7ulv9+GORSqXCy8mTIH0AP0y6H1edoWfbNVEriHQL53jlSQ6UDezfSUtXK8+m/IO8xoIRqnjkScgSo66+uYPtBwtw0+u4cUmMrcsRF3GsIoWCpiJm+iUwxWsSALOCuid3TK3NsGVp49KxihQ2HX+W4ubze1/mBszkjribRn1yxZauVg6VHeOvp17m0QO/4fWMd/m8+EDP9rvib+OxS35ClHs4WfU51LUPrsepP3kN+TioHYY0rifENQgFhbKW/pfiGYymzmZeTfs3KpWKdVNvxVGjo7GjiXeyttJlRz2No8WiWHrWibw8bBH/O3tDr4AFoFFruHvabei1LmzO3kZRc0m/x92et4us+pwx/cCNhCwx6jwMOu5ZNZW1V05GL3Nd2bUZvtNYEb6UNZOu6XlvdnB3yDpTIyHLmswWMx8XfEapsQIXB+fztgcZApgXOBu91mXUano76wMePfBr3sx8j/TaLIIMAVwbdSU3xFzds49OowNgXuBsFBSOlJ+w2vnbTe2UGMsJdw0Z0hxhoYbuheNLLhBah8qiWPhn2ls0djZzXfRVPdNK7CrYw77SQ/zjzGtjPmjtLTk44DF23b3dW3gne2vP/n2Nm/J08mDt1FswKWZePvMGbaa2Po9b2FTMwdIjBOj9WRq6YGgXYgfk6UIx6lQqFXOm+Nm6DDEATg6OXBt9Za/3Agy+BHxrfJBujA1EtVdHK1OobqtlfvA8vJ37voXeYe7EbDHhMgphK0gfQICLH3MCkkjyTcDXxbvPfWf6zSCvoZDJXtbrnc5vKkJBIdojckifD3ENwknjRIe502o1fZz/GVn1OUz3iWNZ6MKe92+IWUVtez1ptZm8cOZf3Dd97Zj83qhvb+C97G0AeDt5keQ3nZl+CYS5hpz34IFFsfBW5vscKj9GqCGIDnMHTg4XX4Uj3nsKK8KX8lX5ceraGwg2nP8HRe9hCtejUY/dMbuajRs3brR1Ed/W2mq9b4aL0esdR+1coltTSyfv7s0hOsgdnbbvbxppG9sraCriTE06oa7B5/1g1esdUZt0JPpOx1/vi0amchg2s8XMy6lv0Gnu5J5pd5w3Huuc3IYCnjj6NGqVmljP8yfvHe73TlNnM9tyPybGIwoHtYZQ12AWBM8jxiOq3x40rdqBGb7xeDp5DPn8/+1I+dfkNOSxPHzJkBZ6dtUaWB6+ZMghrS9VrTXcPe32nl486L4dlug3nZLmUtLrsihsKibJb3qvgGCvP9vaTO28m7WVSPcwXBycCXULQa1SUWIs5WxDHgfLjvJVxddEuYfj8c34M4ti4c3MzRwuP06YazAPJd2Hs/b8wHQhMR6RJAfO6XO+q/2lX3Go/Chz/JNYFrbwgvtY23DaRq/ve/1c6ckSo+atPWc5kl5JiI+eJTNlTix7de6vyOLmUkJdg4lwO/+JHll017qOVJygpr2OhcHJFw0pwYYAUBTO1KSzKmq51c6vKArHK0/yXvY2Wkyt+Dr7sCR0/pDnQmvparXKbU0FBb3WhUj38CF9fjBTPgxUrGc0P5n14AWPrVU7cM/07/Fy6uucqcnglbQ3eSDhTqvXYE2KovBm5mZSqk7j6eTOysgrSPSdRqLvNLrMXaTXZZNSdYb0usyeBxw6zJ08ceRp6trrCXcNZX3iPbgMMGBBdyA16LrXFaxvb8DY1UroN+tNWhQLB8q+wknjxOqYVda/4FEmIUuMitO5tRxJryQqyI1FicG2LkdcxP7SryhuLmVuwMwLBqxva+lqxdnBya4mJq1pq+Vg2VFm+iUQ6jo2/q0dKjuKg0rD8vAlF93PycGJSZ7RZNRlU9dej5fT8J/Mbexo4q2sLZypSUen1nLjpOtYGJI85OP9K/1tTlen8dvLfoGTQ99/4Q/ENVErWBW5fFhhqbq1lqz6s8R7TxlyL5vZYmZr7kcsC1uIh6P7RevRqh24Z9odvJ7xLsmBc4Za9qj5svQQKVWniXaPYEX40l7btBotM3zjmeEbj9li7umVS6vN7A5YbqGsnzG4gPVtbaY2njr2LA5qB34290fotS6oVWoenvkgpcbyAS0Gbu/s5yejGJf2nyrj6bdTxeekzAAAIABJREFUeHbzaTRqFeuunIJabf2/LoV1NHU282HeLpwdnFj9rcHNF/JJwec8euDXFDeXjlJ1fStvqeSdrK1YFAsVLVXsLvyCoxXWG4A90h5KupcHZ9w9oBCQ4DMV6F7maLhOVafxmyNPc6YmnUkeUfzfJQ+zOPSyYYVmHycv2s0dVpsza7i9UWl1mbyVtYWzDXlDPsaO/N18Xryfj/I/HdD+DmoH7oz/bs8TuW2mdquOC7OWwqZitpzdgUGr565pt1107NO3tyX4TOXHM7/Pj5LuH3LAAnB2cGZhSDL1HQ28lv5OzxOKTg6Og1qn0p5JyBJW1dTaSU3jf54YOXimnLSCesL8Ddx3bTwhfoaLfFrY2tacj2gztbMqakXPJIJ98XfxxaJYbDoLudli5v+3d+eBUVb3wse/z0xmkkkm62TfIJCNbBD2ICKCLEqpIOJu3bVWrbe293a5vbe8rde2tvp639vFWvWq1NalKtQqAorsOwHCEhJC9n3fJ5ntef8IRJBAthkyJL/PX2Tmmec5Myfk+c05v/M7nxVv4Vf7X2J7xW5yG/NJDkrA4GEguzan94+2u9Nr9QNOGE9zYvV3H503qtpTA+27mY/2TgcNx6yI6QDsrTowrPOcaDjF1rJdtFs6hnWeaGPPNNRQvwwcr89lU8mXhBhM/X7x6EuXrZs/HH2N3x95jS7r4Kudu0qntZPXjv8Fh+rg/pQ7e3OtBsJD40F8QNwFOWlDtXT8QpIDEzjekMvP9vyaHRV7h31OdyLTheIipTVt7MypwtvLA19vPb7eOny99fh56/A3emI0XLhiprXDwqH8Og6equVUaRNzUsN56Bs937bvvCERby8PQgKG/m1HXMjusFPYUkJC4ASnnresrYJ91YeIMUYyL6r/6aLkoEQ8FC3H6k+yfMISp7ZlICraq1ib+x5lbRX46325I+kWUk3JAEwOSWVv1UGKWkrd+hvxwerDtFs7uSZy5oC3CwnyCiTaGEl+0xm6bN2DnpJrt3TgqdWj0+qID4jj2Wt+jKGPkhFDFWwIIjFgIvnNZ6jrbLjsisTL2Vq2i5ONeaQFT8KIz5DbE23s2bKrvH3wtbIazE28efIdPDQePJR275A+J53GA39Pfw7X5vB/97zKQ8nfckmu2GCVt1fSZmln6fiFTDIljlg7NIqG+1Pv5Jf7X6Kxq4mS1jKujZo9Yu1xNgmyxEUCfD2xqyr/2FV80XMzJ4Xy7ZvTAPhsXyn7TtZQWtvGuXIqEyP9mBj91TeiceFX/5y6u3n52BucajzNf8761yHfwPoSbYzkgZQ7CfY2DWi6yMvDk8TAeE425tFgbrps2QFn21K6nXVnNmBX7cwKn8atCcsvKGkwNXQye6sOkl171G2DLKvDxrozG2i3tpMZmo7/IJb735a4AqPOe0g5T3899XcqOqr53tRvE+Dp79QA65zZEdPJbz7D3uqDQwrAcxvyOdmYxwT/cZiGmXfm5eFFiMFERVslqqoOOMCxOWy8fuJtOm1m7kpa1ZuYPVhajZYHUu7EbDVzuOoE2UE5TAubPKRzOVNiYDw/mfnMFf1/eym+eiPfnnw/eyoPOnVBhzuQIEtcxM9bz9KZsUxPDKHNbKWt00prh4U2s5W484KmuhYzZbXtTIj0Y0ZSKNOSQjH5X75Gihi6Ew2nSA5MYHb4NE425PFZ8Rfcm3Kb086vKArTwzMH9Zr04BRONuZxrOEk86OvcVpb+uOn98VXb+TOpFt6p8/OlxwYj7eHgcO1OaxKWO5Wifnn7Kk8QFN3MwtirsXf029Qrx1q4Hi8Ppej9SeY6B+Hv35w1xyMzNB03stfR3bN0UEnrlsdNt7LX4eCwm2JK50y6hPtG8Xh2hyaupsHvFigqqOG6o5aZoRlMidy5rCur9VouT1pJc/tf5EPTn9Mqimp33pSrlLZXk2QV0BP8OnEL2nDFesbTWzS6Ft1LkGWuEBuSROJMf6EBBj6neK7d3ES9yxKdIuh79HuaN0JXjn2JnMiZnBn8ioiir9gf002S8YvINQ7eFjnbu5uYVPJVpbFLRr0svv04Em8m/8Rx+pcF2Spqkp5eyXZtTksir0Ob50308KmkBaccsmRHK1Gy+yI6bRbO+iydQ8rOXegrA4bB6qzKWrpKaB5vjRTMlNC0wHYVr6bsrYKjtfnotPoWDRu/pCu51AdFLeWMt4vdkBBpMVu5f389WgUDbcnrXDp/1u9Vs9jGfcRZYwc9HU+L9lGrbme+dHXDHn06OuijZEcrTtOXWfDgIOsGN8ofjTjaXz1Rqd8VqHewdw8aTF/P/EpnxZ9zi0JV748Qbulg98ffQ2Dhxc/nPH0kKroi8GRT1j0Kq1p4zd/O8zslDAe/WbqgF4jAZbr1ZsbWJv7LjqNjvlnaxfdFLeI147/hc+Kv+BbKbcP6/wfnP6Y7NocYoyRZEUObsl5oFcAD6TexUQn76enqiqlbeUcrj3G4doc6rsagZ4pnFUJy1EUpd+psvO3AnKVNks7WkWLt86ABoWPCj6hs4+tQvz0vr1BVl5TAUfrjgOwZNyCfhcYXMqHp//Jl+U7+d7Ux4kfQLHNzSVfUt/VyIKYa4k6m6fkSomBg6/83mZpZ2PJF/jpfZ06bTQ/eg4LY+f1G1S0Wdr5qOATlsUtwmQIcvpIz4rkJeRU5g+ov5zNoTp48+Q7NHe3MC8qSwKsK0Q+ZdFrw75SAGanho9wS8Q5VoeN147/BbOti3uSV/feHKeEpBHpE87+6myWjLueMJ+hbVN0qvE02bU5xPmNY1bEtCGdY3rYlCG97lIcqoP/2vci1Z21AHhq9UwLnczU0AxSzia2j6RWSxtHao9zuO4Yp5vOsCphOdfHzEWr0XJ/as8qLb3mwlVXBt1XU0O3J65k5cRlaBRlWHWukoMS+LJ8J8frc/u9add1NrCpdCv+ej+WxS0a8jUHy2K3klN3nBRT8oBGE331Rh5Lvx+banNqrlh/U3OqqnKo9ijv56+n3dqBj87bJUG63kPP05mPOv28A7GpZCsnG/NIMSUNefRUDJ4EWQKAumYz+3NriA4xkj6h760OxJX34el/UtpWwezw6ReMMp0bzfq4cCMdts4hndvqsPFu/kcoKNyetHJYeUtWh426znoijYMP0EtayzhYc4T4gDgmh6ShUTSM84shxjeKzNB0JgUlDXkPuPymM3xatJllcYtI6GMbmsHYWbGXgzVHKGgu6p0OjPOLvWAkKnUAQaCzCiwmBsaj0+jIqT/JivibLntsp60Tk1cgy+IWX9FcoG3lu1h35lPuSFrJtQNYsQq4bKVbXWcDlR3VTA65cJS+pbuNd/M+5Gj9CXQaHasSll+R/MKW7jbqzPXDGtVSVZWy9r5LU/jr/Xt/1w5UH+afhRsJ9OzZoNkdcxRHKwmyBACb9pehqnDj7FiZAnQTpW3lbK/YTaRPOLcnrbjo+XOVmIf6B/OL0m3UdtZznRNyX361/yXarO388pr/GNRmrrsq9/HXUx8A0NTdwuSQnpWrw50CPcehOjjdXMih2pxhB1mHa49xurmQCf7jyQxNJzMk3an79A2WXqtjUlAiOfUnqO2su+zefuP8YvjJzO+hVa7sRrszwjNZf2YDe6oOXjbIstgtfFTwCYvHXe+yz/Td/I/Ibczn+WvX9OYe5jUW8OrxtXTazMQHxHF38uph5zgOhNVu5dcHXsKByn/O+tch5wyqqPz6wP/r87mbJ9zI4vE9OwjsqtyHoig8mHY3Rt3Qy2GIwZMgS9DaaWFHTiUmPy9mThratJNwvljfaB5Ku4dIn/A+i/6dH1x12y14DqIwoNnWxeaSrfjqjXwjbvi5L/EBceys3EdRa+mAv5mXtVXyXv56vD0MfCvldpKDnD+CkRAwAaPOhyO1x1id8M1BBYBftyphOd46w6CKNrpaevAkcupPcKw+l4WxFwdZ3XYLHdYOgrwC8RiBHJwAT38mBSVysjGPqo4aInzC+jxuY/EWtlfswcvDi5sn3uiStsT4RpHbmE9Fe2VvvliodzB6rZ5vTFjCtVGzr9gIj06rY170NXxc+Bn/LNrIbYkXf4m6HLPN3DuduiDm2j6PifX7aqXetLDJLB2/kAlD3ANSDJ0EWYKGli78jXoWTY9Bq5Fh5JFmd9jRKBoURRnQRsw7K/byUcGnfH/adwY8XWfw8OKZad+hpbvVKSvv0oNT2Fm5j2P1JwcUZJltXbx2fC02h41H0u7tswyDM2g1WjJDM9hRsYeC5qIBV1U/x2q3ktuYT3pwypCmQl0t1dTzuZ1uPsPC2HkXPb+h6HO2le/iO5MfHPZI3lDNjpjOycY89lQd4JY+Nvyt6azj89JtBHj6X7R3njOdK0q6uWQbqgpJQfEEegWwJuuHI5IEvjB2HvuqD7K9fA+zI6YT6zuw8gWbir9ke8Ue/mXqYwQbTAPKHRvoVK1wPrmjCuIi/Pjlo1nMz7w6NtMd7d7J+4hXj/+FLtvAtuDw0/vSZe9iQ/Hng7pOlDGCFFPSUJp4kaTAePQaHccGuNXLhuLPqTM3sCh2vssCrHPOBarZtUcH/dqdlfv407E32VK2w9nNcgp/T19+OOO7PJL2rYueq+6o4Yuy7Rj1Rsb5xYxA63pkBKfg7WFgf3U2dof9gudUVeW9vHXYVDu3Jnxz2BtKX0702c3CTzbm8ffT/0A9W0F5pFbZ6TQe3J64EhW1d9/N/nxWvIX1hRsAegtAC/cmQdYYd+4PjUaj4KGVX4eRtq/qELur9lNvbhhw/kx6cAqxvlEcrj1GZXv1ZY+12K28fvxtytsqndHcXrqz+UE1nXXUdNb1e/yyuMV8c8LSK7IdT3xAHL56I0fqjl90k78ci93KppIv0Wv1zAof2srLKyHWN/qiaVBV/erGfWvCN52yx9xQ6bQ6podl4qsz0tzdcsFzh+uOcarpNJOCEplyNh/PVUIMJpIDE0gPTuHxyQ+4Re5pclACU0MzKG4tZU8/ez1uKPqcjws/I8grkH+Z+m23KiQqLk2mC8cwm93Bs28eZMakUJZljR/p5ox5VR01vJP3IV5aLx5Ku2fAe9kpisKyuMX8Med/+bRoMw+n33vJYzeVbOFQ7VECvPyJdlKhx3PSglM4Wn+C3MZ8wi6RhG132NFqtHhq9SwZ77qpofNpFA1Lxy9EgwaH6kDLwILXnZV7abW0sXjc9Rj17p0sXHs2uF0QMguAQzVHyG8+Q5opmYzglBFuHdwSvwwPjccFgY1DdfDxmc/wULTclnizy4MejaLhqcxHXHqNoViVsBy7w058wKX3Iv2kaDOfFm3G5BXI05mPYTLICvCrhQRZY9j+3BpKa9tJiBn6ap78pgLG+40b8hJ70aPbbuHVY2uxOKw8nHbHoFc4pZqSGecXw+G6Y5S3VfYZQNV21rG5ZCsBnv7cNN75tZKmhKQS4xtJtLHv4K3T2skL2X/khtjryIqY7vTrX85gl+Rb7BY2lXyJp1bfZ66TO1FVld8deZVOm5l5idMw27r4sOCf6DQerL4CwctA9PWFQaNoeGLKwxS3ll52ZeRoF+Dpz6MZ913y+ebuFraU7sDkFXQ2wBr5vQbFwMn80Bilqiob9pWiURSWzBhavsax+pP89+FXeOvkO05u3djzRek2qjtrmR99DZlnK4MPRs9oVk/gdKw+96LnVVXlvfz12FQ7qxKWuyT3xVvnTYxvVJ83dVVVeSv3Xao7amg0Nzr92gNltVsHNGW4o2IvbZZ2ro+e6/ZL3hVFIS04BbOti9z6AqwOK+P9Ylk87nqCDe4zpdTS3cr7+evZXfnVtFiwIcjpxWyvZkUtJZS2lV/wWICnP9/NfIR/mSoB1tVIgqwxKudMAxV1HcxMCSW4nz0KL8V8NjH7cN0x6kfwxjkaeGo9CTGYWD5h6ZDPkRKUxI9mPM2NcQsveu5I3XFyG/NJDkwgM2TwQdxAqapKWVslFe1VFzz+Rdl2jtXnkhQYz41xN7js+pezr+oQP9r5c/KaCvo91lvnTZh3CAvcfBTrnHNTggcrcvDT+/Joxn0sHX/x78FI0igatlfsYWv5Tv566u/kN50Z6Sa5ldrOel449Af+mvt3HKqDrWW7aLd0AD11zoazO4AYORJkjVEb9pYAcNOsoddNmRk+lftS7gDgs+IvnNKusWph7Dz+c/a/DmuESVEUYnwvXiGqqiqbSrb05L64eGPg6s5afnXgpQt+H840F7P+zAb89b7cn3rniFWbNhmC6LJ3k12b0++xWRHT+Y9ZPxj0htkjJT4gDk+tng2nv/xqMYubVfX21RtJN02ior2KXZX73XbF5kgJ9Q5mZvhUytorefHQH3n/9Hr+lvfhSDdLDJN7/S8UV0RpTRv55S1kTDQRHWoc1rmmh00h3DuUfdWHqOtscFILxw5VVXuXbjvrpljbWcfrx9+mtLVn2kFRFJ6a8ggPp997yYR0Zwn3DsXkFcTJhjxsDhttlnZeP/E2qqryQOpdQ94M2Rkm+I8jwNOfI3XHsTlsfR5jsVux2C3A1bX5uYfGg0ifnjpQnxRtGuHWXNrss7l4Oo2O1QnfHOHWuJ8V8Tdh8PCiqLWEUO9gVifKZ3S1065Zs2bNSDfifJ2dlityHR8fzyt2LXfj56MnZXwQ6RNM+BuHNnLyyrG3aOhqJD5gAkadN9m1OXhpPQdd7LEvY6lvilpL+O/DrxBiMDkt+besrZJ1Zz6lpbuVGeGZQE/isbMCrMv1j6IoNJgbKWgpIiFgAv6efhQ2F5MVMZ2ZQ9yA2lkURaGpu5nTzYXE+cX2+Xl/Ubad10/8lYn+40d0y5yhiPAJp4tOlo674YruTzgYwQYT9eZG5sdcQ+IIFUcdSf39bfPUehLmHYpDdfBQ2j1utbvAaDec+46Pz6Xvo7K6cAxSFIXEYawoLGur4Gjd8d6fM0MzeFzr6bTClmPJl2U7qTc3OLWOUVJgPBP94zjekMtL2S8zJ3ImM8Iyr9jITFrwJL4s30lO/UmSgxJ4fPIDV+S6AzE1dDJflu0kuzbnoiKoXbYuPi/dhkNVCfe5+raXivOP5UfxT1BX1zbSTbkkrUbL/al3jnQz3Nq5PUnF6CDThaOAwzHw0r+7jlVR3dg5rOvtqToI0LsMX6NoSAue5HY5IO6uqauZI3XHiTJGkHCZGjmDpSgK35jQs9LwdHMh2bVHr+jU17n3sq18F6qqolE0bvO7Md4vhkDPAHLqT2D92pTh1vLddFg7WRhzbe++cEIIMRwykuWmbHYHFXUdtHZaaO2w0NZppc1sITrYSFZazx5qf996hi3Z5XRZ7AT6ehIZ7EOkyYfoUB+uzbi4VlFjaxdvbDhFaKCBZx+eNaQbr9Vh42D1YXx1RlKCLhy5arW0sblkK3MjZxF2FY4EXGnbK/bgUB3Mj57r9CAoMTCe5MAECltLuDXhZqeeuz9ajZbZEdPZV3WI5u4Wt5p20yga7khaib+nPx7nVdQ327r4onQb3h4G5sfMHcEWCiFGEwmy3NRbG/PYmVN10ePTk0J6gyxvLw9CAgwY9FrqWro4UdTIiaJGwgINvUHW8aIGPttXSmSwD/XNXdgdKktmxg75pn6s/iQdtk4Wxsy7aCuPwuZitpTtoM3SLlMC/bDYreyq3IePzttldYIezbiPLlsX/p5+Ljn/5dyeuJLlE5a4ZU5JX3slbi3bRafNzPIJSzC4aT6TEOLqI0GWmzL5eREX4UdmQjB+Pnp8DTp8vfUE+X2VYHfT7HHcNPurEgzmbhuV9R10Wb4qtlhV38nJ4iZOFjcB4G/Uk5UaPuR27T07VTi7j4rdGSGpRBkjOFhzhKXjFxDuEzbk64x2h2tz6LB2smTcApdVy/fU6vEcoT3r9Fodeq37BVjnqKpKWXsFEd5heGg8KGguxMfDe9CV4YUQ4nIUVXWvvbyvVNJmSIivWyeIOpO520Z1YyeV9R2MC/MdVtmGY/UnOd1cyC3x3+jz+aN1x3nl2FtMC53Mg2l3D+kaY6Fv7A47OfUnifOPdcvRnssZDf3zWfEXfFy4kcfS7yMjJBVVVak3N171m+6Ohr4ZzaR/3Ndw+iYk5NKlaQY0krV79242bdqEyWRCURSefPLJC55XVZW1a9cCUFFRQWtrK7/85S8BWL9+Pbm5uWg0GmJjY7njjjuG9CbGClVVUQGNE3N0DJ4exEX4ERcx/Gmj9OAU0i+z4WxGcCoxxkiya3NY2r6QSOPQR81GM61GO6Ttc4RzTApK5OPCjRyqPUpGSCqKolz1AZYQwv30G2SZzWZ+9rOf8cknn6DX63nqqafYs2cPWVlZvcesX78ePz8/VqxYAcCpU6cAqK6u5vXXX2fdunUoisKqVauYPXs248ePd827GQXySpt547NT3HVDAhkTB7dJsCupqkqrpR1/z8sXk1QUhWUTFvNyzhtsKP6ch9LuuUItvHocrDlCYuDEES3MOdbF+kZj8griYM0R9Bo9K+Nvwvsqqe4uhLh69Luu+siRI0RGRqLX9+R2TJ06la1bt15wzMcff0xzczNvvfUWL774Ij4+PRuq7tixg9TU1N4k68zMTLZv3+7ktzC6bD1SQW2TGS+9e6XLFbWW8tPd/8Xmkq39HptmmsTCmHksHne96xt2lak3N/DGib/x52NrR7opY5qiKGSE9IzI7q7aj1bjXv/fhBCjQ79/WRoaGnqDJgCj0UhDw4Xbp1RWVtLe3s6TTz5JUVERDz/8MJ9++imNjY0XvNbHx+ei135dYKA3Hh7ayx7jLJebRx0JLe3dZOfXERPmy5zMaLfa1uPD4qM4VAep0RMH9Lk9Fjq81YXO6pvW7naMem+3qdP06eGNqKgsS77e7X7/BuNqbvs53/RcyO7K/dyXuZro8NEzVTga+mY0k/5xX67om36DLJPJREdHR+/P7e3tmEwX/kEyGo1MnjwZgLi4ONrb26mqqiIoKIiSkpLe4zo6OoiNjb3s9Zqahlcoc6DcMQFxw74SbHaVuenh1Ne3j3RzelnsFnaVHCDA059wTdSgPreytgr0Wv2gtnRxVt84VAcvHvoj0LMn2M6Kfdwz6VY8RmjUosvWxRdnduGv9yXekOB2v38D5Y7/d4ZCjw8vzPsFiqKMivcDo6dvRivpH/flqsT3fr/eT5kyhcrKSiyWnj19srOzmT9/Ps3NzbS39wQCWVlZlJWVAT1BmN1uJyQkhGuvvZYTJ0707gp/+PBh5s2bN6Q3Mdo5VJVtRyrReWiYk+ZeyeJH6o7TZe9mdvi0QY0IlbaV86sD/836gk9d2LpL21t1iKLWEgI8/ThUc4QDNdlsKdsxIm0B2FedTZe9i2ujskYs0BMXcqfRYiHE6NPvX3qDwcCaNWt49tlnCQwMJCkpiaysLJ5//nkCAgJ49NFHeeSRR/jNb37Dyy+/TGlpKb/+9a/x9PQkPDycBx98kOeeew6tVsvq1auvqqT3Q3l15Jc1c/vCeKeu9utLXkkTtU1mrkkLx8fLNXWThupcbaxZg9zgN8YYRZxfLEfrT1DWVkGMb5QrmtenDmsn6898il6rZ1XCcjy1erJrc9hQ9DnTw6YQ5BV4xdoCPaNqW8t34qFouSZq1hW9thBCiJEhdbIu4/EXttFttfPnf5uPVuPanB6b3cHRgnrCAr2HVcfK2dotHfx41y+I84vlmWnfGfTrcxvy+d3RV0kPTuHbGfcP6DXOGFL/26kP2Fm5jxUTb2LRuPlAT7C4Nvc9poSk8Uj6t4Z1/sHqsHby1sl3Mep9uHfSbVf02s4mUx7uS/rGvUn/uK8RrZM1FjnOxp7RIT4uD7AAPLQapiW5335/Rr0Pv5jzYzqsQ8uVSw5KYIL/OI7Vn6S0tZxYv2gnt/Bixa2l7KrcT7hPGAtiru19fFb4NHZX7udI3XFONJwi1ZTs8rac46Pz5vHJD+BQHVfsmkIIIUaWeyy5ckONLV10W+1EhRjpstg4Xd7ssmsVVLTQ0mFx2fmHK8DTnyhjxJBeqygKy+IWA/BJ0SZnNuuS2i0dGHU+3JG44oL9FRVF4faklWgUDZ8Vf3FF2gJw/mCxu6xyFEII4XoyknUJ5fU9KyojTd7811uHaGzr4lePZeHr7dy94Byqyiv/OEFnl43/+9RcdB7ucxOuaK+isauJlKCkizaDHoykwHgmBSUS6h2CQ3W4PNBIC57Ez+f8CH0f+/ZFGSN4KO0eEgMmurQN53v/9D9o7mrmzuRV+OrdZypYCCGEa7nPHd3NVNT1rJyMDjFy7eRIzN12Pt5d7PTrnCxqpL6li2lJIW4VYAF8Ubqdl3PeoLClpP+DL0NRFJ6Y/BCrEpa7NMDqsHZitpkB+gywzpkSkoa3zuCydpyv02pmT9UBStsq8Pa4MtcUQgjhHtzrru5GKut7cpAiQ3xYMDWKkAAvvsyuoNbJdby2HqkEYH7mlVt5NxBdti4O1+YQ7BVEfEDcsM93bqm8Q3UMOb+rPx+e/if/Z+9vqOmo7fdYVVXZV3WItSffw1VrP2wOG5+XbsNit3Bd9JxhjQYKIYS4+kiQdQkP3JTMzx+aSYi/AQ+thlXXTcTuUPlgW+EFx51syKPVMrQVCU1t3Rw5XU9smJHx4e5VBTi7NgeLw8rsiOlOqyVktpn59YH/x/+e+KtTzne+M83F7K0+iL/ej2DDwKp3760+xN7qg+TUn3BqWxyqg7/kvs+Pdv6CjSVb8NJ6MidyplOvIYQQwv1JkHUJHloN0SFGNJqeAGNGcihxEb4cOFXLmcoWOq1m1ua+x++Pvsb7+etxqA72VR2izTLwSu07cypxqCrzp0S5XVHEPVUHUVAGXRvrcgweBrx13uQ25lPYUuy089oddt7J+xCA25NWDmjESFEUbk9cgVbR8n7+P+i2D33hgdVuJaeupxYY9CS313TW4anVc330XL4/7Ql8ZPNhIYQYcyTxvQ/mbhtNbd2EBvaMYkHPTfm26+N5d0sUwqgRAAAUYElEQVQBRe0FvFa4gRZLKzHGSBaPW8DeqkO8fep9FsXOZ0X8TQO6TmNbNwZPLbNSwlz5dgatprOOwpZikgMTnF60c1ncIvKbCvikcDNPZT7ilHNuK99FZUc1cyJmMMF/3IBfF+4TysLYeWwq+ZLPir/g5ok3Dvi1FruV3MY8smtzOF6fS5e9m6yIGdwzaTUAj6Z/Cx+d++yZKIQQ4sqTIKsPJ4ub+P1Hx1g9fyI3zv7qph0d4cm4GWf4qDwbraJl+YQlLIqdj1ajJdw7hE+KNrGtfBcLY+cNaBXZfUuTWT0/HoOne3VDS3cLwV5BTh3FOic+II7kwARONZ2moLlo2Plezd0tfFK0GR8Pb26eOLDg9nxLxy/kQPVhvijdzqzwaYT79F+r7MPT/2Rn5d7e0S+TVyDXRM1ieuiU3mNkFaEQQgj5mt2HirObM0eF+FzweIe1g8N1OcT6RvNo8mPcEHN979SUTqtj8bjrsTisbC7ZOuBreXu5V4AFkBgYz8+y/o1poZNdcv5lExYB8EnR5mGfq9vWTZhPKDdPvBGj3qf/F3yNp1bP6sRvYlft7Kzce/H57Raya3PIrs356kEFfHVGFsXO59+mP8X/yfoRt8R/44oUWhVCCHH1cL87vBuoqOupkRUVbKTN0k67tYMInzBCvUP43tTHqa7Q8d9rc7nrBg+un/rVjXVO5Ew2lXzJ9oo9LIy9Dn/PvpPZG1u7+Ovnp1k6M5b4aP8r8p4GQlVVOmydGHU+PdNcLkoTm+A/nklBiVS2V9FqacNPP/Sk/zCfUH4w7YlhtScjOJXvTH6QSUGJAHTZujnRkEt27TFONJzC6rAS5h1KZkg6iqKwPG4JKycuc7s8OiGEEO5Fgqw+VNZ34KXXUmcv43/3/RWjzocfzXganVbHOL8YAjTdeGg1rN9ZxOzU8N7pPp3Gg6XjF/BO3kd8XrqVVQnL+zz/9qOVZOfXkTHRdMkgq6illLymAq6LnoPBw8tl7/V8h2qP8rdTH3J/6h2kB6e49Fr3TFqNt4c3eu3QNsO22C00djUR7hM27LwnRVF6t9jZUrqdfxR+htVhAyDUO5ipIRlkhmb0Hq8bYpuFEEKMLRJkfY3N7qC6sZNx4Ubez1+P2dbF4nHXX7Bizd/oydJZsazfWcRn+0pZOW9C73NZETPIrskhxrfvuld2h4MdOVV46bXMnNR3/k9jVxN/OPoanTYzOyv2cnfyrUwyJTr3jX6N2dbFh6c/xqbaCPd2fSJ+gOfQR/C67RZeznmDguZCHk67l8khqU5rV5BXICavIDJDM8gMTSfSJ1xGrIQQQgyJBFlfU9PYid2hEhRq5URnLVNC0lgYO++i45bMjGHr4Qo2HihlfmYUgb6eAHhoPHh66mOXPH/OmQaa2rq5PjMKL33fH7+Pzof04BQ6bWZONJzid0dfZU7EDG5J+AYGF1UN/7RoMy2WNm6KW0SI98DqTA2Xqqp8Wb6TwuZiHkq7Z0DBTLfdwh+Pvs7p5kImh6SRakpyapsmh6QxJTTdqecUQggxNkni+9eEBBj40d1T8Y9qAGDqedNE5/PSe7Di2jgsVgfrdxb2eUyn1Uy7taP35+b2bjbuLwPguimRl2yDp1bPvZNu47H0+/i36U8RZYxgd9UB/n7646G+rcuqaK9ia/kugg0mFsfOd8k1+qIoCnmNBRyuO0ZeU0G/x3fZuvnD0dc43VzIlJB0Hkq9Gw+Nc78nyKiVEEIIZ5Eg62v0Oi0J0f6c6TiFTqMj1TTpksfOzYggKsQHh6Nno+fzlbVW8B+7f8nrh9b3PlZa00Z+WTOJMQHEhl2c7H207jh7Kg8APTd7RVGI8Y3ih9O/y/IJS1k+YUnvsRa7dbhvFegZTXo37yMcqoPbEm++4vlGy+K+Wml4ue1tumxd/OHoaxQ0F5EZmsGDqXfJNjVCCCHcmkwXfo2524ZG62CifxzxAQpeHp6XPFar0fAf35qOXqdFVVUq6jvIK20ir7SZU2WNWCYqnLIepbTpBmIDQ0mIDuDeJUnM6iMXq66zgbdOvoddtZMclECgV8B519GydPyC3p9zG/NZe/I97khaScYw85HMti50Gt3ZqbfkYZ1rKGL9okkPTuFY/UlONZ6+ZO6ZQ3VgsVuYFjqZ+1LukABLCCGE25Mg62t+/uZBAH756K0DOl6v67nZHzhVy8vrv9oDL8CoZxyZlGl2s71yB/cErsLg6cH1fWwEbbVbee34WrrsXdw76bYLAqy+NJgb6bB28KdjbzI9bAqrE24eUo0oAG+dgSenPIzV4ZyRsaFYFreIY/Un+aRoE8lBCRc8p6oqiqLgrfPmu5mP4anVS4AlhBDiqiBB1nmsNju1TZ0kRA1+5VtCdACzU8NIjg0kKTaA0AADDtXBz/flsb/2IDdNXHDJLWr+XvAxZe2VzImYweyI6f1ea27UbCYGxPGX3Pc5WHOEvMYCbktaccn8sUtpMDdhMgSiKAp6rX5Qr3WmGN8oJoekcbTuOAXNRYSeLYLaaTXz2vG/sGzCIib4j8db55qkfyGEEMIVJCfrPFUNnWBopSl8C8frcwf12kBfTx5dnsq8yZGEBXqjKApajZYbxy/ErtrZWLylz9cdrD7Mzoq9RBkjWJ24YsDXi/AJ4/vTvsPK+GV02bt47fhfOFB9eMCvL2ktY83eX7Oh6IsBv8aVlk9Ywrcz7u/dZqfTauZ3R17lVNNp9lVnj3DrhBBCiMGTkazzVNR1oA2qol1Th+1sMcrhmhGWycbiLdSbG3GojosKZx6tP4GnVs9DafcMujCnRtFwQ+x1pAensKn4y97SAw7VgYJyyZVyDtXBu3nrcKgOJgYMfENlV4rwCSPCp6c+V7ulg/858mdK28qZFT6N2wcRfAohhBDuQoKs85TXtaMNqkan6EhxUhK4VqPl+9OeuGTO1AOpd1HdUUuYd8iQrxHmHcK9Kbf1/rylbAdnmou5I2kl/p5+Fx2/q3I/JW1lTA+bQmJg/JCv6wo1nXX8fMtvAJgdMZ27k28ddkV3IYQQYiTI3es8hU2laLzMpARNGvJ2L305P8A6V6agvK0S6BmNijSGO+1aqqqS11RATv0Jnt33AvuqDl1QGqHN0s4/zmzAS+vJLfHfcNp1nWVD0eeABFhCCCGufjKSdR7TuCZKW2BW5BSnn9ts6+Lv+f9Aq9ES5z+Ov+T2lGC4NirLqddRFIXHMx5gZ8VePjrzKW/lvkt27VHuTF5FgKc/6858SqfNzKqE5X2Oco20O5NXsSjpGiK00RJgCSGEuKrJXewsVVUp7c7HS+tJSpBzt2qBniruxa2l7Kk6wLt5H2Hw8GKSC64DPaNj86Ln8NOZz5AUGM/xhlP8Yu8LNJib8NUZifWN4rqoOS659nB5avVMiUiVAEsIIcRVT0ayznKoDm4cfwMdtk6XVD3XKBpuiruB10/8FYfq4IHUOwk2BDn9OuczGYJ4asoj7K7cT2FrCSZDICvib8LusEutKSGEEMLFJMg6a++JWv6528bdi9Ncdo3M0AxmNeQR7h3K5BDXXed8iqJwTdQsroma1fuYBFhCCCGE60mQRc9UYXFtEzVNZrx0rvtINIqGb6Xc7rLzCyGEEMJ9SOIL9ORKsRZtaAmRwUPbnkYIIYQQ4nwSZAHZtTmoih0frR/eXjK4J4QQQojhG/NBlkN1cKgmB9XmQYxh/Eg3RwghhBCjxJgPsopbS2mxtGBvCiM62P3qRgkhhBDi6jTmg6xDNUcBmBKSTlqcaYRbI4QQQojRYkwnIDlUB4drj+HtYeDRuddJaQMhhBBCOM2YDrI0ioanMh+htrNeAiwhhBBCONWYny6M8Anj8y1WPth2ZqSbIoQQQohRZMyOZDkcDgpbSgjShnGiqBGddszHm0IIIYRwojEbZJ2qL+CFQ79niv9MIIioEClCKoQQQgjnGbPDN7vLDgFgtEUBSKV3IYQQQjjVmAyyHKqDfWWHMep8sDT5AxAlQZYQQgghnGhMBlmnmwpp6W5jSmg6lfVmFAUiTN4j3SwhhBBCjCJjMicru7anAOm00AwIV/H30aPzkBIOQgghhHCeMRdkqapKQXMR/p6+xAdMIPGGMTmYJ4QQQggXG3NBlqIo/GTm97AbutB0S4AlhBBCCNcYk1GGVqMlyi+cQ3l1vLvlNPXN5pFukhBCCCFGmTEZZJ1z9Ew9G/eXYbE5RropQgghhBhlxnSQVVHXgVajEBpoGOmmCCGEEGKUGbNBlsOhUtnQQYTJGw/ZUkcIIYQQTjZmo4u6ZjPdFrtUehdCCCGES4zZIKu0uhWAqBDjCLdECCGEEKPRmA2yrDYHYYEGYiTIEkIIIYQLjLk6WefMyYgkIcJ3pJshhBBCiFFqQEHW7t272bRpEyaTCUVRePLJJy94/sMPP+Sdd97B09MTgFWrVrFixQoAFixYQFRUFAChoaG88MILzmy/EEIIIYRb6jfIMpvN/OxnP+OTTz5Br9fz1FNPsWfPHrKysi447sUXXyQ6Ovqi169cuZKnnnrKeS12AodD5b3P84kM9CIpNnCkmyOEEEKIUajfIOvIkSNERkai1+sBmDp1Klu3br0oyHr77bcJDg7GbDZzzz33EBAQAMCBAwf485//TEdHB/PmzWPq1KkueBuDU9dsZu2GXLJSwyXIEkIIIYRL9BtkNTQ04OPzVZkDo9FIQ0PDBcfMmDGD+fPnExQUxLZt23j66ad58803AfjBD35ARkYGZrOZlStX8qc//Ylx48Zd8nqBgd54eGiH+n4GpKC6HYCk8UGEhEheljuSfnFv0j/uS/rGvUn/uC9X9E2/QZbJZKKjo6P35/b2dkwm0wXHxMTE9P579uzZPP7449jtdrRaLRkZGQAYDAYmTZpEdnb2ZYOspqbOQb+Jwco9UwdAgLcHdXVtLr+eGJyQEF/pFzcm/eO+pG/cm/SP+xpO31wuOOu3hMOUKVOorKzEYrEAkJ2dzfz582lubqa9vWdE6IUXXsBmswFQXFxMdHQ0Wq2WPXv2sH379t5zlZSUXBCQjZSK+p6gUQqRCiGEEMJV+h3JMhgMrFmzhmeffZbAwECSkpLIysri+eefJyAggEcffZTg4GDWrFlDdHQ0+fn5PP/88wAEBQXxu9/9jpMnT1JbW8uSJUuYPn26y99UfyrqOjB4ajH5eY10U4QQQggxSimqqqoj3YjzuXoo1eFQ+f4fdhEe5MMP78p06bXE0MiQunuT/nFf0jfuTfrHfblqunDMFSPVaBRefOIafP29aW81j3RzhBBCCDFKjcltdRRFweA55uJLIYQQQlxBYzLIEkIIIYRwNQmyhBBCCCFcQIIsIYQQQggXkCBLCCGEEMIFJMgSQgghhHABCbKEEEIIIVxAgiwhhBBCCBeQIEsIIYQQwgUkyBJCCCGEcAEJsoQQQgghXECCLCGEEEIIF5AgSwghhBDCBSTIEkIIIYRwAUVVVXWkGyGEEEIIMdrISJYQQgghhAtIkCWEEEII4QISZAkhhBBCuIAEWUIIIYQQLiBBlhBCCCGEC0iQJYQQQgjhAh4j3YArbffu3WzatAmTyYSiKDz55JMj3aQxra6ujpdeeolTp07xwQcfANDc3MwLL7xATEwMxcXFPPPMMwQHB49wS8ee0tJSXnrpJVJSUqiuriYgIIAnn3xS+sdNOBwOvv3tb5ORkYHVaqWsrIznnnuOrq4u6R830dXVxerVq5k7dy4//OEP5f+OG7ntttvw9PQEQKPR8Oabb7qkf7Rr1qxZ44T2XhXMZjOPPfYYL7/8MnPmzGHt2rUEBgYSExMz0k0bs3bs2EFCQgL79+/n9ttvB+C5554jKyuLO+64A0VReOONN1i8ePEIt3TsKSkpITIykltvvZV58+axZs0aJk+ezKuvvir94wZUVaW8vJwnnniCrKws1q1bh0aj4YMPPpD+cRO//e1v8fX1xWAwMHfuXPnb5kaqq6v51a9+xS233MLKlSsB19x7xtR04ZEjR4iMjESv1wMwdepUtm7dOrKNGuOWLl2Kj4/PBY9t27aNzMxMoKePtm3bNhJNG/MyMjK44YYben92OBwYDAbpHzeh0Wj4zne+A4DNZqOmpoa4uDjpHzexbt06pk6dSnR0dO9j0jfuIz8/n1deeYX/+Z//6Y0DXNE/Y2q6sKGh4YIbutFopKGhYQRbJPpyfj8ZjUZaWlqw2Wx4eIypX1e3snnzZubOncvEiROlf9zMjh07eOONN5g/fz7p6enSP26goKCAwsJCnnnmGfLy8nofl75xH4888ggZGRnY7XbuvvtufHx8XNI/Y2oky2Qy0dHR0ftze3s7JpNpBFsk+nJ+P7W3t+Pv7y9/hEbQ3r172bdvHz/5yU8A6R93c+211/Laa69RXl7O22+/Lf3jBjZv3oxer+eVV17h0KFD5OTk8MYbb0jfuJGMjAwAtFot06dPZ9++fS7pnzHVu1OmTKGyshKLxYJeryc7O5u77rprpJslvua6667j8OHDREREkJ2dzXXXXTfSTRqztm7dysGDB/n3f/93amtrqayslP5xEwUFBZSXlzN//nwAoqOjKS8vl/5xA48//njvv7u7u+ns7OT++++nsLBQ+sYNnDlzhuzsbFavXg305J8uWrTIJf93xtwG0bt27WLjxo0EBgai0+lkdeEI279/P+vWrWPHjh3ceeedPPjgg3R1dfHb3/6WyMhIysrK+P73vy8rcEbA8ePHuffee0lLSwOgs7OTu+++mwULFkj/uIHS0lKef/55UlJSsNlsnDlzhp/+9KfodDrpHzexceNG3n77baxWK3fffTdz586VvnEDNTU1/PznPyclJYX29nZsNhs//vGPaW1tdXr/jLkgSwghhBDiShhTOVlCCCGEEFeKBFlCCCGEEC4gQZYQQgghhAtIkCWEEEII4QISZAkhhBBCuIAEWUIIIYQQLiBBlhBCCCGEC0iQJYQQQgjhAv8fYbZVn3aNJrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res[['accuracy', 'val_accuracy']].plot(figsize=(10, 6), style='--');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08448604345321653"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.mean()['accuracy'] - res.mean()['val_accuracy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_features = 0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seeds()\n",
    "base_estimator = KerasClassifier(build_fn=create_model,\n",
    "                        verbose=False, epochs=20, hl=1, hu=128,\n",
    "                        dropout=True, regularize=False,\n",
    "                        input_dim=int(len(cols) * max_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_bag = BaggingClassifier(base_estimator=base_estimator,\n",
    "                          n_estimators=15,\n",
    "                          max_samples=0.75,\n",
    "                          max_features=max_features,\n",
    "                          bootstrap=True,\n",
    "                          bootstrap_features=True,\n",
    "                          n_jobs=1,\n",
    "                          random_state=100,\n",
    "                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 40.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=<tensorflow.python.keras.wrappers.scikit_learn.KerasClassifier object at 0x000001707FD52220>,\n",
       "                  bootstrap_features=True, max_features=0.75, max_samples=0.75,\n",
       "                  n_estimators=15, n_jobs=1, random_state=100)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time model_bag.fit(train_[cols], train['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Public\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:425: UserWarning: `model.predict_proba()` is deprecated and will be removed after 2021-01-01. Please use `model.predict()` instead.\n",
      "  warnings.warn('`model.predict_proba()` is deprecated and '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7623138602520045"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_bag.score(train_[cols], train['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Public\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:425: UserWarning: `model.predict_proba()` is deprecated and will be removed after 2021-01-01. Please use `model.predict()` instead.\n",
      "  warnings.warn('`model.predict_proba()` is deprecated and '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.665903890160183"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_bag.score(test_[cols], test['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Public\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:425: UserWarning: `model.predict_proba()` is deprecated and will be removed after 2021-01-01. Please use `model.predict()` instead.\n",
      "  warnings.warn('`model.predict_proba()` is deprecated and '\n"
     ]
    }
   ],
   "source": [
    "test['p'] = model_bag.predict(test_[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    382\n",
       "1     55\n",
       "Name: p, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['p'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizers = ['sgd', 'rmsprop', 'adagrad', 'adadelta',\n",
    "              'adam', 'adamax', 'nadam']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sgd        | time[s]: 13.3078 | in-sample=0.6077 | out-of-sample=0.6499\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for optimizer in optimizers:\n",
    "    set_seeds()\n",
    "    model = create_model(hl=1, hu=128,\n",
    "                     dropout=True, rate=0.3,\n",
    "                     regularize=False, reg=l2(0.001),\n",
    "                     optimizer=optimizer\n",
    "                    )\n",
    "    t0 = time.time()\n",
    "    model.fit(train_[cols], train['d'],\n",
    "              epochs=50, verbose=False,\n",
    "              validation_split=0.2, shuffle=False,\n",
    "              class_weight=cw(train))\n",
    "    t1 = time.time()\n",
    "    t = t1 - t0\n",
    "    acc_tr = model.evaluate(train_[cols], train['d'], verbose=False)[1]\n",
    "    acc_te = model.evaluate(test_[cols], test['d'], verbose=False)[1]\n",
    "    out = f'{optimizer:10s} | time[s]: {t:.4f} | in-sample={acc_tr:.4f}'\n",
    "    out += f' | out-of-sample={acc_te:.4f}'\n",
    "    print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['p'] = np.where(model.predict(test_[cols]) > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['p'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(test['p'], test['d'])"
   ]
  }
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