Real Regression .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import preprocessing \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, DotProduct, WhiteKernel, ConstantKernel as C\n",
    "from sklearn import linear_model\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "FAMA_49CRSP = 'FAMA_49CRSP.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode(df, col):\n",
    "    \"\"\"\n",
    "    Takes in pandas dataframe and encodes df.col as numbers. \n",
    "    \"\"\"\n",
    "    encoder = preprocessing.LabelEncoder()\n",
    "    industry_desc_encode = encoder.fit_transform(df[col])\n",
    "    df[col] = industry_desc_encode\n",
    "    return df \n",
    "\n",
    "def process_data(filename):\n",
    "    \"\"\"\n",
    "    Reads in a CSV file and encodes text columns. \n",
    "    Removes indret_ew, indret_vw from df. \n",
    "    Returns df (X matrix) and ew_indret (Y values )\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(filename, header=0, dtype={'public_date' : str})    \n",
    "    df = df.dropna(axis=0)\n",
    "\n",
    "    ew_indret = df['indret_ew']\n",
    "    \n",
    "    df = df.drop(labels=['indret_ew', 'indret_vw'], axis=1)  \n",
    "\n",
    "    return df, ew_indret\n",
    "\n",
    "def split_data(x, y): \n",
    "    \"\"\"\n",
    "    Splits data into 0.64 Train, 0.16 dev, 0.2 Test\n",
    "    \"\"\"\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, shuffle = False)\n",
    "    x_train, x_dev, y_train, y_dev = train_test_split(x_train, y_train, test_size = 0.2, shuffle = False)\n",
    "    \n",
    "    return x_train, x_test, x_dev, y_dev, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20577, 71)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'BOOKS'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-a64ef6cc3b1d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtrain_dates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdev_dates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_dates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_dates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mElasticNet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_ElasticNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mmse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mEN_train_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mEN_dev_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdev\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mElasticNet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_dev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-22fe10fd7c7f>\u001b[0m in \u001b[0;36mtrain_ElasticNet\u001b[0;34m(alpha, l1, x_train, y_train, fix_intercept, normalize, maxiter)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain_ElasticNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfix_intercept\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mreg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mElasticNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml1_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ml1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mreg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, check_input)\u001b[0m\n\u001b[1;32m    710\u001b[0m             X, y = check_X_y(X, y, accept_sparse='csc',\n\u001b[1;32m    711\u001b[0m                              \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 712\u001b[0;31m                              copy=X_copied, multi_output=True, y_numeric=True)\n\u001b[0m\u001b[1;32m    713\u001b[0m             y = check_array(y, order='F', copy=False, dtype=X.dtype.type,\n\u001b[1;32m    714\u001b[0m                             ensure_2d=False)\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    745\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 747\u001b[0;31m                     estimator=estimator)\n\u001b[0m\u001b[1;32m    748\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    520\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 522\u001b[0;31m                 \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    523\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m     \"\"\"\n\u001b[0;32m--> 501\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'BOOKS'"
     ]
    }
   ],
   "source": [
    "df, ew_indret = process_data('FAMA_49CRSP.csv')\n",
    "print(np.asarray(df).shape)\n",
    "x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)\n",
    "train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "\n",
    "ElasticNet = train_ElasticNet(0.001, 0.01, x_train, y_train)\n",
    "mse, EN_train_pred, EN_dev_pred = dev(ElasticNet, x_train, y_train, x_dev, y_dev)\n",
    "\n",
    "graph(train_dates, y_train, EN_train_pred)\n",
    "graph(test_dates, y_test, EN_dev_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "INDUSTRY_SET = {'FIN', 'HLTH', 'TOYS', 'SHIPS', 'MACH', 'AERO', 'STEEL', 'LABEQ', 'MEALS', 'RLEST', 'CLTHS', \n",
    "                'HSHLD', 'FOOD', 'PAPER', 'PERSV', 'BOXES', 'OTHER', 'TXTLS', 'BUSSV', 'MEDEQ', 'DRUGS', \n",
    "                'HARDW', 'WHLSL', 'SODA', 'INSUR', 'ELCEQ', 'RTAIL', 'FABPR', 'TRANS', 'FUN', 'RUBBR', 'SOFTW', \n",
    "                'GUNS', 'TELCM', 'CHEMS', 'AGRIC', 'BOOKS', 'COAL', 'SMOKE', 'OIL', 'GOLD', 'BLDMT', 'CNSTR', 'AUTOS', \n",
    "                'BANKS', 'UTIL', 'BEER', 'CHIPS', 'MINES'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dev(model, x_train, y_train, x_dev, y_dev):\n",
    "    y_train_predict = model.predict(x_train)\n",
    "    train_mse = metrics.mean_squared_error(y_train, y_train_predict)\n",
    "    \n",
    "    y_dev_predict = model.predict(x_dev)\n",
    "    mse = metrics.mean_squared_error(y_dev, y_dev_predict)\n",
    "    r2 = metrics.r2_score(y_dev, y_dev_predict)\n",
    "    \n",
    "    return mse, y_train_predict, y_dev_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_GPR(x_train, y_train):\n",
    "    kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))\n",
    "    gpr = GaussianProcessRegressor(kernel=kernel)\n",
    "    gpr.fit(x_train, y_train)\n",
    "    return gpr\n",
    "\n",
    "def train_LinearRegression(x_train, y_train):\n",
    "    regr = linear_model.LinearRegression()\n",
    "    regr.fit(x_train, y_train)\n",
    "    return regr\n",
    "\n",
    "def train_ElasticNet(alpha, l1, x_train, y_train, fix_intercept=True, normalize=True, maxiter=100000):\n",
    "    reg = linear_model.ElasticNet(alpha=alpha, l1_ratio=l1)\n",
    "    reg.fit(x_train, y_train) \n",
    "    return reg\n",
    "\n",
    "def train_Ridge(alpha, x_train, y_train):\n",
    "    reg = linear_model.Ridge(alpha=alpha)\n",
    "    reg.fit(x_train, y_train) \n",
    "    return reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dates(x_train, x_dev, x_test):\n",
    "    train_dates = [datetime(year=int(x[0:4]), month=int(x[4:6]), day=int(x[6:8])) for x in x_train['public_date']] \n",
    "    dev_dates = [datetime(year=int(x[0:4]), month=int(x[4:6]), day=int(x[6:8])) for x in x_dev['public_date']]\n",
    "    test_dates = [datetime(year=int(x[0:4]), month=int(x[4:6]), day=int(x[6:8])) for x in x_test['public_date']]\n",
    "    \n",
    "    x_train = x_train.drop('public_date', axis=1)\n",
    "    x_dev = x_dev.drop('public_date', axis=1)\n",
    "    x_test = x_test.drop('public_date', axis=1)\n",
    "    \n",
    "    return train_dates, dev_dates, test_dates, x_train, x_dev, x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph(dates, y_real, y_pred=None):    \n",
    "    plt.plot(dates, y_real, color='tab:green')\n",
    "    plt.plot(dates, y_pred, color='tab:red')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/klchou/.local/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df, ew_indret = process_data('industries/AERO.csv')\n",
    "x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)\n",
    "train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "        \n",
    "regr = train_ElasticNet(0.001, 0.02, x_train, y_train)\n",
    "mse, regr_train_pred, regr_dev_pred = dev(regr, x_train, y_train, x_dev, y_dev)\n",
    "                \n",
    "graph(train_dates, y_train, regr_train_pred)\n",
    "graph(dev_dates, y_dev, regr_dev_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_Industries_LinearRegression(industry_set): \n",
    "    all_mse = []\n",
    "    for ind in industry_set:\n",
    "        df, ew_indret = process_data('industries/{}.csv'.format(ind))\n",
    "        x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)\n",
    "        train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "        \n",
    "        regr = train_LinearRegression(x_train, y_train)\n",
    "        mse, regr_train_pred, regr_dev_pred = dev(regr, x_train, y_train, x_dev, y_dev)\n",
    "        \n",
    "        print(ind)\n",
    "        \n",
    "        graph(train_dates, y_train, regr_train_pred)\n",
    "        graph(dev_dates, y_dev, regr_dev_pred)\n",
    "        \n",
    "        all_mse.append(mse)\n",
    "        \n",
    "    return np.array(all_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13168, 70)\n",
      "(4116, 70)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def test_LinearRegression(): \n",
    "\n",
    "    df, ew_indret = process_data('FAMA_49CRSP.csv')\n",
    "    df = encode(df, 'FFI49_desc')\n",
    "\n",
    "    x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)\n",
    "    train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "    print(x_train.shape)\n",
    "    print(x_test.shape)\n",
    "    regr = train_LinearRegression(x_train, y_train)\n",
    "    \n",
    "    regr_train_pred = regr.predict(x_train)\n",
    "    regr_test_pred = regr.predict(x_test)\n",
    "    \n",
    "    train_mse = metrics.mean_squared_error(y_train, regr_train_pred)\n",
    "    test_mse = metrics.mean_squared_error(y_test, regr_test_pred)\n",
    "    \n",
    "    graph(train_dates, y_train, regr_train_pred)\n",
    "    graph(test_dates, y_test, regr_test_pred)\n",
    "                \n",
    "    return train_mse, test_mse\n",
    "\n",
    "linear_train_mse, linear_test_mse = test_LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0045468478058974075\n",
      "0.007385432322696895\n"
     ]
    }
   ],
   "source": [
    "print(linear_train_mse)\n",
    "print(linear_test_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_Industries_Ridge(industry_set): \n",
    "    all_mse = []\n",
    "    average_alpha = []\n",
    "    for ind in industry_set:\n",
    "        df, ew_indret = process_data('industries/{}.csv'.format(ind))\n",
    "        x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)\n",
    "        train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "\n",
    "        min_alpha = 10\n",
    "        min_mse = 100\n",
    "        \n",
    "        for alpha in np.arange(0, 2.1, 0.01):\n",
    "            reg = train_Ridge(alpha, x_train, y_train)\n",
    "            mse, ridge_train_pred, ridge_dev_pred = dev(reg, x_train, y_train, x_dev, y_dev)\n",
    "            \n",
    "            if mse < min_mse: \n",
    "                min_alpha, min_mse = alpha, mse\n",
    "            \n",
    "        print(ind)\n",
    "        \n",
    "        reg = train_Ridge(min_alpha, x_train, y_train)\n",
    "        mse, ridge_train_pred, ridge_dev_pred = dev(reg, x_train, y_train, x_dev, y_dev)\n",
    "        \n",
    "        graph(train_dates, y_train, ridge_train_pred)\n",
    "        graph(dev_dates, y_dev, ridge_dev_pred)\n",
    "        \n",
    "        all_mse.append(mse)\n",
    "        average_alpha.append(min_alpha)\n",
    "        \n",
    "    return np.array(all_mse), np.array(average_alpha)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13168, 70)\n",
      "(4116, 70)\n",
      "99\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'train_Ridge' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-690ba33f4b80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mprev_mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mridge_dev_mse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprev_mse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mRidge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_Ridge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mridge_dev_mse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mridge_train_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mridge_dev_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdev\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRidge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_dev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_Ridge' is not defined"
     ]
    }
   ],
   "source": [
    "df, ew_indret = process_data('FAMA_49CRSP.csv')\n",
    "df = encode(df, 'FFI49_desc')\n",
    "\n",
    "x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)\n",
    "train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "prev_mse, ridge_dev_mse = 100, 99\n",
    "alpha = 0.01\n",
    "\n",
    "while prev_mse - ridge_dev_mse > 0.00000001 and alpha > 0: \n",
    "    prev_mse = ridge_dev_mse\n",
    "    print(prev_mse)\n",
    "    Ridge = train_Ridge(alpha, x_train, y_train)\n",
    "    \n",
    "    ridge_dev_mse, ridge_train_pred, ridge_dev_pred = dev(Ridge, x_train, y_train, x_dev, y_dev)\n",
    "    print(ridge_dev_mse)\n",
    "    ridge_train_mse = metrics.mean_squared_error(y_train, ridge_train_pred)\n",
    "    alpha += 0.01\n",
    "\n",
    "graph(train_dates, y_train, ridge_train_pred)\n",
    "graph(dev_dates, y_dev, ridge_dev_pred)\n",
    "\n",
    "print(ridge_train_mse)\n",
    "print(ridge_dev_mse)\n",
    "print(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_Ridge' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-601c974a167c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mRidge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_Ridge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.479999999999991\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mRidge_y_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRidge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mridge_test_mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRidge_y_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mridge_test_mse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_Ridge' is not defined"
     ]
    }
   ],
   "source": [
    "Ridge = train_Ridge(2.479999999999991, x_train, y_train)\n",
    "Ridge_y_pred = Ridge.predict(x_test)\n",
    "ridge_test_mse = metrics.mean_squared_error(y_test, Ridge_y_pred)\n",
    "print(ridge_test_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-a51dc1ec3bf5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtrain_dates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdev_dates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_dates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_dates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_GPR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mgpr_train_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr_dev_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdev\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_dev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-21-22fe10fd7c7f>\u001b[0m in \u001b[0;36mtrain_GPR\u001b[0;34m(x_train, y_train)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mRBF\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGaussianProcessRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    230\u001b[0m             optima = [(self._constrained_optimization(obj_func,\n\u001b[1;32m    231\u001b[0m                                                       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m                                                       self.kernel_.bounds))]\n\u001b[0m\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0;31m# Additional runs are performed from log-uniform chosen initial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36m_constrained_optimization\u001b[0;34m(self, obj_func, initial_theta, bounds)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"fmin_l_bfgs_b\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mtheta_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvergence_dict\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 476\u001b[0;31m                 \u001b[0mfmin_l_bfgs_b\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_theta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    477\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mconvergence_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"warnflag\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m                 warnings.warn(\"fmin_l_bfgs_b terminated abnormally with the \"\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36mfmin_l_bfgs_b\u001b[0;34m(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m     res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,\n\u001b[0;32m--> 199\u001b[0;31m                            **opts)\n\u001b[0m\u001b[1;32m    200\u001b[0m     d = {'grad': res['jac'],\n\u001b[1;32m    201\u001b[0m          \u001b[0;34m'task'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'message'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36m_minimize_lbfgsb\u001b[0;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;31m# until the completion of the current minimization iteration.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0;31m# Overwrite f and g:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m             \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    336\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mtask_str\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb'NEW_X'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m             \u001b[0;31m# new iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36mfunc_and_grad\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    283\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m             \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjac\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[0;34m(*wrapper_args)\u001b[0m\n\u001b[1;32m    291\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjac\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36mobj_func\u001b[0;34m(theta, eval_gradient)\u001b[0m\n\u001b[1;32m    222\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0meval_gradient\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m                     lml, grad = self.log_marginal_likelihood(\n\u001b[0;32m--> 224\u001b[0;31m                         theta, eval_gradient=True)\n\u001b[0m\u001b[1;32m    225\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py\u001b[0m in \u001b[0;36mlog_marginal_likelihood\u001b[0;34m(self, theta, eval_gradient)\u001b[0m\n\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0meval_gradient\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m             \u001b[0mK\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mK_gradient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_train_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_gradient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0mK\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_train_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/gaussian_process/kernels.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, X, Y, eval_gradient)\u001b[0m\n\u001b[1;32m    762\u001b[0m         \"\"\"\n\u001b[1;32m    763\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0meval_gradient\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m             \u001b[0mK1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mK1_gradient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_gradient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m             \u001b[0mK2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mK2_gradient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_gradient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m             return K1 * K2, np.dstack((K1_gradient * K2[:, :, np.newaxis],\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/gaussian_process/kernels.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, X, Y, eval_gradient)\u001b[0m\n\u001b[1;32m   1017\u001b[0m                 return (K, np.full((X.shape[0], X.shape[0], 1),\n\u001b[1;32m   1018\u001b[0m                                    \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1019\u001b[0;31m                                    dtype=np.array(self.constant_value).dtype))\n\u001b[0m\u001b[1;32m   1020\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36mfull\u001b[0;34m(shape, fill_value, dtype, order)\u001b[0m\n\u001b[1;32m    308\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m     \u001b[0mmultiarray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopyto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'unsafe'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    311\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "df, ew_indret = process_data('FAMA_49CRSP.csv')\n",
    "df = encode(df, 'FFI49_desc')\n",
    "\n",
    "x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)\n",
    "train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "    \n",
    "gpr = train_GPR(x_train, y_train)\n",
    "gpr_train_pred, gpr_dev_pred = dev(gpr, x_train, y_train, x_dev, y_dev)\n",
    "\n",
    "graph(train_dates, y_train, gpr_train_pred)\n",
    "graph(dev_dates, y_dev, gpr_dev_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_Industries_EN(industry_set): \n",
    "    all_mse = []\n",
    "    for ind in industry_set:\n",
    "        df, ew_indret = process_data('industries/{}.csv'.format(ind))\n",
    "        x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)\n",
    "        train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "\n",
    "        ElasticNet = train_ElasticNet(0.001, 0.001, x_train, y_train)\n",
    "        mse, EN_train_pred, EN_dev_pred = dev(ElasticNet, x_train, y_train, x_dev, y_dev)\n",
    "            \n",
    "        print(ind)\n",
    "        \n",
    "        graph(train_dates, y_train, EN_train_pred)\n",
    "        graph(dev_dates, y_dev, EN_dev_pred)\n",
    "        \n",
    "        all_mse.append(mse)\n",
    "        \n",
    "    return np.array(all_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13168, 70)\n",
      "(4116, 70)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'train_ElasticNet' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-b32d5f3980d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mElasticNet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_ElasticNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mmse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mEN_train_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mEN_dev_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdev\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mElasticNet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_dev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_ElasticNet' is not defined"
     ]
    }
   ],
   "source": [
    "df, ew_indret = process_data('FAMA_49CRSP.csv')\n",
    "df = encode(df, 'FFI49_desc')\n",
    "\n",
    "x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)\n",
    "train_dates, dev_dates, test_dates, x_train, x_dev, x_test = get_dates(x_train, x_dev, x_test)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "\n",
    "ElasticNet = train_ElasticNet(0.001, 0.01, x_train, y_train)\n",
    "mse, EN_train_pred, EN_dev_pred = dev(ElasticNet, x_train, y_train, x_dev, y_dev)\n",
    "\n",
    "graph(train_dates, y_train, EN_train_pred)\n",
    "graph(dev_dates, y_dev, EN_dev_pred)\n",
    "\n",
    "EN_train_pred = ElasticNet.predict(x_train)\n",
    "EN_train_mse = metrics.mean_squared_error(y_train, EN_train_pred)\n",
    "\n",
    "EN_test_pred = ElasticNet.predict(x_test)\n",
    "EN_test_mse = metrics.mean_squared_error(y_test, EN_test_pred)\n",
    "\n",
    "print(EN_train_mse)\n",
    "print(EN_test_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# citation: https://medium.com/@alexrachnog/neural-networks-for-algorithmic-trading-part-one-simple-time-series-forecasting-f992daa1045a\n",
    "\n",
    "def time_window(x_train, y_train, window):\n",
    "    X, Y = [], []\n",
    "    for i in range(0, x_train.shape[0] - window):\n",
    "        x_i = x_train[i:i + window]\n",
    "        y_i = y_train[i + window]\n",
    "        \n",
    "        X.append(x_i)\n",
    "        Y.append(y_i)\n",
    "    return np.dstack(X).T, np.dstack(Y).T.reshape((-1))\n",
    "\n",
    "df, ew_indret = process_data('industries/AERO.csv')\n",
    "x_train, x_test, x_dev, y_dev, y_train, y_test = split_data(df, ew_indret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train = time_window(x_train, y_train, window=12)\n",
    "x_dev, y_dev = time_window(np.asarray(x_dev), np.asarray(y_dev), window=12)\n",
    "\n",
    "x_train = x_train.reshape(x_train.shape[0], -1)\n",
    "x_dev = x_dev.reshape(x_dev.shape[0], -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: -277.06495099365503\tMSE: 1.1201554534917209\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regr = train_LinearRegression(x_train, y_train)\n",
    "regr_train_pred, regr_dev_pred = dev(regr, x_train, y_train, x_dev, y_dev)\n",
    "\n",
    "graph(train_dates[12:], y_train, regr_train_pred)\n",
    "graph(dev_dates[12:], y_dev, regr_dev_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: -0.6218000685517087\tMSE: 0.006533251259353777\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = train_Ridge(400, x_train, y_train)\n",
    "ridge_train_pred, ridge_dev_pred = dev(reg, x_train, y_train, x_dev, y_dev)\n",
    "\n",
    "graph(train_dates[12:], y_train, ridge_train_pred)\n",
    "graph(dev_dates[12:], y_dev, ridge_dev_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: -0.049920724814840556\tMSE: 0.004229495380243589\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gpr = train_GPR(x_train, y_train)\n",
    "gpr_train_pred, gpr_dev_pred = dev(gpr, x_train, y_train, x_dev, y_dev)\n",
    "\n",
    "graph(train_dates[12:], y_train, gpr_train_pred)\n",
    "graph(dev_dates[12:], y_dev, gpr_dev_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "314/314 [==============================] - 1s 2ms/step - loss: 982253551785.5796\n",
      "Epoch 2/100\n",
      "314/314 [==============================] - 0s 443us/step - loss: 38199869357.2484\n",
      "Epoch 3/100\n",
      "314/314 [==============================] - 0s 492us/step - loss: 8353590020.2803\n",
      "Epoch 4/100\n",
      "314/314 [==============================] - 0s 485us/step - loss: 1028372538.4968\n",
      "Epoch 5/100\n",
      "314/314 [==============================] - 0s 491us/step - loss: 206498705.4554\n",
      "Epoch 6/100\n",
      "314/314 [==============================] - 0s 601us/step - loss: 34941297.8424\n",
      "Epoch 7/100\n",
      "314/314 [==============================] - 0s 540us/step - loss: 7756417.0060\n",
      "Epoch 8/100\n",
      "314/314 [==============================] - 0s 458us/step - loss: 1513040.5987\n",
      "Epoch 9/100\n",
      "314/314 [==============================] - 0s 539us/step - loss: 372134.2018\n",
      "Epoch 10/100\n",
      "314/314 [==============================] - 0s 499us/step - loss: 202542.8934\n",
      "Epoch 11/100\n",
      "314/314 [==============================] - 0s 502us/step - loss: 190515.6202\n",
      "Epoch 12/100\n",
      "314/314 [==============================] - 0s 510us/step - loss: 267008.4845\n",
      "Epoch 13/100\n",
      "314/314 [==============================] - 0s 447us/step - loss: 307147.5348\n",
      "Epoch 14/100\n",
      "314/314 [==============================] - 0s 487us/step - loss: 196784.1790\n",
      "Epoch 15/100\n",
      "314/314 [==============================] - 0s 489us/step - loss: 166632.6017\n",
      "Epoch 16/100\n",
      "314/314 [==============================] - 0s 471us/step - loss: 119238.5081\n",
      "Epoch 17/100\n",
      "314/314 [==============================] - 0s 453us/step - loss: 122518.2202\n",
      "Epoch 18/100\n",
      "314/314 [==============================] - 0s 458us/step - loss: 192228.9538\n",
      "Epoch 19/100\n",
      "314/314 [==============================] - 0s 471us/step - loss: 104968.6715\n",
      "Epoch 20/100\n",
      "314/314 [==============================] - 0s 493us/step - loss: 147924.8007\n",
      "Epoch 21/100\n",
      "314/314 [==============================] - 0s 427us/step - loss: 125075.5502\n",
      "Epoch 22/100\n",
      "314/314 [==============================] - 0s 459us/step - loss: 128234.5232\n",
      "Epoch 23/100\n",
      "314/314 [==============================] - 0s 491us/step - loss: 133838.2590\n",
      "Epoch 24/100\n",
      "314/314 [==============================] - 0s 500us/step - loss: 95328.6911\n",
      "Epoch 25/100\n",
      "314/314 [==============================] - 0s 439us/step - loss: 148713.5439\n",
      "Epoch 26/100\n",
      "314/314 [==============================] - 0s 405us/step - loss: 150607.1035\n",
      "Epoch 27/100\n",
      "314/314 [==============================] - 0s 500us/step - loss: 158390.0298\n",
      "Epoch 28/100\n",
      "314/314 [==============================] - 0s 514us/step - loss: 179231.7247\n",
      "Epoch 29/100\n",
      "314/314 [==============================] - 0s 505us/step - loss: 244052.6542\n",
      "Epoch 30/100\n",
      "314/314 [==============================] - 0s 611us/step - loss: 106176.9888\n",
      "Epoch 31/100\n",
      "314/314 [==============================] - 0s 481us/step - loss: 316083.1712\n",
      "Epoch 32/100\n",
      "314/314 [==============================] - 0s 417us/step - loss: 153049.0213\n",
      "Epoch 33/100\n",
      "314/314 [==============================] - 0s 479us/step - loss: 174759.1697\n",
      "Epoch 34/100\n",
      "314/314 [==============================] - 0s 404us/step - loss: 235898.6827\n",
      "Epoch 35/100\n",
      "314/314 [==============================] - 0s 439us/step - loss: 157562.4836\n",
      "Epoch 36/100\n",
      "314/314 [==============================] - 0s 472us/step - loss: 74951.2974\n",
      "Epoch 37/100\n",
      "314/314 [==============================] - 0s 413us/step - loss: 86535.1628\n",
      "Epoch 38/100\n",
      "314/314 [==============================] - 0s 434us/step - loss: 187183.2576\n",
      "Epoch 39/100\n",
      "314/314 [==============================] - 0s 424us/step - loss: 145806.9444\n",
      "Epoch 40/100\n",
      "314/314 [==============================] - 0s 504us/step - loss: 149913.1216\n",
      "Epoch 41/100\n",
      "314/314 [==============================] - 0s 489us/step - loss: 142266.0425\n",
      "Epoch 42/100\n",
      "314/314 [==============================] - 0s 453us/step - loss: 205591.6265\n",
      "Epoch 43/100\n",
      "314/314 [==============================] - 0s 505us/step - loss: 170952.2133\n",
      "Epoch 44/100\n",
      "314/314 [==============================] - 0s 494us/step - loss: 65968.2140\n",
      "Epoch 45/100\n",
      "314/314 [==============================] - 0s 478us/step - loss: 67251.3201\n",
      "Epoch 46/100\n",
      "314/314 [==============================] - 0s 507us/step - loss: 163027.1410\n",
      "Epoch 47/100\n",
      "314/314 [==============================] - 0s 506us/step - loss: 290141.7857\n",
      "Epoch 48/100\n",
      "314/314 [==============================] - 0s 437us/step - loss: 105382.2593\n",
      "Epoch 49/100\n",
      "314/314 [==============================] - 0s 468us/step - loss: 166542.2949\n",
      "Epoch 50/100\n",
      "314/314 [==============================] - 0s 564us/step - loss: 178412.8035\n",
      "Epoch 51/100\n",
      "314/314 [==============================] - 0s 493us/step - loss: 86576.1139\n",
      "Epoch 52/100\n",
      "314/314 [==============================] - 0s 455us/step - loss: 131254.3423\n",
      "Epoch 53/100\n",
      "314/314 [==============================] - 0s 486us/step - loss: 125354.1701\n",
      "Epoch 54/100\n",
      "314/314 [==============================] - 0s 529us/step - loss: 106536.2812\n",
      "Epoch 55/100\n",
      "314/314 [==============================] - 0s 413us/step - loss: 88065.7773\n",
      "Epoch 56/100\n",
      "314/314 [==============================] - 0s 408us/step - loss: 150566.3976\n",
      "Epoch 57/100\n",
      "314/314 [==============================] - 0s 406us/step - loss: 149159.7021\n",
      "Epoch 58/100\n",
      "314/314 [==============================] - 0s 504us/step - loss: 49813.3547\n",
      "Epoch 59/100\n",
      "314/314 [==============================] - 0s 403us/step - loss: 92265.6997\n",
      "Epoch 60/100\n",
      "314/314 [==============================] - 0s 462us/step - loss: 295007.1200\n",
      "Epoch 61/100\n",
      "314/314 [==============================] - 0s 412us/step - loss: 418109.5121\n",
      "Epoch 62/100\n",
      "314/314 [==============================] - 0s 401us/step - loss: 399668.3414\n",
      "Epoch 63/100\n",
      "314/314 [==============================] - 0s 459us/step - loss: 395782.1354\n",
      "Epoch 64/100\n",
      "314/314 [==============================] - 0s 452us/step - loss: 253155.4769\n",
      "Epoch 65/100\n",
      "314/314 [==============================] - 0s 511us/step - loss: 185435.7453\n",
      "Epoch 66/100\n",
      "314/314 [==============================] - 0s 539us/step - loss: 107049.9528\n",
      "Epoch 67/100\n",
      "314/314 [==============================] - 0s 531us/step - loss: 58275.3771\n",
      "Epoch 68/100\n",
      "314/314 [==============================] - 0s 466us/step - loss: 52789.6278\n",
      "Epoch 69/100\n",
      "314/314 [==============================] - 0s 472us/step - loss: 138646.2677\n",
      "Epoch 70/100\n",
      "314/314 [==============================] - 0s 514us/step - loss: 149930.5429\n",
      "Epoch 71/100\n",
      "314/314 [==============================] - 0s 440us/step - loss: 157494.9015\n",
      "Epoch 72/100\n",
      "314/314 [==============================] - 0s 443us/step - loss: 160297.3817\n",
      "Epoch 73/100\n",
      "314/314 [==============================] - 0s 545us/step - loss: 239100.7514\n",
      "Epoch 74/100\n",
      "314/314 [==============================] - 0s 382us/step - loss: 230739.3855\n",
      "Epoch 75/100\n",
      "314/314 [==============================] - 0s 467us/step - loss: 329992.9932\n",
      "Epoch 76/100\n",
      "314/314 [==============================] - 0s 484us/step - loss: 45298.5453\n",
      "Epoch 77/100\n",
      "314/314 [==============================] - 0s 497us/step - loss: 772649.3285\n",
      "Epoch 78/100\n",
      "314/314 [==============================] - 0s 440us/step - loss: 588890.6825\n",
      "Epoch 79/100\n",
      "314/314 [==============================] - 0s 496us/step - loss: 9467458.6353\n",
      "Epoch 80/100\n",
      "314/314 [==============================] - 0s 512us/step - loss: 2195704046.8790\n",
      "Epoch 81/100\n",
      "314/314 [==============================] - 0s 452us/step - loss: 11004075518.1242\n",
      "Epoch 82/100\n",
      "314/314 [==============================] - 0s 467us/step - loss: 1357834881.7643\n",
      "Epoch 83/100\n",
      "314/314 [==============================] - 0s 436us/step - loss: 195637897.1146\n",
      "Epoch 84/100\n",
      "314/314 [==============================] - 0s 503us/step - loss: 41855474.3782\n",
      "Epoch 85/100\n",
      "314/314 [==============================] - 0s 447us/step - loss: 8001102.5539\n",
      "Epoch 86/100\n",
      "314/314 [==============================] - 0s 433us/step - loss: 203236.0290\n",
      "Epoch 87/100\n",
      "314/314 [==============================] - 0s 527us/step - loss: 922535.7409\n",
      "Epoch 88/100\n",
      "314/314 [==============================] - 0s 499us/step - loss: 2437379.2700\n",
      "Epoch 89/100\n",
      "314/314 [==============================] - 0s 519us/step - loss: 5734921.6539\n",
      "Epoch 90/100\n",
      "314/314 [==============================] - 0s 447us/step - loss: 8835550.3778\n",
      "Epoch 91/100\n",
      "314/314 [==============================] - 0s 420us/step - loss: 394376022.3185\n",
      "Epoch 92/100\n",
      "314/314 [==============================] - 0s 474us/step - loss: 8275648117.4076\n",
      "Epoch 93/100\n",
      "314/314 [==============================] - 0s 451us/step - loss: 1012141248.3057\n",
      "Epoch 94/100\n",
      "314/314 [==============================] - 0s 449us/step - loss: 173933794.6847\n",
      "Epoch 95/100\n",
      "314/314 [==============================] - 0s 535us/step - loss: 18825058.4562\n",
      "Epoch 96/100\n",
      "314/314 [==============================] - 0s 435us/step - loss: 10754219.7420\n",
      "Epoch 97/100\n",
      "314/314 [==============================] - 0s 500us/step - loss: 5459916.3619\n",
      "Epoch 98/100\n",
      "314/314 [==============================] - 0s 391us/step - loss: 5034217.9098\n",
      "Epoch 99/100\n",
      "314/314 [==============================] - 0s 451us/step - loss: 5070158.6555\n",
      "Epoch 100/100\n",
      "314/314 [==============================] - 0s 391us/step - loss: 3154769.9376\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f7e4065acc0>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten\n",
    "from keras.activations import linear \n",
    "from keras.optimizers import adam\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(500, activation='relu', input_dim=x_train.shape[1]))\n",
    "model.add(Dense(250, activation='relu'))\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "model.fit(x_train, y_train, epochs=100, batch_size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dev_pred = model.predict(x_dev)\n",
    "y_train_pred = model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XPf+gAdZyIn+yW57d8Kx47aHVD+GP7/1R/J1yUqK8BOER/1J5/tzB42sfx0ubX9KOW9DFr05qKUf81Qc2+aq/XPFLfOGpL2DZtmWhbVYfsH/f8XfP3+KBVugNdGW7AABXLLoCVz12Vcn27UnvKbmNSspJ1Zz4P7LmETHprxIsXL0QX3/u672qIVUPhOX5eyJ/xx/5D/Qs30qI/yYA+0t/Tyq8NmCQ0NByiTKy5x9miYRF/rpsn6BeQFD7onj+5eTG02flB6DjOp7IN+NkkLSTSFgJz2tRI/+cm8Mrm1/B8ORwfO24rwHwzncgQZP32dfvIz6riDd9HxJxEuugz6mRvzouRO2k3gztj1ZeKjU3pE/in0sh7daWKN70wk247dXbKrY/Op+DTfxLLeZC9/lgjfxfA3AwY2waYywB4FIAj1Rgv32GBFuOrgnZ8w87+bpMG4IE4PlNz2Ph6oUAehf5c8612T7qBVTOgC99Vn7I5NycJ8JMO+m8+Nu9FH/pgs+5OTTGGsWD1hP5u8XIv1Lir8uIclwHndlOAMGVWoPSUlXxf2fnO1iyZYmI/Gm/UdseZTEflbST9kX+Lnfx4d4Pe72vapN1spEmS5L41VoPp1xKFXajVGpd5F/3nj/nPAfgBgB/BfAugPs4528zxm5ljJ0PAIyx4xhjGwFcDOC/GWNvl3vcMETUqxF/eq+U5x/WnaN9PPT+Q/jfv/8vgN7X9pEXCqfoXBWWsMGkUgjxl37iHM8JQeacI5VLIRlTIn+3d7aPwx3YzBYPGY/nX4iyfbZPBQd8qc0k0oGRP3n+ytoDau/wt2//Fre9elsx8s9496euykQ0xhoBoE+5/j25Hl9EfMeyO3D+w+djc+fmXu+vv1j0wSKccf8Z4lymcikcM/8Y3LnizpKfpe/XX+KvjitVi1J5/pRQIVugxGCwfcA5X8Q5n845P5Bz/u+F177FOX+k8O/XOOeTOOfNnPNRnPOZlThuEHRSwzz/UqmeYdk+cqRD2wWVd9YRlOdfKtWzN6tt6WwfOfKniSYNdgPidlEA5SJUsu0k37RySWfHdRCzYtrBdXrQVNL20d1kGSeDzkxe/NVIXT1mqciftqEa7Or+gmyLplgTgL7bPi53PW2jLKOgh9lAsK5jHbZ1bysuT1j4793vlc7g7m/xP/XeUzHv0Xn9su8wSs3wbY43e7eTPf9BkOpZc0QZyC01yStKqqe8P1mUokT+slhS9FlqwFfOqimFGPRmiudfeJ0i2Ci2zxWLrsCx848V28gXco7nYDMbDPmeTNCAb3/ZPnQcEulXt7yKe967x7dNUOSvI+fmhO2jzvAOEq+GWL5MRm9tH865+C3kfdP3KTUprZrQuaOHOl0f8kzWIIT4V6gch0pHpgMrd6/sl30TPbken8UVGvk7aREU6K6/urd9apGwHyTqDN+wFXp01oY8uSlsv/SeJ/K3AlI9pch/6daluOmFmwL3q0Ltku0lhzueQVggL1pJOym2yTgZX+39d3a+49m3HO04rgPb0ts+sucfNdtnw74NuGHxDb7Vj8RnNdFSV7ZLbP/42sfxgyU/CPycKv5BpbTpQRs18qf99jbylx+MtG/qxQDBNlMluPaJa/HVZ74aeXv12qFzF0XQ6QHRH5F/X8ZZesv6jvWY88c5ePSDRz2vhxVfTDtpNMUL4q8ZGB7oAd9BuYB7WNQuZwL1NdVTFnraLuokJt1AbFAJZXkw6bN//WzgPnWQaMgT3UiEHdcREWXCTvg8/1KLyMiZUKrnL0dG9ADy2T4h5+eN7W/g2Y3PYsO+DZg+YrrvfV35BZ0vzjn3PPiCbB9dT8BxHU+qpzpxTQftt7fiL4s7ib8cwQY9BCvBki1LtK8HBS/iYR5S+iQIXe+mUqzvWB/6/lWLrkJHpgMLL1zY52NQyZilW5fi/APPF6+TPuiuo4ybQcJKIMaKK/V5PP8Btn2GnPjn3BxiLO9Rh2UphNXskPerE/8okb8u1TNohm/QfnQ+O0E3mbwN7S/jZoTQqJ6/HPmXnOTFnaLtUxBa+cHYF9unlD2ja9OutL8EQ9pJCysmbL+6tuR4TkT+DneQdtKwmAWXu8GRf0EQOzId2veDkG0dOl+y4A+E7aNWoKTrU0T+rjfyj0J/2j7r9q0LfX/F9tKT80qxtmMtAGDqsKme10NTwl0XtmXnCzdqgg9j+/QDpWwfGqAMTfWMOOCrE/+wH5X2p438AwZ8w/YTBN1saqonkL+JSWAaYg2+PP9SC5vIXVhh+yDE9nH6IP7K4Pa6jnX542na1JH2C65v0eyAyF8r/m7OM77Sme0U57GU7dObQXlAL/7y9dOftk8Q8nfXDfSrtk8U+jPVc11HXvx12X2V4D9e+w/Mf3c+gOLAPiHXrFLvVxf5AI2q9gLe62OgbZ9BKf4lI38rBpvZ0Tz/EgO+OTfnq11TKmIHoJ3h6xOmkG5hqahBrc0vfybrZkXBspENI70Dvm6muKRhqcJuPBdq+/Rlhq8uQl/fsR6f+NMn8Ks3fqVtE5VgkJHFf296b6Dnr7V9uIOebPHzXdku8XupYnjPe/cg5+aKpSx62ZWX20kTveR9yO2oFvIxPd/X8Q749inydzK47M+X4Y7X76hEUwEUbR+yNCvNH975g/i3byKmG3xdc85hwYJt2Z57Jmh7IF999sl1T1ak3aUYlOIfFpnIkX9fa/voJhpFtX3CIv+gAV8dLnfx3MbnsKNnh/Z9utkoC0fef8bJYGfPTgDAqMZRHvGnyp466HWP519I9aTD6DKh+mT7SBESlap4a/tboZG/HJVRpEmL0f/lg79oj6219VwnUuR/38r78IMlP8CC9xZoe5vburbhlhdv8QhoV7YL5zx4jihDIUf2NRn5SzaNnLoL9F38/77z72J+TG/54ZIf4q9r/wog/9ve+PyNnnvgB0t+4EtQqCSqHoSJuctdMMY8K/WVivzn/Xlerwbhy2FQi79OPMmjjiz+JSJ/QGNrBER/O3p24KdLfwrAG5EH1vYJiWIyTgbXL74e5z54Lo6+62is3OVNc6ObTReZZJwMdqYK4t8wyjPRKeNkSnr92klehUspqLxD1MhfN4BGYtQYa9SeE/LZ5bELiqg37Nvg2Taq5y9H5N3ZbvGAlsWfsnK2dW/T7m/ptqV4ePXDngHJ5e3LsalzE/7rjf/K709KkxS/mXSu+nPANwhPb0T6vqInJ03ei4qYG1Dmw+yRNY/gpc0vAQC++/J38ZcP/oJXt74q3r9v1X14dM2j+MnSn/jOXTkz5cU+KO06240dPTu8Kd4a29ZilmeNbrmqrPH8+wHdTUT02vOPEPmrkW3QgiA3v3gzHnw/X1UzyoBv2MOJLuyUk4LDHfzu7d953tfNKJQHfHf07EBzvDnv+Qfk+auI7r7O81dSPSkTiNrgmRgXYo2oueRAUfyb4k3a34xS/WQrjc6POiiuevJh2T6UKdWV7dJG/vSwkecC6FL55O9LPa4RDSMA6CN/eR93rrgTX3zqi742yqzctdJXCiKVSwX2Ckshf58dPTuwcd9GnPXAWfhg7wf5dmpsn6DeouM6uH/V/eI3lNdTOOL3R+CJtU9EbpfLXXRluzzp2jrmvzsfv3v7d5j/zny0d7cXv1cv5skQ9Ht/6egvAfDOfTntvtNCgxoODotZiFvxyJF/NRnU4l/K9gkt7BZS26eU+Af9qLt6ilkpctmFvmT7qFHN+n3edDdd/XD5AtzZsxOjG0cDyJe6kD3toPo4IsVP6gE43EGMxUS2D31WFu8+ef7STUJi1BRrCo38deKvDgKqv6euLRwcndlOtCXzdVkyTkbr+dNAeVeuOAtXt66DfAx5rEVup7xv9Xd/YdMLvjbKXPToRTj/4fM9r33xqS/itPtOC/1cELJIXv3Y1fjhqz/Elq4torideEhJ5zIoi2fhmoW49eVbxfeUxRgAfrz0x9Hble0GBxcPbHn9aZpJK/Ofy/8Tc++f6/m8jp5cD776zFex6INF2vcAoCWeX/SIftPVe1YDCM/bd7kLBoaYFfOUNycGRXmHWiPoJgLyJzxuxSNH/roo2FXWpFGzUIL2K99UliWJfyHCVBcakfcje/fqvgB/rrOI/DXdzIybt31GNYwCABww/AAcNuowWMzy5PmrqBGfSPXUZPvIItmb8g66iUNy5K8d8KXIX7LSFqxcgMXrF/cp8gfydfmHJYcByAcTtG9P5G/5I395f7obnqJxmvwjZ/vorLq+snRbfv3qKEXXVNTAYtXuVZ6/M24Gb2x/w2M1yhPTwvbV3uMV/61dWyO3iybciRItseLkxDGNpUvAB0X+//Haf+DJdU9qZ4bT7yPEP6DyrtwuQtg+zNbm+ZvIvx+gm0gnFORR28wOjHCB4NRAwH9DqZUNg25euU6Lboav+jm5/Uk7iQWfWICpbVMB+KMYdXKR6P04/loiNOA7qjEv/lfMuAJ3f/xuJKxEeOTvaiJ/V5/to4pgOdk+JCxBazBQ5D+5dbJ47bmNz+ErT3/FM9FLd+ww8R+RHCG20Xn+BIkKA9PbPtJrJHb0ENKmehY+R8XiyqEvefXqtbWj22sfZZ0srlx0pSdqD6pBpH4HdRlNDl6yZ0PQQjl0PuXIf0xTBPEPiPypHDi5AW/vfFtcx/T7NMYaYTPbd/3I14M224dSPU15h+oQloqWdbOIWXmbIuzkR5klLO+TXouxWOATXb5Bonj+8t9xO46Zo2bi80d8HoA+ipEvbroo6WKTF47OOHnPn6wH+RhqlC63WfWk5QFfMclLE/n7PP9e2j4k7i53A7N94lYcPzr1R/i/x/5fz3s6i053PJV9mX0e24d+L0+PpnCd0XlviDXobR/JHqG1fsnr183wpTZRtKnCOY/s5+sE7/3d7+Oh9x8K/oxybalWmS5XP6ignurLq7YPkLeoKFc/DLoWReQvlSUhCzOMoMifBH57z3bc9c5duPTPl4qeE/VcGmONWrdA7vH4bB/ks31sZiPHc1jevtyzRkJvBsz7g8Ep/iVsn0h5/iEDvr7Iv1AhEwhfJ0DuAsuRP9kHYQO+dKHT53RZIPIi4kJIuN++6sp1oSPTISJ/+RhyVU8AOOHuEzzfU25nzs3X8/cM+EIj/k4vIn9NPj6Jf9Akr4ybQUOsASMbRmLedG9lx1JiH3QDppyUEN+MU7TC5EiP/k0C2xhr1Hq68jVEkT+dn7DIX87Cyrk5LFy9EJs6N+FPq/+E0+47zZfhpUMneJ9+5NP49kvfFn+r13OpDCNdbyIo8ldttu09+uVZo9TnUSN/uSLr2MaxJT8f1Eb6Hdu720UWFvWk5cmQMSvm0wz5oae1fZAf8HVcx3POAWP79Ath3imJPwPzefcyakVNGV+ev1vM89ddIDpkOyJKtg8NLpL46yK6sMwRbcZJwdaQj6EOXsuoA76eyL8wJkFCog74Rq3to+see8Q/oLfWaOftBbmkg7w/uS1h78vQIGLGzXgsM7GvwvmgAV+f+GuKeZGY0DXq+c2oWqY0NkN0Zjpx84s340/v/0ks9r68fXlg24koJaHVc1KqV6HbZ9BxSqV2HjsuXy02qOegO0ZPrgc3vXCTZ5yrHNuHfou0kxbtoIheFn+d7RMm/lRfimyfiS3epc2/9/L38Lf1f9O2qS9jNb1lUIp/WOQvPH8rH/kHnWTdYJ28D3VbErdSBeMIzwzfwoBvWCYKdZ9pfEAX0ck543TT6UpO002kLmSSsBOhef7qLNacm4PrusJGA4oPLDnie33b63hn5zvF79lL22dfep84vvpgogcnib5an199WESZ4UvQoGzWyfpKYQNFcaaoVRV/Odvn0TWP4r6V94n9CPHPpcRDRu2tyQ8aGijdl9mH8c3jAeR7EaVEIkjwZORz8OiaR3H/qvtDt6fgQUYV74fefwgr2leULOfw9eO+DqB4TX79ua/ja89+Tbvtvmz+OljbsRaPrHkEL25+UbwXxfbpynVhS+cWnHD3CZ5eUyqXwtgmb8+BAg76vRtjjZ58faKU7UN5/jmew4TmCZ73M24Gz258Fjqq0SsYdOIvWwOlsn2A0sv+6aJgne3jEf+QUtJEb/P8hfiHRP66CTm6yJ+iGVUoE3YidIavPGMXyIubPGnWCMOJAAAgAElEQVQO0Kd6LmtfhuXty0VefJj46/L8w2wfssPUiJ8oldoZ1paknUTMiiHtpD3jJQSdb9kXlo8n5/n/2wv/hu+98j2f+HfnutEUa0LCSvh6rPKxaNC1M9spBlG3dm/1CLf8u4nrJEJuu/ygpcHPH58anIJJEwRlVKvojtfvwAOrHggtTNdgN2B4cjiAoog+9uFjeHzt43h/9/u+7dVoXP7ulJkVRne2G6v3rEZXtgvv78nvn3OOtJP2JAswMPFAFwO+dqMna4fOb0nbh1lihm/YHBUVI/59QL5hQgu7Fb56lMqVKr7IX/K0gyJ/tdqjtp5/SJ6/avvofFmdH63LWhI58MpKZ5TtEzjJq1D0TX6gOFxf2E0X8ZFQ59wctndvF2VyZcIGfHW2D/nyQZkxUWyfmaNm4q1r3vIV7Ypb8fw5cTNiP7oHLNEQa0DOzfnKYMgParV2f0e6A22JNiTtpK/HKh+LvPJ9mX3iO2zr2ubZRv5uujRUQH9PyJ/b3rMd04ZNw+xxs33bEbrIX61B1JXtQtpJhy7Y3hxvFr0e1TZ6YNUDvu1JaHX7VH87Hd3ZbvHgItElUZ7cVhT/Yclh4ppTbR/6bSgYC4v8Oecizz/n5rT3bKD4VyETaNCJvyf1SuMtiwHfgvCVLF4WMAlIRs5miVkx7Q+3J+VNxexz5B9i++gyR3S2DwmCLvKXxy9Usm7WU3RKTvUUk7zI89eIPz3AcjyH6568Dtc9eV2gB6+N/Lk/ehrXPA5ASORfItKn7C/Afy3ErJiwwnTir7adHkDqzF5drRk6P5RVRMehNlrM8lxnJP6d2U6x3ZauLZ72vLPzHdFOIf7KdUKTzNRzQOzo2YHRjaMDZ8/SNiqysGXdLDJuBiknpRVqStFsSbQI8e/Mdnp+G7ksx4ubXsRPlv4kcC4BkH+Q3HXuXfjmnG8GbtOd6xZtp3pQdM/s37o/AOCCAy9AW6JNvC/36mTbR3d+A/P8C5+jfX312GLtHl1RQsBE/n0iaSfxz8f8M4Dw2j40QLk7tdu3GIjL3dCa9rr0QXFR2HHtcdXI3yP+AZ6/J/KPYvto6sSERf7yQi8AxEzEoIHw7mw3/uXZfym2r1DCQV7DV2T7aDJCLKvYBaZibdu6tnm2UWv7yGvb6rrO45ry4k8DvipRIv8g8afIP+2kxfXwzIZnxECrKmwkamqRN7JSZMhO6Mh0oDXRKjKt6PM2s3H8+OPF9pQf35npFNtt6driuQ6ueuwqkXtP30m9TtRJVoD3Qb29eztGN472pFGq6GwfWQTpmOmcPvKf1DoJQF6wY1YMDXYDurJdIpuHvhvx5LonseC9BaGDwk3xJswaOwsHjzg4cJuubJfotdD9SO1ribfgmXnP4Dsnfic08t+V2oXf/f13vvEyIN9bka83tbxDKpfCUWOOwuUzLhfbBEX+1ViMftCJf1O8CdcecS2OHH1kqO1DIvrTpT/F9YuvB5D/IbJuVjtoJ6MTf4p441Zc2+NQsx5k8Vcr/+mOTTcjfU474Fu4kH+x/Beebi2g9/zV0gfUPQ268OSbL2kn8zN8Xa/nrxvwJWigPefmMLwh7/XKN7ncXl3xMLltfzj3D5h/3nwh/vJsTyJhJUp6/lk3K25k9aEXt+JI2AmfJXH1Y1cD8PduKPJXLUN1hqz82Y5M3vaRI3+qlPqfp/8nbjnhFgD6yN/lri+Sp54Zib/adnWSFZ0DIN9r25naiTGNY7TiRqj7bLAbfIXwAATaPiT+rfFWAPmHQGe2Uwju8ORwbOrcJO6pjkwHUk4qdKEc6kG0JlpD203ir3r6STuJUY2jELNiaEu0ed6PW3GhGc9tfA4/ef0nngcgBZKLPlyEh1c/LF6nqp6UJdST6xEPEUK3FgVQndIPg078CYtZobYPidXW7q2iZslJC07Cl//2Zdz4/I1i+6ipnmKSV0CqpyoUQcIbdByK/OmmDvP8qcjb2Max2qwlenCotk/cioemegaJv8Ws0FRPQp7tSGmmaq9LtX3k8yb7pjNHzcRRY44SmS9BWVmlsn3kyF8d+yfxDxo0VR9wJP6bOzd7rCLduRCef0jk3xRvwoHDDwRQtFrkyB/wz+wma4RsOLXtuklWdE5oLeQxjWN8M6PDaE20esW/cMyUk0I6l8bIhpG4cc6NYnb6pJZi5A/k7Z+uTJcQwkNGHoKeXI8QYOoR6B5cQF58aV/Thk3DedPOw0HDD/Jt98b2N7AjlT+PZLfQuZTHjNoSbZ5sH7IU1TEyQrYc1dLuForXfCqXEjOFiSBrzNg+ZUCpnCqq+HekO9CT6xHRygubXvD42lFtH5e7omyErregir9ac0Yn/vLDi2bjisg/JNvH5S4+e/hn8fEDPq6daESDc+rFLGyfIPGXPFe6wTm41/YJGfCVxZ+yMzZ36cVfl12Tc3PYm96L5nizyByiyF/XfXa447uxVMGm7C9AE/nbefFXBzNJKNR9N8bzr3/6kU/jG899IzB6i1txMZmuM9OJ1kQrmuJNnhms9NtQj49Ee192n+e46vfuznVj4eqF2sFmQJ9PT+JPvYvRTaXTJmWa480e8afvkc6lkXJSGN04GlfMuEIcmyL/lkSL+DxNPASAGSNnAAA2deWDMnpdJ/4HDT8I/+9j/0+cp6SdxO2n3O4T/7OmnIVNnZvw2tbX8vtUPH/Z5mpLtnlsH7IUPUka0r/lz8o9D095B55Dykmh0W70PVipLfI9amyfMpAXTZZxXMeT6kmRU9DMw0i2T6GeP3XxtLNQS0T+8mo/8nEOH3U4vnLMV8QgkS7bp8FuAANDKpePIrJuFq3xVs8DRW4TCbfataftKXqnCI2Qu/tyWz2TvOCv7UOItDdp4HZLp9f2UZcKVBe83pveK9IDgeKArzxwNv+8+Zg1dpbvPNE+1OOV8vxpH1cddhXOnHImenI9SDtpZNyMp+CePO7w1PqnAqO31kSrmFDEwdGWaMPIhpHCwqHIHyj2+Ej4cm7O443rFoy/+cWbxb7UyF8XZdIDkXoXUXLmiZgVQ2Os0fOQoesk5aSQcTJCHKndNLgqIv94CzozRdvnkJGHACheG/Q53VjDfi37Ye6Uub7X1QSAc6ad44nuV2xfgc8+/lls6MgPLMu2YVuiDfsy+8A5z4t/4aGu9pQJdTEkQi7v4Lj51eFoXzL0AJd7iMb2KQOaWKGi5qWTaOi6w7S9ijbyRzHy1z211ZtOffqTKMo43EHcjuPzR3xeTDjS5W/HrJiwDehGaUm0iLRTedAUCPb8yfYh0VJzveWo8YzJZ4h/y+UdOOfY2rUV//XGf/lq09jM9nSBAU3kr5R3kCP1nJvDnvQetCXaxGtUzVGOgI8acxTOnHKm57sSusg/6KaOWTHE7bg419OGTcPJE08GkE93zDpZzB5fTIlU002DbuCWeAsyTkb8Vqr4u9wVg/EknLIwyCJYqiyCGvnrxmJo3+SHj27Ii/+J+52IuZP9wioTt+Joijdhe892PL3+aXDOxflKO/nIn74D3QMUVND10RxvRle2aPuIyL/TG/nrHqZBYxPqbzEsMQwfP+DjnteWbluKrz2Xn1AmF4kblhwGhzvCBqP35PtFbov8WTnpQuT5U6qn0+PZliANkgMdE/mXgcWswJzmmBUTkQHdoPJqTDLa2j6aVE/X9c7m68h0eLqpaiQc1fNX7SGyA2QrglIS005aWDMt8RZPCmmUSV7ygK/8gCRISH5++s/FtHwAnnr+LnfxxvY3sCu1C7efcjtU1JxnNdtHLe+gRkO+yL9g+3zhqC949kNppar46SL/IAGJW3Ek7aSIZGMsJuoh7ejZgbST9jzgVMEJytVujjcj7aSF2JH470nvyU+c09g+MvK6EGGDoEC0yJ/OOX1PsmP++8z/xpdnfTl0/zGWj/zf2/Uevvz0l7Fk6xLPgG/GyfgG48c3j8cXjvoCzpp6Vv548Ras3L0SP309v8rdpNZJaEu0YX3HenDOPT0dFfqdVdTfIm7HceWMKwHoyz+rkT+QT4tN5STPP2CBeDnyJxuJes+y59+T69HOR6EHuCfQ6eVa0H1h0Ip/jOkHXinSo1r2RFDkH7WqJ03ltpgF13Vx7oPn4vT7TxfbqDddFM+fcuhlgiL/BrvBU5ukNdHqKRjnGfAt3Jw6z5/EnzHme5/2nbASnvfUwm5k2Uxrm+Y7T6r4qze26vX7PP/MXs9szrgdx1vXvIWLp1/s2Q/dkKr4hXn+KmT7yPMiyBLZ0bMDGTeDhJ3AhQddCKBYDkLsO+AGbkm0gIOLCL410YqRDSPhchd7M3u1tg9QFLRdqV3CMlFtn9+c/RvP32pmTpjtQ8IlR6f7t+2Pi6df7HngypDtQ9y/8n5PqmfKSSFpecW/Kd6E64++HoeOPBRA0f6h3yppJzG1bSrW7VuH7ly35z5W17WQl+6UUW2fuBXHgcMPxEuXvYR5h8zzby9958NGHQYgX5ZEFuygAV+5QCL1aKnNlMmXclLIuTntfBQh/lUu91wR8WeMncMYW8kYW80Yu1HzfpIxdm/h/SWMsamVOG4YgbZPQfxVX7M34h+U6mnBEp6/GpGV8vzlpd7kY/si/yDbJ5ZEKpcq2j5S5J91s56LiaJpNc9fZPsULCz1fRKShJ3w9BrUwm4kMOpEoZybEyWvSfxVcQqK/OmhsSe9J9JUfhIFn+evmfQVZPvErbjH9olbca/4OxkkrAS+e+J38dwlz0WO/Km3QD3DtmQbRjbmB/R39uwUqZ6AV5RooLQ7141hifw5UMVfjWrVno9uIF7ULSoIlyxQcSuOb33kW77xHypSFrNintm1z296XhS6y7gZpHIpEVU/fMHD+MmpP/EdX1f8bUrbFKzrWOdLhaQy23L7dOhmawP5By318sleArw9rOkjpmNs01g8t/E5rO9YL3qXQZH/edPOE3OLRNJFIXmAbB86x7rIn7Si7mwfxpgN4E4A5wI4DMBljLHDlM0+D2A35/wgAP8PgN8PqDBBWTcUVanljAPFP0pJ50J5BxrcWdux1retL9vHKh35u9z1RRv0OfniiLGi5y9H/rL46x6EQbYPZSmoDx4R+dsJz4NBzfahG4AWoPnY/h8T7aAbQV6DWM3lp22BYlTaFGtCxs2gI90RSfyF7aNG/prCboGRfyHbR/6eIxpGgIFhZ8/OvPjbCVjMwoiGEb7zGbSQCmWEyJE/9UR3pXaJcSnA+wCVe1J0DlRxVMVFHRPQRv5K0Tqd1aReCwcMOwCAP/LvyfX4bCl6gB04/EBh9cjo0pantE3B1q6tvklpaiXaoN9O9dbl7eZNn4eRDSNx2aGXFbeXHniMMZw88WQ8tf4p7E7vxlFjjwIQHPkfNuowXHvEtWiON/ttn4IVTKi/j83surZ95gBYzTn/gHOeAbAAwAXKNhcA+H3h3w8AmMt6k0jcB3RZNy534XIXcSsuFtAmAj3/wj7ufe9eUQlQ3a/sk9uWrc15zrgZX7SstjfHc3hi7RO49eVbxX59YwPMH6XKA77k+QfZPrJHGtT7cLgDBr/t05XJR3RJO+m1fZjX9hHiH0ti5qiZOGvKWaIdJP6pXEpM8pFTSNVJXiSgTfEm7EntAQcPtCBkSDTlsRGb2R7x/97L30NntjN4wLfwUBV/WzHErTiGJ4d7bB/5fRldKQWgaHNQdg3ZPvQZx3XE+ZX3OW1YUfwpApbFPWknPdkkrfFWtPe0e4IVXeRPr6Vz6XzmmObWVMWf5h/ErJgvg0WeuNeR7ggtFQHkK3veeuKtWPDxBcK2mtI2BQBw5aIrPduqD/6gfft6CJI9NGPUDDx7ybOYPmK6eE19WMiDw0eOPhJAcORP5yJpJ8WAr7B9CrV9CN9AdHKY1vZx3TqI/AFMBLBB+ntj4TXtNpzzHIC9AEYp24Axdh1jbCljbOn27frUy6jYlj/yJ2Ghm1gmaAIJfeb2127HI2seQeE7eLah3HiK/GUoMks7aY/wqt4lRd2LPlyE+1flfVPdgK/6N32WxJ+6kC0J74AvnQv5RtVF/mRhUeaSDHXn43bcI4q25a3tI2yfwvelG5TEnxbipnxyj/gHZPs0xZqwO70bQLQKjnRs+UHMWHGZxc5MJ+5bdV/++4RE/vJ7dL6a4k0ijTHsYSrXwJF/N7J9yKJrsBu84s/9Yz1AUWQA+Gyfbxz3Ddz3yfs84jKpdRJybk6cNyB/Harfl851T65HO1NabT/gjfzJYqHvIIs/B9dmuMiMbRqLTx38KcwcPRPHjT8OQD5jS0fUyH/u5Lm4c+6dIq1Ut50cAKrfW05ooDkDut+EgYl9N9gNIvIn8af0ZkL1/NsSbdpsnyE3yYtz/mvO+WzO+ewxY0ovzhCGLvIP6ta2JloDozRKlcy6Wc8MTBl5kpd6k5AYy/nO1D4ZEv8P934IAPhw74f5ujma+jsqlL2UzhVtn+ZYszbbR/ZCdeLPwZF1s9oHmfD8rYRHZGTP3+Uu0rm0p3ge3fy0Fi6JHq2+9NPXf4rnNz4v2krnCyhG/vLxokT+FOnJto8c+cs9vTDPXxfZx604sk5W2D5B+5HFvznWLP5Ntg899GJWDMOSw2AzGzt7diLH9eMQU9qmiOuhJdECi1ni+10x4wocMOwAj8iRLy8HNhkn40vBlctMBwk1XddUk56EU7b8aFyAUjSJoAdKGBNaJuCNq9/wvU5lQYigyD9hJ3DKpFPEPacTf/k6Ut+3mIVfzv0lvvOR74jrWO0JX3DgBXjtytfE3zTuBhQzAsnzJ9TzGxT5V0P8WbkrxjDGPgLgO5zzswt/fxMAOOc/lLb5a2GblxljMQBbAYzhIQefPXs2X7p0aZ/atPUHP8DKJY9jb3qvJ4JIOxm8ueMNTG2bijGNY/DatvwPJz+xVUYkR2LasGlY1v46RjeOxrS2aVi6bakn3TM/CMjQkd6LtkSbmEIOAIeOOBStiVZ82LEWHem9QswOH30EGqUL4b1d74ED6CpM/JnWNg1buraiKdboifgybhZvbF/haWNzrBlxO4GMk0ZrohU7enbgmLHHYFdqN9bsXY2Zow5HKteDNXvXoNFuRI+Tj4aPHnO056Lf0rUFGzs3YnTjGOxJ7cbhow/HCuVYAHDUmKORyvVg5e68DXbAsAPRHG/CWzvewgHDDkBXtku0Acg/AFfuXgkGhtZEK1K5FDJuBqMaRnly1o8bdxyWty/Pix+LYdbYWdiZ2okP9n6A1nirWMxjxsgZgevbEp3ZTry7613ErQSyhXNuMRsudzB73HHoyHRgVaH9E1smYb/mCeJ6II4YfSR29GwXkSz9ln/f+Xck7ST2pPdgYstE7Ne8n+eYOqg0NABMaZuKdR1rxWQiKp+8vH05RjaMRMpJweWuGJCkdh015mi8s/NtZN0sxjaNw86eHfmkAFieSJW2H980Hlu7t2L68Omit/Turnc9gQwATGrZHxOax2PN3g/Qne3CEaOP8LV/bcdabO/ZjsNGHobGeBP2ZTqwavcqNMWa0ZZsw9auLZ65CjJ0fvvC69te98y8pu9UbPsk3yIpMu/sfAdduS7MGnsMYprInc7VceOOK9mW1XtWe3pRYxvHCnsKAN7e+Q4SdhwHDz8YOe5gefsy7N86GQzA+n35VcfoGqLjDk8OR8bJYuaow8R9AgAHH3cWjvr3n5Vskw7G2Ouc8+Ca3AUqEfm/BuBgxtg0xlgCwKUAHlG2eQTANYV/XwTgb2HCXxmYEOjuXDc6Mh1wuXchBiKsiBUHBy9cfG5AkznnAOcAGBQ3R0TcnLtgcjE3tbWMIZXrEW3O2xUcUPxX3UAJYyyfYspdT3po0bvlnkgkaG9F68Yt/Fs/LGMVjufdS3FbGjAu7re40AsDk8ri+qM2ameO57Bq9yphsXlTS/WRureNNABdjKDo5s+5WWQl79sNiLIYGBjk70Hf1JI8XcuzfRC60gCO6yifoVnS+n3FrZiI/C0UfwOdFQh4J4hl3RyWbnsdndlO3/VfvL792WXE/q2TcdDwg9Acb84fG3RsBrdgKQZVArXKGN6bOXomJrVMQozl71G1RxR2zoHitVdquyiU2gfdg3l44TPF/wf8vQebxeCQRkgBZTWyfcqO/AGAMXYegDsA2AB+wzn/d8bYrQCWcs4fYYw1APgDgFkAdgG4lHP+Qdg+y4n8AeDfX/l3PL72cTx/6fM44vf5SGb+efNx5aIrcefcO3HKpFMwe/5spJ00zph8Bp5a/5R2P6dMOgXf/si3Mff+uTh9/9Nxx2l34Mi7jvRsc9aUs5C0k1jWvgwnTDgBD77/IMY1jcO27m249cRb8amDP4WvPvNVfLDnA6zZuwYAsOjTi4QfCQBfeOoLeHFTflm6uBXHnAlzsG7vOhw19ijcdvJtYrs9qT04+d6Tvedq3GxMbJmIhWsWAsj7iC9e9iKe2/gcrl98Pe4+726s2bsGt7x4C44bf5yob/LSZS95apH88d0/4rZXb8OZU87E8vblePiCh3HSgpN85+TVK17F+o71uOjRiwAAd3zsDhwy8hCc+9C5+P5Hv49Xt76K17a+hicuegIA8PaOt3HpXy4FAJw66VSxdN3XZn8NP1r6I7Hft655C8f+4VhPlszNx9+M7y/5Ps6Zeg4eX/s4AGDxxYt9y+6prNmzBhcuvNBzQx4z9hgsa1+Gu869C0u2LMGdK+4EAJwz9Rz86NQfieuEeHre03h49cP42bJ8BHbPx+/B4aMPxxWLroDjOnh759u4cc6NuGLGFQCAd3e+i3l/9ueQA/lBwzd35NfevXPunbh+8fWY0jYFu3p24aXLX8qfm3tPxdzJc7FmzxrErTj+5+z/AQDRrreueQtXP3Y1lrcvxz8e8Y9Y9OEibOrchDGNY/C3ecW1YGn7X5z+C9zwtxvwpaO/hIOGH4SvPvNVz3kgrjvyOvyfWf8H1z1xHbpz3Zh/3vzQcwsAK9pX4KrHrsIxY4/B5TMux78++6/4w7l/wFWPXQUg781TlHzz8TfjkkMvKbnPMM558Bxs6tyE73zkO/jOy98Rr8vnX8e1f70WS7YuwfKrlmutNPncluLrz30dj334mPj7ihlX4MY5xcz2f3ziH9GT68H88+Zjd2o3Trn3FNw450bErTi+98r3AACPffoxTGqdhB+/9mO0JduwO7UbC1cvxEuXv4Sn1z+NLz+dn1T3qzN+hZMm+u+9KESN/EuHUBHgnC8CsEh57VvSv1MALlY/15/oFlVZu3ctgGK2xd0fvxsvbXpJLOmmw3GLxcHSbhq6ZR+zbhZxO18viKKq8c3jsa17m8fzl/1J3yQvydv/2P4fw4ubXkTCTmhrAOm+qzyQRLno1KOR8/xVr16Gtk87aTEzUYfP87e8JZ3V8Y0gX1y36Laa4kZdfPl4UVZtomPKEdTElolY1r4MG/Zt8Hj+QSl8MRbzDOjKnj8N5OsGhHXIE8DEpKZstycLhR5UOZ5Dg6X33umzSTspzkmQ790Ub8LIhpFo7273LB6uTkaTUz1LDc4S1G7bsnH21LNx4n4nojXRKr7DjFEz8NLml0Lb1xsaY41IWAlf28N67XRsBv/4VV9Qx9/Ue7jBbhAD8J4BX+m6oGDrX4/7VwDAr974FfZl9yHn5qo+yasi4l+LyCWdyW99fdvrAIo30PQR0zF9xHR856XviM/99uzfgoPjc3/9HCa2TESO5zyzTXXdMbm8A10QY5vGgoF5Fo2QbwLdgC9x+aGX48l1T6I71x04w5c+Qxk0tO+PTvyoWM3IM+DrRhvwpbZalj/Pn45vW7bnYaOu4Zt20h7xV9MlCXmiXWu8VaTiymzalx88lG/6oCUbZXTT/sc3j4fFLKzvWI/27nbMGDkDVx12lajXo+LL8y/c/JSxBAQ/2FTkMQp5Riv9G4BYvUud2X3qpFPF3/TdZfEPslsSdgKjG0djR88Oz3wA34CvU5zk1dbkTZEMgkSXzgmJGv1+h448VIg/Fd8rh6ZYkyeDjSj1YEnaScSteGCJ6jvn3hmY7KHimxGvPAxowFdeDIrqWRHy7w0Us7bk5TmBIZjtU0nkVE/Kj166LW8jyZkXgDd6aE204rjxx+Gta97ChOYJcFxHDAanc2mfOFnMyuf5w/U85ZvjzWhNtIqbTo2GdeUdgPzNfey4Yz053er3Em0t5MnHrJi4gE/f/3QxCCVH/vQglIUzSPwzTiYf+WvmFNDN5hF/S8n2UcQ/6KEnp2y2JFrEA+rKGVeKWj1U1pceWmppiSB0ojCmaQzGN43PR/5d2zC2aSw+eeAnfRkkRFi2D6W9eh5smvNF6B5e3dluz7Unxm244/mOv5j7C/zs9J95Phu34+I3oFo8KgkrgZZ4C7qz3Z6qtaoAUXAj164vBT1cgx548uxZeVC0rzTGGvOz1jWz0sOI2/HAEhBA3tal8hylUAMx9R5O2kms7ViLo+46SjxQqbwDoZ4vugf2pvd61x834t935No+dCPRuqBq11G+weWLiSp0ikkwjl/8k3ZSRKwWitFyU6wpn81RyFDJOBlfF9/TXkn8GWOioNZ7u97zbOfJFy/c9HErLr6b/NCgVLZdqV1CWEk8GJivDR7bR5mZSNEinSu5fHGM+Wf4yul9spUQNOGFFj8H8r2mzxz8GQDFxV5IlKJaCPJ2h486HN/76Pcwb/o87N+2Pzbu24iOTEfJlFG5RyW3PW7Fi5G/xhYC/Cl9dP6oWByQ7yV5xL8wkExlMHTQfhmYOPbMUTO121LFze5st2cGu3zeG2ONxcVXcqnAXoRv37Z+AJagssxAPkOnXKYOm4oDhh/Q58i/EpQSf/k3p/EOC1ZosEJF5PZm9lY98h+0tg/V9rn6sat99U3UyCdQ/C0bPU5P0fPXiD/NinW5C8sqev5N8ab8ohAU+bsZJK0kWCELKWjmLkW4cyfPxWcP/yxOmHCC93sxvyDHWAzHjTsOb25/05MWSqtcbe7cLAZsfWUAACAASURBVG5WuvGD5gsAhchfKe8wLDksPxtWsj7k8yRP8so4Gc8My6DzK4tQxsmIi18uvLejZwcSVrGWUGTxl0RZLr42qWUSntnwDFzuhka59EAL8vypWy8/0OVzkowlPenDdM3RkoCE/HnGGDjnvshfhs5ZykmJfPojx3gTEKgHkbATaIo1YXNusyfXn1aTcrgjflcgf31HsdSA4Mi/MdaInlyP58EapadWiptPuBmcc7yy5RXP66WEfc74OYEZTL3Fl6mj/C1fT9QTVz1/FTnyN55/haAffHn7co/Y28z2RTdB0RvVB4oS+VO+NT2xhe2jDPjSoi1BkT/1ShhjYgEX3fcCij5rzIrhhlk34MrDrhSzLIH8xTiyYSS2dG0RA360f90FSTcSiT9QXGeA1lWlC1T2UD2TvOAi5aQw1i5m4wT54rLQpHIp7wxsOy7yxuN2XDx0okZx8nFUz5Ui3SChY2DFlcIkv1qIvyTY6kxn3et0XPps0MPQYhZcuNpqrgTN0E7lUqKG1FGjvbNhqceasPMDpN25bs8Ad8JO5Nd6cBwMTw4Xk/d6cj3RI39LH/n/+VN/Rle2K/J+egNjzB/5B5R0Jj554CfxyQM/WZHjh42/Ad7ffHcqH/kzxnzjIzIU+XdkOrxrhxvbp+/IJ1quHNkUa/IN/sg3s/xv28pHRzRrL+2kfUv9JewEHO6I3HZ5TVB5LVAh/oULJlD8I2SyEBT525atrVQKAPs174ctXVt8tk9QjSD6nmoOOUVyusJg6gLuYTNfVWvk5uNvRmOsEWknLdpI54i+T18if8aKtoh8kzbEGpDKpcRi2jpsq1jRlOq6AEXBkwU76HsGir/lLRkR5PkHRYtkLfTkenD21LMBFKt9qu2IW3E0xZp8nn/STopthiWHoTPTKcpyRPb8C99bfRiPbRqLacOmif0cPOLgSPuLinq8MD+/0qi/iSr+QZG/OlgvI3pyuVTVI/9BK/5BXU3d8oKedD15cIbls2lI8DJOxlfXJ2EniraPTvzT3mwfMQFLU9sH8FtSYZDnH9atnNAyAZs7NyPH870NEqWglFEgf45IzGk7GhTVir/lLWuhDvh6jiGtUGVbNi459BJcfujlSDkpafJX/vegVNC4XbRKSkV6Muqi90D+dyHLJijyj7GYEBXGGG4/+XYcPupw8dvI+/P0Gllp8U9YCc9n5GuPgQnPPyjyv/CgC3H4qMNx+aGX47aTb8OSy5f4ghn6bMyKoSnehI5MhycAosgfyD/UO7OdokRJVNsnKPInLGbhrnPvwm/P/m2k/UWFjjexZSIYWK+WnCyXUnW2ZFeAxF8u7KY7t/TASOVSZsC3Uqg3D00B15Vx8HTDlchfHvCVBUp81koUB3yZJar6Je2kWAiac573/O0kTtzvRLFvGeH5x6NH/sL2CckymdA8AVu7toq6OmE3rTzgS4JC5zFscNRmRc+favsEib+o9yNFSclY0vOQpbaR7y9H/r2J9ILEn1AHZRd8YgFuOeEW0ZMizjvgPNzziXs8EbV6DPV19fs3xhqFnSQ/LHUJBmGe/6jGUbjnE/dgQssEIe4qlCnVFGvS9iQ9kX9imFiuUNfuIOjzYdferLGzIhXh6w103CNHH4lnLnkmMCuuPyhl+8hrK2gjf836vUL8nZRI2gDqpJ5/raL+MPu17Be4bajnz4uTvNQVsYD8zULF3+TIv8FuQFuiTdRRoQqQPzz5h1h44UJfFBAWHQQhBnxDIv/9WvZDyklhR/cOT/aKLrKUbR96n0Qo7CaOWTEx3V+X6kmcOulU7fckEZZXzAKKVSI9kX8vJgyRnxqUhaOe65mjZmLeIfM8D0kdQeIf5vkn7IQn4tYNmIoB3xDPPwrXzLwGb13zFuJ23PNwkPP741YcNrPRksgvni6u24i2D3nZYddefyDGfgpjQlU9tmr7KA9o8vnlf8sDvroJdAkrPwktlUth2bZloqqpWcC9DNQfRp7hqKKbyAMUauxLESlQXO1I/qxO/BN2wjOYQz54Q6xBlMOV6Yv4U+QfJlSTWycDAJZsWYIRyRHaaFhtQ87NCVsqauQvL+CuzmkAgDeufgM/P/3n2u9J21LWiSr+cupjb2wfUXmSBUT+AUIXs/wlv2U84i+1R7YB1IcUef1i3MD2jx/IM3wrJapy5E8WScbJry0Rs2L5xeQLi+QAeoEKYv/W/UPvq/5A1/uqFqVSPa+ccSWGJYchbsU9A74UxevubcYYGmINWNuxFpu7NosCc8b2KQP1h1KXoZOhG1HNbacBX1n81VWHEnYCjuuISV5yBEXpjrtSu8DBQ6PW/or8Z4+fjcZYI9p72nHypJNDJ+eoQgREFH8p1ZMmlKnibzHLk/kgiwyJsBD/gliTeHdmOvsU+dPDQ/5NZcEPOtc2s0PtJfm9oPYERv6K6PvKO5TI9uktstVAYygZJyMifxqLoOqqUSN/AHjo/Idw1WFXVaSdURlQ8fcVZfP+fcSYI/DCpS/ggGEHiPNpwRIBY9D11mA34NUtrwLI36+AsX3KQh0EC7MthCAq/qWa6gl4a7TTZx3uwHEdj/gn7STa4nnxpxzrsKiVBiF7I25RBnwbY4346H4fBZCfOyBqsoTYPvL7wvZJhNg+0iSvUt4xnWNd5E+DkmrkL1ei7FPkL9s+Mf9DR0XO9tERZPt49qGpmyRH/rpsGYbSef69RRf5p510MfIvXEN0XfcmRVN+6FeLvgQBlaJU5E8MTw732D609GqQ9ZyMJcWkMApSq2H7DNo8f3ViV9JO4ptzvulZuo3QRWFAoTicEvl/4akveD9rx/Opnsinek5qmYS3dryF4cnh4uJ4esPTAMIvWJoO3htxk8s7hHHlYVeCMYbZ42fjjfY3Aj8TZ94JR0Dxgg+LhOXyDjSgHiiKln/wi3oBtMiLKv5ZN4ssz5Zsh4qI/KWbVhbDIIujVOTvyfYp8T3l7Ty2jyZtVC7LHfbw6Q3y9/2nI/8JWzq34DPTP4OnNzwtbB+gKP696XkOBLVk+wTdd8Mbhou5JIwxHD/+eNxywi34xAGf0G4vX4fkFlQj8h+04k8WApGwE/jM9M9otw3ywdUBXx1JOwnXLU7y+vaJ38b5B52PyW2Tsb4jv4DD/avu1+5fRh4riEqUyB/IL0lHi32EDfh6Mp0o8lfmJcydPNf3OTnbh7q4YX464C0PQaUgaE4ERZ/yMnvi4dgH20e+FqLYPmouvu59IuhhrZuMRNaP/Lkgz79Skb/8kJ02bBr+cN4fxHFtZgvxX7MnX2q8VKnsgUbtOVUT9TcJivxprA/I2z6MMcw7RF/qGyhekzErJh4E1VjAffCKf8Yr/mFeZtCElWHJYdiX2ecZxdd9Nsdz+UleloXmeLOowy1fBEB+YYogyFrqzUUt8s57ESUGfVfAn3kCeL3/N69+09PNl0tVULYPiX/Q9wgb8KVBR3oYyOu10vnpTcRH4k89CiA824eImu0TVC2yLdFWzLUvzJCO23EcPfZoUedGeP9KeQd1olu5yJG/LFZk+zQn8tcQrSBFJUFqlVqK/IN+I93YWRh0TbbEWzyTJfubQSv+6sBsmJcZNAh6+OjD4XIXK9r9SxkScSvuKe8gIy+U8vJlLwdWXwSKk896I/6lJtroELNeNZGl7qKlfdOArWdfdiKfFirlrQetk0zoPH+6+GktU/pbTlOkBV764vnL5Yyjev5RxF/3Wy28cCGGJ4fjp0t/KrbJ5XKIW3F898Tviu10kb+8xnB/ZPuo30GO/FftXoUxjWP6pSxDJWmKNeGSQy7BiRNPHOimBAq7fG9FGROhYKc53izWzjYzfMvghlk34IIDLxB/h4mqLu0OgFjLtL2n3fcZImblF43h3F+szVMVM0T4AXgGiqMiJtr0QiiiDviq5R1KPSzoIqceV5CfLnKelUlegN/2IY4ddyxO3/90DEsOw2WHXhb43VQo8t+b2Stei5Lqedr+p+GjEz8auF86h7rf6oBhB2Bkw0jxPWkb9frTRbAMTIh/xSL/gEmDcqonkB9gDJsLUyswxnDzCTcHVjLtT9SFnIJ+I/n1KJE/WaD0W9nMNrZPOYxuHI3vn/R9PLvxWexJ7+lT5D+iYQT2b90fG/ZtUNbnLELjAg53tE/5sU1jMWf8nJLtFZ5/LyLbcU3jcNH0i3yVP8MIS/X0iD+8qZ66C/3np/8cd793t7CfLGaJxetHNuon4JDYhUb+kii/dsVrQqheuPSFKF9RoLN9POWMbb3tc8OsG0L3qxusVaFzSdeE+rvq7DeLWRUX/8D0wlgDEnbCE5TUg/gPJGppl6BxGY/4R4ivKfiRa3W5rrF9yqYh1gCko0X+OkE8YvQR2LBvA1oTrUKcAODi6ReLqds0yUt3wy6+eHGkdl40/SI8ue5JHD326EjbA/mL5Nsf+Xbk7QFpwFdX24f5I/+gQnRAPieZ8pKB/IW+ozsv/lSaQddmQO/5U4QuP6h7k3euQum9cpZF3Movt2nB6nNRsCjphnTemuPN6M52a/P+AX+ePw1sV2rAN6hX+MWjvojObCeSdhLHTzgeS7YsqWqdnHpEjfwrZfuoNidpSn8z+MW/cGKTVunIXxfJHTnmSCz6cBFa417xP2niSTh98un42bKfCfEvJ+f5xP1OjLSIdLkI0WH+70rePc1WpteAaJEoY0zkKweJv9bzj+k9/3KJW3G8esWrHuFljIk6O+XsFwi36Oi8XXDgBThz6pm+B42u98BY0fapVKonoc5zkattXnHoFViyZUnVyyXUG77Iv0K2j1iRTYr8jfhXgFLrnALBqZ5AsaSv6tnLnrjjOh7BrGXCBnyBvOhkeMYX+UcR/xENI9De3Y62RFtgVF0q26fU4he9RVtJ0W4o60EdyfaRFr3RlfPQ2W8WJNunQpE/ADzwyQcwqlH/MAaA0yafhv896399i8IYvOiWcNXRa/EvBDtkn5oB3woRZfm/sJv5kJGHIG7FMaVtCn5x+i/E6/JiJxz5WZnVEv+PH/BxnH/g+X36bKlBYrnUBdC7yH9q21QACLUPgsSfgYmyEP09a7Qh1lBW7yJKrjmdt6AILqi2T6U9fyB/DZeydOZMmFOWxTYUUG2foN6Zx/aJ0MMkz58ys4ztUyGE7dPHyD9hJ/C5wz+HA4cfiFP3P1W8rmbD6Fbn6i9uO/m2Pn+WFjkJEhc5tROQPH+r9Heb0jYFr259NTTK1Ik/YwzN8WbhQfc3tE5yXwnL9iHovAVFcEEzfGk+QyUjf0P/EHRP9Drbp3AvkLtgbJ8KQSe2VFE1i1mB3Xhd9ofIhincpFQvvx6QSySrkB3RF9tnStuU/P5D7JBpw6bh0JGH+spsVF38K+D5lyp5AQTP1NTN0eivyN9QGVTbp1KeP13zzbHq2j6DXvyj2D6Af2HtIBJWAhk3I5769ENnnExZglJN5MVRVOhhqdo+US5iEn91drXM6MbRuP+T9/teb4m3YBu2VcV6OHTkoWV9Xq3Jr+Og4QcBKFphKrpsH3nA14h/7RHZ8+9ttk/hmqfZ1sb2qRCNsUbEWKyksCesRGjESiTtZF78lTz4rJutm676Pxz+D4ECSOlm9P3UHkAYtFpaXy5c6vJWI/L/1ke+Vdbn1QlcOs6cciYWfHwBDht1mPZ9re0Dq1jeoU6upaFEnyZ5RRhWFQO+seJ8GSP+FaAp3hSpUmGYFaJuh6xU9VKyfeol8v/c4Z8LfI/OFX0/GtSKIv4HDT8Ilx5yKS455JJet4nS3CqV5tmfRC0uFlbLKWiSl/j34M/FqDuOHpOfg0OReaD4W73M9ol5s31iVqz26/kzxkYyxp5kjL1f+O+IgO0eZ4ztYYz9uZzj9YUrZ1yJH5/645LbtSZaIy2eLhZAV7zwsEW36wk54wCQBrYjDPjalo2bTrgJB404qNfHpXNPmQ+1TJRUz1Jo6/lLFkE9pA0PNWaPn41XLn8Fp0w6BUDwPSFnAUWxfeieo1pgFrPqop7/jQAWc85vY4zdWPj7G5rtfgSgCcA/lXm8XrNfy36Rpq3/9GM/DV2tiiDx19W8qfbCFv2BGvn3JtWzHKpp+5RLlGyfkvsIyPYhjO1TmzTHm4VDEHRPeHpwER7ic8bPwc3H34yjxhwl9lvzkT+ACwD8vvDv3wO4ULcR53wxgH2692qF6SOmR6plThGbutiJ+u96xTfgG1LeoZIMRtsnyj485R3QO9EwDAylMuB6m+cft+O45NBLxOdiVqwqhd3KvcLGcc63FP69FcC4cnbGGLuOMbaUMbZ0+/btZTatf1Cjvd6mddU6cn0R+b+VLjegQuI/EHXae0slbB/tJC/LeP71QKnesK5GVm+gqgH9Tck7mjH2FADdCg83yX9wzjljjGu2iwzn/NcAfg0As2fPLmtf/QVFezQZp7ddvFonyPbp7+9Gnn81Ip5yoeQAeb2G3qJdyUuO/COMsRgGhlK94XI1oVq2T0nx55yfEfQeY2wbY2wC53wLY2wCgODC94MEumlJ/HU18OsZdfGPqtk+iWJd+VonbsXxm7N/gwOHH9jnfYxpHIMYi3nGmeQxo8FgIQ5WSnn+5Y4D2pZdlfug3Dv6EQDXFP59DYCFZe6v5iHbh+rvy6I4mAZ8RZkBZmtX8ao0ZPvQJKdaZ9bYWb5lOnvDR/b7CJ646AmMaRojXhtsvcjBirBEQ4ojEn2x7+plwPc2AGcyxt4HcEbhbzDGZjPG/oc2Yow9D+B+AHMZYxsZY2eXedwBY8aoGQCKSwTKP/RgiNZI/GktXnmJxv5E2D51EPlXAsaYR/gBJdtnEFxLg5Vq2D41P8mLc74TwFzN60sBXCv9fXI5x6kl/unIf8LxE47HrLGzACiRf51M8gqDBnypZ2MzuypCRN73UBF/HfL1Mxh6kYOV/rZ9fnnGL6sScA36Gb6VxrZsHDvuWM/fun/XKyLyLyzEft608yKlwJYL3VBDWfxN5F8flIr8K5HtUw2M+JdJb+t41Do04JvO5SP/maNnhpYpqBTjmvNZwvKDdahhPP/6oFSqZ708xI34l8mgi/zj+cifbJ9qMbFlIv7yqb8M6UXE60U0hjolB3ylDMBatoKN+JeJfJPWQ2mCUlDkT7ZPNZncNrnqx6wlTORfH5T0/Otk4mfttqxOkH/oeihNUAry/Mn2MVQPOUqsZdEY6pTM9rHqI/3bRP5lIl8A9VCRshTqgK+hepjIvz6YM2EOztt7XmBPv9wB32phxL9MZH9vMET+VFu82p6/wXj+9cKssbNEqreOekkCqd2W1QnyDVtOlcdagTz/iw6+aIBbMvQw9fwHB8b2GSIMNs/ftmwsu2pZv1fxNPipl4FCQzjG9hkieLJ9BoHnD9RHWeXBiBnwHRz0dhnHgaJ2W1YnyD/0YIj8DQOH8fwHB/JvV8t5/kb8y2Sw5fkbBg6T7TM4qBf7rnZbVifIkf9gsX0MA4Op5z84MLbPEGGwDfgaBg45LbCWs0QM4Xhsnxr+HY34l8lgHPA1DAzG8x8cmDz/IYLH9jGev6EMTJ7/4MDYPkME+SlP6/saDH1BvpYGQ4XYoYqxfYYI9fJDG2ofk+0zOKgXy85cYWViblJDpfBM8jK3Zt1SL5pQH62sYeTCbgZDOZjIf3BQLw6AucLKxNykhkpBomExq24ExFC/GOUqk3rx9wy1DwUSJqAwVANzlZWJidAMlYJ8fhNQGKqBEX+DoUagOvAm8jdUA3OVGQw1AkX+RvwN1cBcZQZDjWA8f0M1MVeZwVAj0PiR8fwN1aAs8WeMjWSMPckYe7/w3xGabY5mjL3MGHubMfYmY+ySco5pMAxWTORvqCblXmU3AljMOT8YwOLC3yrdAK7mnM8EcA6AOxhjw8s8bk1x3Pjj8C/H/stAN8NQ59AMXyP+hmpQ7vTUCwB8rPDv3wN4BsA35A0456ukf29mjLUDGANgT5nHrhl+c/ZvBroJhkGAifwN1aTcq2wc53xL4d9bAYwL25gxNgdAAsCaMo9rMAw6yOs3nr+hGpSM/BljTwEYr3nrJvkPzjlnjPGQ/UwA8AcA13DO3YBtrgNwHQBMnjy5VNMMhkGFXN7BYOhvSoo/5/yMoPcYY9sYYxM451sK4t4esF0bgL8AuIlz/krIsX4N4NcAMHv27MAHicEwGDG2j6GalHuVPQLgmsK/rwGwUN2AMZYA8CcAd3HOHyjzeAbDoMWUdzBUk3LF/zYAZzLG3gdwRuFvMMZmM8b+p7DNPACnAPgHxtiKwv+OLvO4BsOgw9g+hmpSVrYP53wngLma15cCuLbw7/kA5pdzHINhKGBsH0M1MVeZwVAjkOgb28dQDYz4Gww1gpnkZagm5iozGGoEiviN+BuqgbnKDIYawRR2M1QTs/q4wVAjmAHfwcPPTvsZ9qRru4KNEX+DoUYw4j94OH3y6QPdhJKYq8xgqBHMgK+hmpirzGCoEUyqp6GaGPE3GGoEY/sYqom5ygyGGkGIv2VuS0P/Y64yg6FGEOJvbktDFTBXmcFQI5g8f0M1MeJvMNQIFPEbz99QDcxVZjDUCCLbxzKRv6H/MeJvMNQIpp6/oZqYq8xgqBGE7WNuS0MVMFeZwVAjmFRPQzUxV5nBUCOYGb6GamLE32CoEcwMX0M1MVeZwVAjmAFfQzUxV5nBUCOYyN9QTcxVZjDUCJTlYzx/QzUw4m8w1AjG9jFUE3OVGQw1gsn2MVQTI/4GQ41A4k89AIOhPzHibzDUCCbyN1QTI/4GQ41gqnoaqklZVxljbCRj7EnG2PuF/47QbDOFMbaMMbaCMfY2Y+wL5RzTYBismHr+hmpSbohxI4DFnPODASwu/K2yBcBHOOdHAzgewI2Msf3KPK7BMOgwef6GalLuVXYBgN8X/v17ABeqG3DOM5zzdOHPZAWOaTAMSoz4G6pJuVfZOM75lsK/twIYp9uIMbY/Y+xNABsA3M4531zmcQ2GQQeDyfM3VI9YqQ0YY08BGK956yb5D845Z4xx3T445xsAHFmwex5mjD3AOd+mOdZ1AK4DgMmTJ0dovsEweKAVvIznb6gGJcWfc35G0HuMsW2MsQmc8y2MsQkA2kvsazNj7O8ATgbwgOb9XwP4NQDMnj1b+yAxGAYrSTuJrxzzFcydPHegm2IYApTbv3wEwDWFf18DYKG6AWNsEmOssfDvEQBOArCyzOMaDIOSzx/xeUwdNnWgm2EYApQr/rcBOJMx9j6AMwp/gzE2mzH2P4VtZgBYwhh7A8CzAH7MOX+rzOMaDAaDoQxK2j5hcM53AvD1UTnnSwFcW/j3kwCOLOc4BoPBYKgsJq3AYDAYhiBG/A0Gg2EIYsTfYDAYhiBG/A0Gg2EIYsTfYDAYhiBG/A0Gg2EIwjivzYm0jLHtANb14yFGA9jRj/uvFKadlaVe2gnUT1tNOytLue2cwjkfU2qjmhX//oYxtpRzPnug21EK087KUi/tBOqnraadlaVa7TS2j8FgMAxBjPgbDAbDEGQoi/+vB7oBETHtrCz10k6gftpq2llZqtLOIev5GwwGw1BmKEf+BoPBMGQZNOLPGPsNY6y9sFgMvXYUY+xlxthbjLFHGWNthdevYIytkP7nMsaOZow1Mcb+whh7jzH2NmPstlpsZ+G9BGPs14yxVYX2fmaA2xpnjP2+8Pq7jLFvSp85hzG2kjG2mjF2Y622s/C+zRhbzhj78/9v7+xiqyiiOP7714qUotJqIGjVlkQeSHygGiTGaNWIWj/wxS9iRItP+gBoNBh9IZBojDGExMQHlaAojSiJaKJSBYOxQhQitbRI+TBgvVpjVYw8FOT4MKfpcnMvsZfeeze980s2O3tmdvffuXvOzs7OdtKqU9JSvz67Ja2XNLGMOidIWuP23ZJa3J42X8qpM5FXNF9SmMZ2q6Qer4vFbq+X1CGpz9d1bpek1e4vXZKaE8da6OX7JC3Md87/hZmNiwW4DmgGuhO2b4DrPd0GrMix3xXAAU9PAm7w9ATgS+C2tOn07eXASk9XAReWs06BBUB7oh5/BBqBs4ADwAyv093ArLTpTOz3BPAO8FFK6/Ni4BBQ43nvAg+XUefjwBpPTwV2+vWYKl/Kp9NK4EvAdKDZ0+cC+4BZwIvAMrcvI8xvDtAKfAwImAvscHs9cNDXdZ6uK1TXuGn5m9k2YDDLPBPY5ukOINcd/QGg3Y9xzMy2enoI2AU0pE2n0wY878c8aWZj/vHKKLUaUCupGqgBhoCjwBxgv5kd9DptB+anUCeSGoDbgdcoAmOlkzAPR43nTQJ+LqPOWcAW328A+BO4KoW+lFOn5xXVl8wsY2a7PP030Eu4ic8H1nqxtcDdnp4PvGmB7cAUhWlybwE6zGzQzP7wv+/WQnWNm+Cfhz2MBJp7gEtylLkPWJ9tlDQFuBP4vGjqRhiVTtcGsELSLkkbJE0rvkwgv9b3gH+ADHCYMGPbIOEiP5LY/ye3pU0nwCrgaeBkCfQVpNPM+oGX3JYB/jKzzWXUuRu4S1K1pCbgSrKu35T4Uk6dpfYlSY3AbGAHMM3MMp71CzB83nw+M6a+NN6DfxvwmKSdhMetoWSmpKuBY2bWnWWvJgTa1WZ2MIU6qwmtqE4zawa+JgSEUpBP6xzgX+AioAl4UtKMEmnKxah0SroDGDCznSnXWUcIbk2eVyvpwTLqfIMQhL4l3Dw7XTeQKl/Kp7NkviRpMvA+sMTMjibzLPTrlHTo5RlN45h2zGwvMA9A0kzCI32S+8nR6ieMs+0zs1XFVRgoQOfvwDFgo29vABYVWSZwWq0LgE/M7DgwIOkrwmP1EU5tCTYA/SnUOZvQMmwFJgLnSVpnZkUNrAXoNOCQmf3m+2wErgHWlUOnmZ0Alg6Xk9RJ6NMeJhW+dBqdJfElSWcTAv/bZjZ8rl8lTTezjHfrDLi9n9w+0w+0ZNm/KFTTuG75S5rq6yrgOeDVRF4VcC+n9qMjaSVwPrAkfIqF7AAAAWNJREFUrTq9lfAhIxfCTUBPmbUeBm70vFrCi6q9hBdwl0tqkjSBcCPblDadZvaMmTWYWaNr3FLswF+ITrfP9dE0Ivz2veXS6TpqPX0zcMLMenw7Nb6UT2cpfMl/p9eBXjN7OZG1CRgesbMQ+CBhf8hH/cwldO1lgE+BeZLq/AlwntsKYyzfapdzIbSMM8BxwuPdImAx4e6+D3gB/6jNy7cA27OO0UBoWfUC3/nyaNp0uv0ywoutLkJf6qXlrFNgMqHVtIfgPE8ljtPq5Q8Az6ZVZ1adF2O0z1jV53LCjaAbeAs4p4w6G4Ef3Gc+I/xHydT5Uj6dpfAl4Fqvi65EXbQCF/j5+lxTvZcX8Ir7y/eEF+jDx2oD9vvyyJnoil/4RiKRSAUyrrt9IpFIJJKbGPwjkUikAonBPxKJRCqQGPwjkUikAonBPxKJRCqQGPwjkUikAonBPxKJRCqQGPwjkUikAvkPex+2SR4fT2gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph(train_dates[12:], y_train, y_train_pred)\n",
    "graph(dev_dates[12:], y_dev, y_dev_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}