{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "051d70d956493feee0c6d64651c6a088724dca2a",
    "id": "DfBfWzYzWitP",
    "papermill": {
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     "end_time": "2021-07-22T21:33:20.414432",
     "exception": false,
     "start_time": "2021-07-22T21:33:20.377253",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# ML for wage prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kk13PYstWitS",
    "papermill": {
     "duration": 0.035632,
     "end_time": "2021-07-22T21:33:20.486115",
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     "start_time": "2021-07-22T21:33:20.450483",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We illustrate how to predict an outcome variable Y in a high-dimensional setting, where the number of covariates $p$ is large in relation to the sample size $n$. So far we have used linear prediction rules, e.g. Lasso regression, for estimation.\n",
    "Now, we also consider nonlinear prediction rules including tree-based methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fQLyPTHhWitT",
    "papermill": {
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     "end_time": "2021-07-22T21:33:20.558154",
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     "start_time": "2021-07-22T21:33:20.522456",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QAJp34YkWitU",
    "papermill": {
     "duration": 0.035998,
     "end_time": "2021-07-22T21:33:20.630093",
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     "start_time": "2021-07-22T21:33:20.594095",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Again, we consider data from the U.S. March Supplement of the Current Population Survey (CPS) in 2015.\n",
    "The preproccessed sample consists of $5150$ never-married individuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as  pd, numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wage</th>\n",
       "      <th>lwage</th>\n",
       "      <th>sex</th>\n",
       "      <th>shs</th>\n",
       "      <th>hsg</th>\n",
       "      <th>scl</th>\n",
       "      <th>clg</th>\n",
       "      <th>ad</th>\n",
       "      <th>mw</th>\n",
       "      <th>so</th>\n",
       "      <th>we</th>\n",
       "      <th>ne</th>\n",
       "      <th>exp1</th>\n",
       "      <th>exp2</th>\n",
       "      <th>exp3</th>\n",
       "      <th>exp4</th>\n",
       "      <th>occ</th>\n",
       "      <th>occ2</th>\n",
       "      <th>ind</th>\n",
       "      <th>ind2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.615385</td>\n",
       "      <td>2.263364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.2401</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.076923</td>\n",
       "      <td>3.872802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.61</td>\n",
       "      <td>29.791</td>\n",
       "      <td>92.3521</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5070.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.057692</td>\n",
       "      <td>2.403126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>5.832</td>\n",
       "      <td>10.4976</td>\n",
       "      <td>6260.0</td>\n",
       "      <td>19</td>\n",
       "      <td>770.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.942308</td>\n",
       "      <td>2.634928</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>15.625</td>\n",
       "      <td>39.0625</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6990.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.846154</td>\n",
       "      <td>3.361977</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.84</td>\n",
       "      <td>10.648</td>\n",
       "      <td>23.4256</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>6</td>\n",
       "      <td>9470.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        wage     lwage  sex  shs  hsg  scl  clg   ad   mw   so   we   ne  \\\n",
       "0   9.615385  2.263364  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0   \n",
       "1  48.076923  3.872802  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0   \n",
       "2  11.057692  2.403126  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       "3  13.942308  2.634928  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0   \n",
       "4  28.846154  3.361977  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0   \n",
       "\n",
       "   exp1  exp2    exp3     exp4     occ  occ2     ind  ind2  \n",
       "0   7.0  0.49   0.343   0.2401  3600.0    11  8370.0    18  \n",
       "1  31.0  9.61  29.791  92.3521  3050.0    10  5070.0     9  \n",
       "2  18.0  3.24   5.832  10.4976  6260.0    19   770.0     4  \n",
       "3  25.0  6.25  15.625  39.0625   420.0     1  6990.0    12  \n",
       "4  22.0  4.84  10.648  23.4256  2015.0     6  9470.0    22  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/d2cml-ai/14.388_r/main/data/wage2015_subsample_inference.csv\"\n",
    "data = pd.read_csv(url).drop(columns=[\"rownames\"])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SijRfuBnWitY",
    "papermill": {
     "duration": 0.036385,
     "end_time": "2021-07-22T21:33:20.972103",
     "exception": false,
     "start_time": "2021-07-22T21:33:20.935718",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The outcomes $Y_i$'s are hourly (log) wages of never-married workers living in the U.S. The raw regressors $Z_i$'s consist of a variety of characteristics, including experience, education and industry and occupation indicators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['sex', 'shs', 'hsg', 'scl', 'clg', 'ad', 'mw', 'so', 'we', 'ne', 'exp1',\n",
      "       'exp2', 'exp3', 'exp4', 'occ', 'occ2', 'ind', 'ind2'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "Z = data.drop(columns=['lwage', 'wage']) # regressors\n",
    "print(Z.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Oo2wY8oIWitc",
    "papermill": {
     "duration": 0.036941,
     "end_time": "2021-07-22T21:33:21.171426",
     "exception": false,
     "start_time": "2021-07-22T21:33:21.134485",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The following figure shows the weekly wage distribution from the US survey data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['wage'], bins=35, label='hourly wage')\n",
    "plt.xlabel('hourly wage')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Empirical wage distribution from the US survey data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XCwz2ZNoWitg",
    "papermill": {
     "duration": 0.038762,
     "end_time": "2021-07-22T21:33:21.629360",
     "exception": false,
     "start_time": "2021-07-22T21:33:21.590598",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Wages show a high degree of skewness. Hence, wages are transformed in almost all studies by\n",
    "the logarithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cp2Tf15pWiti",
    "papermill": {
     "duration": 0.038159,
     "end_time": "2021-07-22T21:33:21.706383",
     "exception": false,
     "start_time": "2021-07-22T21:33:21.668224",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jjN31ZYbWitj",
    "papermill": {
     "duration": 0.038644,
     "end_time": "2021-07-22T21:33:21.783461",
     "exception": false,
     "start_time": "2021-07-22T21:33:21.744817",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Due to the skewness of the data, we are considering log wages which leads to the following regression model\n",
    "\n",
    "$$log(wage) = g(Z) + \\epsilon.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jNsHcgDTWitl",
    "papermill": {
     "duration": 0.038767,
     "end_time": "2021-07-22T21:33:21.861283",
     "exception": false,
     "start_time": "2021-07-22T21:33:21.822516",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We will estimate the two sets of prediction rules: Linear and Nonlinear Models.\n",
    "In linear models, we estimate the prediction rule of the form\n",
    "\n",
    "$$\\hat g(Z) = \\hat \\beta'X.$$\n",
    "Again, we generate $X$ in two ways:\n",
    " \n",
    "1. Basic Model:   $X$ consists of a set of raw regressors (e.g. gender, experience, education indicators, regional indicators).\n",
    "\n",
    "\n",
    "2. Flexible Model:  $X$ consists of all raw regressors from the basic model plus occupation and industry indicators, transformations (e.g., ${exp}^2$ and ${exp}^3$) and additional two-way interactions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DpTuFFQ-Witn",
    "papermill": {
     "duration": 0.038121,
     "end_time": "2021-07-22T21:33:21.938272",
     "exception": false,
     "start_time": "2021-07-22T21:33:21.900151",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To evaluate the out-of-sample performance, we split the data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1234)\n",
    "training = np.random.choice(data.index, size=int(len(data)*(3/4)), replace=False)\n",
    "\n",
    "data_train = data.loc[training,:]\n",
    "data_test = data.drop(training, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s0AlDE-nWitp",
    "papermill": {
     "duration": 0.039021,
     "end_time": "2021-07-22T21:33:22.115014",
     "exception": false,
     "start_time": "2021-07-22T21:33:22.075993",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We construct the two different model matrices $X_{basic}$ and $X_{flex}$ for both the training and the test sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "import patsy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "formula_basic =  \"lwage ~ sex + exp1 + exp2+ shs + hsg+ scl + clg + mw + so + we + occ2+ ind2\"\n",
    "formula_flex = \"lwage ~ sex + exp1 + exp2 + shs+hsg+scl+clg+occ2+ind2+mw+so+we + (exp1+exp2+exp3+exp4)*(shs+hsg+scl+clg+occ2+ind2+mw+so+we)\"\n",
    "\n",
    "y_basic_train, x_basic_train = patsy.dmatrices(formula_basic, data_train, return_type='dataframe')\n",
    "y_basic_test, x_basic_test = patsy.dmatrices(formula_basic, data_test, return_type='dataframe')\n",
    "p_basic = x_basic_train.shape[ 1 ]\n",
    "\n",
    "y_flex_train, x_flex_train = patsy.dmatrices(formula_flex, data_train, return_type='dataframe')\n",
    "y_flex_test, x_flex_test = patsy.dmatrices(formula_flex, data_test, return_type='dataframe')\n",
    "p_flex = x_flex_train.shape[ 1 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = data_train['lwage']\n",
    "Y_test = data_test['lwage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "execution": {
     "iopub.execute_input": "2021-07-22T21:33:22.429312Z",
     "iopub.status.busy": "2021-07-22T21:33:22.428202Z",
     "iopub.status.idle": "2021-07-22T21:33:22.445258Z",
     "shell.execute_reply": "2021-07-22T21:33:22.443989Z"
    },
    "executionInfo": {
     "elapsed": 39,
     "status": "ok",
     "timestamp": 1658250110923,
     "user": {
      "displayName": "Jhon Kevin Flores Rojas",
      "userId": "10267608749788811245"
     },
     "user_tz": 300
    },
    "id": "HpKaJlTgWitt",
    "outputId": "df8c7f4f-e0bb-4c62-de65-b3ed914c9994",
    "papermill": {
     "duration": 0.059354,
     "end_time": "2021-07-22T21:33:22.445413",
     "exception": false,
     "start_time": "2021-07-22T21:33:22.386059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 51)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_basic, p_flex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qUvQOieNWitu",
    "papermill": {
     "duration": 0.039589,
     "end_time": "2021-07-22T21:33:22.525196",
     "exception": false,
     "start_time": "2021-07-22T21:33:22.485607",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As known from our first lab, the basic model consists of $10$ regressors and the flexible model of $246$ regressors. Let us fit our models to the training sample using the two different model specifications. We are starting by running a simple ols regression. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QpogkfgpWitu",
    "papermill": {
     "duration": 0.039781,
     "end_time": "2021-07-22T21:33:22.604883",
     "exception": false,
     "start_time": "2021-07-22T21:33:22.565102",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### OLS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9VZOD13IWitu",
    "papermill": {
     "duration": 0.039692,
     "end_time": "2021-07-22T21:33:22.684509",
     "exception": false,
     "start_time": "2021-07-22T21:33:22.644817",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We fit the basic model to our training data by running an ols regression and compute the mean squared error on the test sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Intercept</th>\n",
       "      <th>sex</th>\n",
       "      <th>exp1</th>\n",
       "      <th>exp2</th>\n",
       "      <th>shs</th>\n",
       "      <th>hsg</th>\n",
       "      <th>scl</th>\n",
       "      <th>clg</th>\n",
       "      <th>mw</th>\n",
       "      <th>so</th>\n",
       "      <th>we</th>\n",
       "      <th>occ2</th>\n",
       "      <th>ind2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4595</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5009</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3730</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Intercept  sex  exp1  exp2  shs  hsg  scl  clg   mw   so   we  occ2  \\\n",
       "4595        1.0  1.0  12.0  1.44  0.0  1.0  0.0  0.0  0.0  0.0  1.0   1.0   \n",
       "5009        1.0  1.0  12.0  1.44  0.0  0.0  0.0  0.0  0.0  0.0  1.0   5.0   \n",
       "3730        1.0  1.0  12.0  1.44  0.0  1.0  0.0  0.0  0.0  1.0  0.0  16.0   \n",
       "179         1.0  0.0  29.0  8.41  0.0  0.0  0.0  1.0  0.0  0.0  0.0   8.0   \n",
       "1323        1.0  1.0  12.0  1.44  0.0  1.0  0.0  0.0  1.0  0.0  0.0  22.0   \n",
       "\n",
       "      ind2  \n",
       "4595   8.0  \n",
       "5009  17.0  \n",
       "3730   9.0  \n",
       "179   14.0  \n",
       "1323   5.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_basic_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_lm_basic = smf.ols(formula_basic, data = data_train).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean squared error (MSE) using the basic model is equal to 0.25113575025351914\n"
     ]
    }
   ],
   "source": [
    "yhat_lm_basic = fit_lm_basic.predict(data_test)\n",
    "print(\"The mean squared error (MSE) using the basic model is equal to\", ((Y_test - yhat_lm_basic)**2).mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aUdqcpXAWitw",
    "papermill": {
     "duration": 0.056431,
     "end_time": "2021-07-22T21:33:23.026875",
     "exception": false,
     "start_time": "2021-07-22T21:33:22.970444",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To determine the out-of-sample $MSE$ and the standard error in one step, we can use the function *lm*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coef.       0.251136\n",
       "Std.Err.    0.016115\n",
       "Name: const, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_basic  = (Y_test - yhat_lm_basic)**2\n",
    "\n",
    "MSE_lm_basic = sm.OLS( resid_basic , np.ones( resid_basic.shape[0] ) ).fit().summary2().tables[1].iloc[0, 0:2]\n",
    "MSE_lm_basic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WSJe5AWiWitw",
    "papermill": {
     "duration": 0.040507,
     "end_time": "2021-07-22T21:33:23.223781",
     "exception": false,
     "start_time": "2021-07-22T21:33:23.183274",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We also compute the out-of-sample $R^2$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2021-07-22T21:33:23.310177Z",
     "iopub.status.busy": "2021-07-22T21:33:23.308577Z",
     "iopub.status.idle": "2021-07-22T21:33:23.323143Z",
     "shell.execute_reply": "2021-07-22T21:33:23.321848Z"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1658250112095,
     "user": {
      "displayName": "Jhon Kevin Flores Rojas",
      "userId": "10267608749788811245"
     },
     "user_tz": 300
    },
    "id": "6iCw0TS4Witw",
    "outputId": "b7a5abc6-be2d-4852-9c68-dffdd5ce07ce",
    "papermill": {
     "duration": 0.058991,
     "end_time": "2021-07-22T21:33:23.323276",
     "exception": false,
     "start_time": "2021-07-22T21:33:23.264285",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R^2 using the basic model is equal to, 0.2335579652253298\n"
     ]
    }
   ],
   "source": [
    "R2_lm_basic = 1 - ( MSE_lm_basic[0]/Y_test.var() )\n",
    "print( f\"The R^2 using the basic model is equal to, {R2_lm_basic}\" ) # MSE OLS (basic model) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OxxeDdauWity",
    "papermill": {
     "duration": 0.040697,
     "end_time": "2021-07-22T21:33:23.404884",
     "exception": false,
     "start_time": "2021-07-22T21:33:23.364187",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We repeat the same procedure for the flexible model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R^2 using the flex model is equal to, 0.22646565124243312\n"
     ]
    }
   ],
   "source": [
    "# ols (flex model)\n",
    "lm_flex = sm.OLS(Y_train, x_flex_train)\n",
    "fit_lm_flex = lm_flex.fit()\n",
    "\n",
    "yhat_lm_flex = fit_lm_flex.predict(x_flex_test)\n",
    "\n",
    "resid_flex = (Y_test-yhat_lm_flex)**2\n",
    "\n",
    "MSE_lm_flex = sm.OLS(resid_flex , np.ones(resid_flex.shape[0])).fit().summary2().tables[1].iloc[0, 0:2]\n",
    "MSE_lm_flex\n",
    "\n",
    "R2_lm_flex = 1 - (MSE_lm_flex[0]/Y_test.var())\n",
    "print(f\"The R^2 using the flex model is equal to, {R2_lm_flex}\") # MSE OLS (flex model) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "smBG74geWity",
    "papermill": {
     "duration": 0.055232,
     "end_time": "2021-07-22T21:33:23.764568",
     "exception": false,
     "start_time": "2021-07-22T21:33:23.709336",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We observe that ols regression works better for the basic model with smaller $p/n$ ratio. We are proceeding by running lasso regressions and its versions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4gRVP4umWitz",
    "papermill": {
     "duration": 0.041825,
     "end_time": "2021-07-22T21:33:23.870407",
     "exception": false,
     "start_time": "2021-07-22T21:33:23.828582",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Lasso, Ridge and Elastic Net\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8_l0nfHyWitz",
    "papermill": {
     "duration": 0.04152,
     "end_time": "2021-07-22T21:33:23.953937",
     "exception": false,
     "start_time": "2021-07-22T21:33:23.912417",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Considering the basic model, we run a lasso/post-lasso regression first and then we compute the measures for the out-of-sample performance. Note that applying the package *hdm* and the function *rlasso* we rely on a theory-based choice of the penalty level $\\lambda$ in the lasso regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hdmpy as hdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    }
   ],
   "source": [
    "fit_rlasso = hdm.rlasso(x_basic_train.to_numpy() , Y_train.to_numpy().reshape( Y_train.size , 1 ) , post = False )\n",
    "fit_rlasso_post = hdm.rlasso(x_basic_train.to_numpy() , Y_train.to_numpy().reshape( Y_train.size , 1 ) , post = True )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have to apply those transfomations to original test data, `(post = False)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting mean of each variable\n",
    "meanx = x_basic_test.mean( axis = 0 ).values.\\\n",
    "                        reshape( x_basic_test.shape[ 1 ] , 1 )\n",
    "\n",
    "# Reducing the mean\n",
    "new_x1 = x_basic_test.to_numpy() - \\\n",
    "                    (np.ones( ( x_basic_test.shape[ 0 ] , 1 ) ) @ meanx.T)\n",
    "\n",
    "# Getting the significant variables\n",
    "x1_est_rlasso = new_x1[ :, fit_rlasso.est['index'].iloc[:, 0].to_list()]\n",
    "\n",
    "# Getting the coef. from significant variables\n",
    "beta_rlasso = fit_rlasso.est['beta'].loc[ fit_rlasso.est['index'].\\\n",
    "                                     iloc[:, 0].to_list(), ].to_numpy()\n",
    "\n",
    "# yhat\n",
    "yhat_rlasso = (x1_est_rlasso @ beta_rlasso) + np.mean( Y_test.to_numpy() )\n",
    "residuals_rlasso = Y_test.to_numpy().reshape( Y_test.to_numpy().size, 1)  - yhat_rlasso"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply those transfomations to original test data, `(post = True)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting mean of each variable\n",
    "meanx = x_basic_test.mean( axis = 0 ).values.\\\n",
    "                        reshape(x_basic_test.shape[ 1 ] , 1 )\n",
    "\n",
    "# Reducing the mean\n",
    "new_x1 = x_basic_test.to_numpy() - \\\n",
    "                    (np.ones( (x_basic_test.shape[ 0 ] , 1 ) ) @ meanx.T)\n",
    "\n",
    "# Getting the significant variables\n",
    "x1_est_rlasso_post = new_x1[ :, fit_rlasso_post.est['index'].iloc[:, 0].to_list()]\n",
    "\n",
    "# Getting the coef. from significant variables\n",
    "beta_rlasso_post = fit_rlasso_post.est['beta'].loc[ fit_rlasso_post.est['index'].\\\n",
    "                                     iloc[:, 0].to_list(), ].to_numpy()\n",
    "\n",
    "# yhat\n",
    "yhat_rlasso_post = (x1_est_rlasso_post @ beta_rlasso_post) + np.mean( Y_test.to_numpy() )\n",
    "residuals_rlasso_post = Y_test.to_numpy().reshape( Y_test.to_numpy().size, 1)  - yhat_rlasso_post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R^2 using the basic model is equal to 0.2089428603664083,for lasso and 0.21505140970720815 for post-lasso\n"
     ]
    }
   ],
   "source": [
    "MSE_lasso = sm.OLS( ( residuals_rlasso )**2 , np.ones( yhat_rlasso.size )  ).fit().summary2().tables[1].round(3)\n",
    "MSE_lasso_post = sm.OLS( ( residuals_rlasso_post )**2  , np.ones( yhat_rlasso_post.size )  ).fit().summary2().tables[1].round(3)\n",
    "\n",
    "R2_lasso = 1 - MSE_lasso.iloc[0, 0]/ np.var( Y_test )\n",
    "R2_lasso_post = 1 - MSE_lasso_post.iloc[0, 0]/ np.var( Y_test )\n",
    "\n",
    "print( f\"The R^2 using the basic model is equal to {R2_lasso},for lasso and {R2_lasso_post} for post-lasso\") # R^2 lasso/post-lasso (basic model) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4D3os7iXWit1",
    "papermill": {
     "duration": 0.051245,
     "end_time": "2021-07-22T21:33:24.660529",
     "exception": false,
     "start_time": "2021-07-22T21:33:24.609284",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, we repeat the same procedure for the flexible model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2021-07-22T21:33:24.749884Z",
     "iopub.status.busy": "2021-07-22T21:33:24.748227Z",
     "iopub.status.idle": "2021-07-22T21:33:27.945305Z",
     "shell.execute_reply": "2021-07-22T21:33:27.942260Z"
    },
    "executionInfo": {
     "elapsed": 315,
     "status": "ok",
     "timestamp": 1658250112397,
     "user": {
      "displayName": "Jhon Kevin Flores Rojas",
      "userId": "10267608749788811245"
     },
     "user_tz": 300
    },
    "id": "GWGYhDYUWit1",
    "outputId": "4f3718c4-a1ab-4126-8092-e921add22961",
    "papermill": {
     "duration": 3.242671,
     "end_time": "2021-07-22T21:33:27.945527",
     "exception": false,
     "start_time": "2021-07-22T21:33:24.702856",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    }
   ],
   "source": [
    "fit_rlasso_flex = hdm.rlasso(x_flex_train.to_numpy() , Y_train.to_numpy().reshape( Y_train.size , 1 ) , post = False )\n",
    "fit_rlasso_post_flex = hdm.rlasso(x_flex_train.to_numpy() , Y_train.to_numpy().reshape( Y_train.size , 1 ) , post = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting mean of each variable\n",
    "meanx = x_flex_test.mean( axis = 0 ).values.\\\n",
    "                        reshape(x_flex_test.shape[ 1 ] , 1 )\n",
    "\n",
    "# Reducing the mean\n",
    "new_x1 = x_flex_test.to_numpy() - \\\n",
    "                    (np.ones( (x_flex_test.shape[ 0 ] , 1 ) ) @ meanx.T)\n",
    "\n",
    "# Getting the significant variables\n",
    "x1_est_rlasso_flex = new_x1[ :, fit_rlasso_flex.est['index'].iloc[:, 0].to_list()]\n",
    "\n",
    "# Getting the coef. from significant variables\n",
    "beta_rlasso_flex = fit_rlasso_flex.est['beta'].loc[ fit_rlasso_flex.est['index'].\\\n",
    "                                     iloc[:, 0].to_list(), ].to_numpy()\n",
    "\n",
    "# yhat\n",
    "yhat_rlasso_flex = (x1_est_rlasso_flex @ beta_rlasso_flex) + np.mean( Y_test.to_numpy() )\n",
    "residuals_rlasso_flex = Y_test.to_numpy().reshape( Y_test.to_numpy().size, 1)  - yhat_rlasso_flex\n",
    "\n",
    "# Getting mean of each variable\n",
    "meanx = x_flex_test.mean( axis = 0 ).values.\\\n",
    "                        reshape(x_flex_test.shape[ 1 ] , 1 )\n",
    "\n",
    "# Reducing the mean\n",
    "new_x1 = x_flex_test.to_numpy() - \\\n",
    "                    (np.ones( (x_flex_test.shape[ 0 ] , 1 ) ) @ meanx.T)\n",
    "\n",
    "# Getting the significant variables\n",
    "x1_est_rlasso_post_flex = new_x1[ :, fit_rlasso_post_flex.est['index'].iloc[:, 0].to_list()]\n",
    "\n",
    "# Getting the coef. from significant variables\n",
    "beta_rlasso_post_flex = fit_rlasso_post_flex.est['beta'].loc[ fit_rlasso_post_flex.est['index'].\\\n",
    "                                     iloc[:, 0].to_list(), ].to_numpy()\n",
    "\n",
    "# yhat\n",
    "yhat_rlasso_post_flex = (x1_est_rlasso_post_flex @ beta_rlasso_post_flex) + np.mean( Y_test.to_numpy() )\n",
    "residuals_rlasso_post_flex = Y_test.to_numpy().reshape( Y_test.to_numpy().size, 1)  - yhat_rlasso_post_flex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R^2 using the basic model is equal to 0.2089428603664083,for lasso and 0.21505140970720815 for post-lasso\n"
     ]
    }
   ],
   "source": [
    "MSE_lasso_flex = sm.OLS( ( residuals_rlasso_flex )**2 , np.ones( yhat_rlasso_flex.size )  ).fit().summary2().tables[1].round(3)\n",
    "MSE_lasso_post_flex = sm.OLS( ( residuals_rlasso_post_flex )**2  , np.ones( yhat_rlasso_post_flex.size )  ).fit().summary2().tables[1].round(3)\n",
    "\n",
    "R2_lasso_flex = 1 - MSE_lasso.iloc[0, 0]/ np.var( Y_test )\n",
    "R2_lasso_post_flex = 1 - MSE_lasso_post_flex.iloc[0, 0]/ np.var( Y_test )\n",
    "\n",
    "print( f\"The R^2 using the basic model is equal to {R2_lasso_flex},for lasso and {R2_lasso_post_flex} for post-lasso\") # R^2 lasso/post-lasso (basic model) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p19rDtdYWit2",
    "papermill": {
     "duration": 0.043014,
     "end_time": "2021-07-22T21:33:28.156922",
     "exception": false,
     "start_time": "2021-07-22T21:33:28.113908",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In contrast to a theory-based choice of the tuning parameter $\\lambda$ in the lasso regression, we can also use cross-validation to determine the penalty level by applying the package *glmnet* and the function cv.glmnet. In this context, we also run a ridge and a elastic net regression by adjusting the parameter *alpha*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 using cross-validation for lasso, ridge, and elastic net in the basic model: 0.23032, 0.23338, 0.23032\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV, RidgeCV, ElasticNetCV\n",
    "\n",
    "fit_lasso_cv = LassoCV(cv=10, fit_intercept=True, normalize=False, random_state=0).fit(x_basic_train, Y_train)\n",
    "fit_ridge = RidgeCV(cv=10, fit_intercept=True, normalize=False, scoring=None).fit(x_basic_train, Y_train)\n",
    "fit_elnet = ElasticNetCV(cv=10, fit_intercept=True, normalize=False, random_state=0).fit(x_basic_train, Y_train)\n",
    "\n",
    "yhat_lasso_cv = fit_lasso_cv.predict(x_basic_test)\n",
    "yhat_ridge = fit_ridge.predict(x_basic_test)\n",
    "yhat_elnet = fit_elnet.predict(x_basic_test)\n",
    "\n",
    "residual_lasso = (yhat_lasso_cv - Y_test)**2\n",
    "residual_ridge = (yhat_ridge - Y_test)**2\n",
    "residual_elnet = (yhat_elnet - Y_test)**2\n",
    "\n",
    "MSE_lasso_cv = sm.OLS(residual_lasso, np.ones(Y_test.size)).fit().summary2().tables[1].round(3)\n",
    "MSE_ridge = sm.OLS(residual_ridge, np.ones(Y_test.size)).fit().summary2().tables[1].round(3)\n",
    "MSE_elnet = sm.OLS(residual_elnet, np.ones(Y_test.size)).fit().summary2().tables[1].round(3)\n",
    "\n",
    "# R2_lasso_cv_flex = 1 - MSE_lasso_cv.iloc[0, 0] / np.var(Y_test)\n",
    "# R2_ridge_flex = 1 - MSE_ridge.iloc[0, 0] / np.var(Y_test)\n",
    "# R2_elnet_flex = 1 - MSE_elnet.iloc[0, 0] / np.var(Y_test)\n",
    "\n",
    "# yhat_lasso_cv = fit_lasso_cv.predict(x_basic_test)\n",
    "# yhat_ridge = fit_ridge.predict(x_basic_test)\n",
    "# yhat_elnet = fit_elnet.predict(x_basic_test)\n",
    "\n",
    "# MSE_lasso_cv = mean_squared_error(Y_test, yhat_lasso_cv)\n",
    "# MSE_ridge = mean_squared_error(Y_test, yhat_ridge)\n",
    "# MSE_elnet = mean_squared_error(Y_test, yhat_elnet)\n",
    "\n",
    "R2_lasso_cv = 1 - MSE_lasso_cv.iloc[0, 0] / np.var(Y_test)\n",
    "R2_ridge = 1 - MSE_ridge.iloc[0, 0]  / np.var(Y_test)\n",
    "R2_elnet = 1 - MSE_elnet.iloc[0, 0]  / np.var(Y_test)\n",
    "\n",
    "print(\"R^2 using cross-validation for lasso, ridge, and elastic net in the basic model: {:.5f}, {:.5f}, {:.5f}\".format(R2_lasso_cv, R2_ridge, R2_elnet))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HXyV0kqUWit4",
    "papermill": {
     "duration": 0.04689,
     "end_time": "2021-07-22T21:33:30.403846",
     "exception": false,
     "start_time": "2021-07-22T21:33:30.356956",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Note that the following calculations for the flexible model require significant computation time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16581324345997928, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1678078988946936, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16363316256570215, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0669461177604944, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.952454960287582, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0254332839571134, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11377039412604972, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.23576360495599147, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6531991654060221, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.477089604166963, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.836746741327488, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.065939423035161, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6554591172340452, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.20966200716986805, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13309604053551993, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11794256421603677, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1256861208735245, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.886834080940048, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14618838621640862, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12040692123980534, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.110812312848111, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.257921011946792, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1665494585123497, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7770228923302511, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1383647329130326, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4633162003989355, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2972033114853048, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8616429255878302, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6999516734695135, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5936668574936448, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.526916641891944, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12214773876098661, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2736574180237312, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.30469045001621, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.20083609904088462, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8764359533100787, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.000957542692049, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.154183121740971, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.32704784394923, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1555860529998654, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4194941685620961, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6796281039084988, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9530745774059142, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.234474299518979, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5194791836677268, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7407905811140836, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1266336469120688, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17720183108190213, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2617631893617727, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15269301267835544, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.19388020367034642, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5820282213978771, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14150156849927953, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9908064002902393, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.745532807448285, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.866682422780514, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.227517161493097, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4632367244394118, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1815014062483442, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4878934889054563, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5725247205748474, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17011945947365348, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13472262226071052, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12755182809178223, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13735801699203876, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14604436090564832, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14402223188176322, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15354137573910975, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17779146690293146, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.24937836699348281, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2516668005090423, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5448464852714778, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8014436381233736, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.463041600168026, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.894213086437958, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.36994279587702295, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6861121734996232, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9777115606231064, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2677446193963533, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5534770099725392, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1368627560477762, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1467587142967659, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18195356093531245, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1927724312419059, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1452876146311155, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16185690591294133, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.826636506248633, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13936487135651987, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13735754005415401, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3855087305092866, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7058182937710171, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0292113018437021, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3620456868278552, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7009680695089173, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7753423647986892, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.833292086733536, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0906584780692583, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1292533944169918, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12799189354052487, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1245015580063864, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11722771249242214, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12136076168405907, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16308825144813, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11902208133676595, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2317021411666929, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3054274729732924, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11390263614043761, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5441917657674367, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7529097568460656, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.23092481587207203, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15787525256951085, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4839459828950794, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5533286433844751, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6167134629240536, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6778649519341116, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7362717527639688, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.792051660568859, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8519522896310718, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9123992606507727, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9755423794861144, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12189680941230563, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2198052765184002, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.29240821226267144, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1545466824182995, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8839346653486473, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1975537586904466, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.922712348796267, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.84354382331935, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.063972019346238, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3511790586809411, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0673909294055193, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3938393073088946, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.722174906157079, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16125336759239417, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1605161698179245, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.19336911231857812, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16120199758677245, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12614875658675828, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13229736026266892, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16589454435938933, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.998612542715591, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.141895138211112, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.536690811828066, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9940134006219523, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.287266494313144, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6444150418210484, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9813883660150395, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.283820875702986, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5687386857265437, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6290113582807635, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17242735627326056, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22058627043440993, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15389174804522554, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16686905884580483, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1635101980118634, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15928078604770235, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1297701005047429, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3182896676554492, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3173031545163667, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9778242147017409, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8373872132427778, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.1955533075877156, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6432843755288786, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15278277908646487, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9625861251090555, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26.019893365242638, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.528e+00, tolerance: 1.253e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18116187993257427, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16568667140586513, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16194811580646729, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.539481903327328, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.512763799536742, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.478891695863922, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12762751562081576, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4512982051792278, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1947136436151595, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.519907573820774, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8126186692169313, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.074527700913791, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11714854330750768, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1207635653951229, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11317892031263455, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12081842307713941, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.247076046782013, tolerance: 0.11146464292939776\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16555830996230725, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12102643185767192, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.7227217174123552, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.889081842176779, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3498521864136137, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9133138471313487, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2372447348981268, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0719197228551138, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8635438761281193, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7492384716562128, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6299326519452961, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5498579296527168, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.499850173031291, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4685416274388672, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.24875921851389649, tolerance: 0.11323123739388508\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17396473498115483, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2980721666356203, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.20233028693519373, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.264832298428928, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.078917923253357, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.375415451833078, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.916915693580563, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2499538740036087, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5288912344143455, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.776387781313133, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0318069524024622, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2902165786813384, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.548635561183346, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7929986684570167, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15109750812621314, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11799942015056786, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.26628761091672004, tolerance: 0.11281840967457779\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1471314086942357, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14460663843487964, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4674588907314501, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2300303310308891, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4989325223865535, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.49200205633781, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.542164771648117, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5518103105155205, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5896010143105741, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3102402309787067, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.5884392692016718, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2598918228918592, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18620071448140152, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13617313408099108, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12821246047394652, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13809667582552265, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14382572250315206, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1419800088502825, tolerance: 0.11267944581523541\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17182193811140678, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2172774391611938, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.340137317726203, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6410230365850111, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2730019185407855, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.274660137309411, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6721792098779815, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.2595567091060502, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5107083328754243, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7734941947900325, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.043084024921086, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.309097276971329, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9028566205627158, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.19925414558508692, tolerance: 0.11365509563329593\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17782097312999667, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.190076219393859, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14806314299653422, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5408852623276061, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4.348686980681691, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4253750327235366, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.17323202913348723, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5257077811693307, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8261582682545168, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1309971535388286, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4395096785832493, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.749111469981699, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8575062297293243, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.075488546264637, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8004832844180783, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1453107744761155, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1255240502275683, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12096200451787809, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11531352192139366, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11672140368295914, tolerance: 0.11333220073744452\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16005606941564565, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12623146092892057, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22489674701955664, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5246617132191886, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13971750296320806, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9716122686530753, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11798143065925615, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1955429148449639, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.5973916400097323, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.734376482946459, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7771493744756981, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8176975994470013, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8558047616550084, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.8917498885958821, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9322203681589372, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.9783950714343064, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12392801366377171, tolerance: 0.11331487742204527\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.11898348091324351, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.22200954363245273, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.772843566056963, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.52573464597549, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.79146337115219, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.704335225471937, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.409807714399335, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.6987346078805103, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.154158552062313, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4521143400922938, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.171242635255112, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4727806230134775, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.771815134706344, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.13239319931881255, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18635833476514563, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.18134784762946765, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12510941201833248, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1266504859638644, tolerance: 0.11167322032275435\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.15957272050229676, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.749372630847688, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.781100090374252, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.506120092826109, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.21605285482132786, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4146611602993744, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.7639789877231351, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0968953033544722, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3879993852192456, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6652435855717158, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7232366393251368, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1273622266228358, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.20939798792437614, tolerance: 0.11436955122138627\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14836990488606716, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.16413325117900968, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1615834970115202, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.1580561996936467, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.12811952811716765, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.310180357037325, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.24923019512414157, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.43620186255395765, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0791375881092335, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9213790812433444, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.2432488517969205, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.214093848724474, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.14414188996465782, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.225788828593636, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:633: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27.13743091362778, tolerance: 0.11149001330224016\n",
      "  model = cd_fast.enet_coordinate_descent_gram(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "c:\\Python38\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.367e+00, tolerance: 1.253e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 using cross-validation for lasso, ridge, and elastic net in the basic model: 0.230, 0.233, 0.230\n"
     ]
    }
   ],
   "source": [
    "fit_lasso_cv_flex = LassoCV(cv=10, fit_intercept=True, normalize=False, random_state=0).fit(x_flex_train, Y_train)\n",
    "fit_ridge_flex = RidgeCV(cv=10, fit_intercept=True, normalize=False, scoring=None).fit(x_flex_train, Y_train)\n",
    "fit_elnet_flex = ElasticNetCV(cv=10, fit_intercept=True, normalize=False, random_state=0).fit(x_flex_train, Y_train)\n",
    "\n",
    "yhat_lasso_cv_flex = fit_lasso_cv_flex.predict(x_flex_test)\n",
    "yhat_ridge_flex = fit_ridge_flex.predict(x_flex_test)\n",
    "yhat_elnet_flex = fit_elnet_flex.predict(x_flex_test)\n",
    "\n",
    "residual_lasso_flex = (yhat_lasso_cv_flex - Y_test)**2\n",
    "residual_ridge_flex = (yhat_ridge_flex - Y_test)**2\n",
    "residual_elnet_flex = (yhat_elnet_flex - Y_test)**2\n",
    "\n",
    "MSE_lasso_cv_flex = sm.OLS(residual_lasso_flex, np.ones(Y_test.size)).fit().summary2().tables[1].round(3)\n",
    "MSE_ridge_flex = sm.OLS(residual_ridge_flex, np.ones(Y_test.size)).fit().summary2().tables[1].round(3)\n",
    "MSE_elnet_flex = sm.OLS(residual_elnet_flex, np.ones(Y_test.size)).fit().summary2().tables[1].round(3)\n",
    "\n",
    "R2_lasso_cv_flex = 1 - MSE_lasso_cv_flex.iloc[0, 0] / np.var(Y_test)\n",
    "R2_ridge_flex = 1 - MSE_ridge_flex.iloc[0, 0] / np.var(Y_test)\n",
    "R2_elnet_flex = 1 - MSE_elnet_flex.iloc[0, 0] / np.var(Y_test)\n",
    "\n",
    "print(\"R^2 using cross-validation for lasso, ridge, and elastic net in the basic model: {:.3f}, {:.3f}, {:.3f}\".format(R2_lasso_cv, R2_ridge, R2_elnet))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TjeCfaakWit6",
    "papermill": {
     "duration": 0.043216,
     "end_time": "2021-07-22T21:33:44.229372",
     "exception": false,
     "start_time": "2021-07-22T21:33:44.186156",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The performance of the lasso regression with cross-validated penalty is quite similar to the performance of lasso using a theoretical based choice of the tuning parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ypNrjgnwWit6",
    "papermill": {
     "duration": 0.043279,
     "end_time": "2021-07-22T21:33:44.316231",
     "exception": false,
     "start_time": "2021-07-22T21:33:44.272952",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Non-linear models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qkm_TFteWit6",
    "papermill": {
     "duration": 0.043761,
     "end_time": "2021-07-22T21:33:44.403514",
     "exception": false,
     "start_time": "2021-07-22T21:33:44.359753",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Besides linear regression models, we consider nonlinear regression models to build a predictive model. We are apply regression trees, random forests, boosted trees and neural nets to estimate the regression function $g(X)$. First, we load the relevant libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s8QdDVokWit7",
    "papermill": {
     "duration": 0.043594,
     "end_time": "2021-07-22T21:33:44.840683",
     "exception": false,
     "start_time": "2021-07-22T21:33:44.797089",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "and we illustrate the application of regression trees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "npHGkYKsWit7",
    "papermill": {
     "duration": 0.043219,
     "end_time": "2021-07-22T21:33:44.927600",
     "exception": false,
     "start_time": "2021-07-22T21:33:44.884381",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Regression Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GBDE9z5yWit7",
    "papermill": {
     "duration": 0.043147,
     "end_time": "2021-07-22T21:33:45.014486",
     "exception": false,
     "start_time": "2021-07-22T21:33:44.971339",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We fit a regression tree to the training data using the basic model. The variable *cp* controls the complexity of the regression tree, i.e. how deep we build the tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_trees = DecisionTreeRegressor(random_state = 0, min_impurity_decrease = 0.001)\n",
    "fit_trees.fit(x_basic_train, y_basic_train)\n",
    "from sklearn.tree import plot_tree\n",
    "\n",
    "plt.figure(figsize=(30, 20))\n",
    "plot_tree(fit_trees, filled=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G3QZT03wWit8",
    "papermill": {
     "duration": 0.047977,
     "end_time": "2021-07-22T21:33:46.929812",
     "exception": false,
     "start_time": "2021-07-22T21:33:46.881835",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "An important method to improve predictive performance is called \"Pruning the Tree\". This\n",
    "means the process of cutting down the branches of a tree. We apply pruning to the complex tree above to reduce the depth. Initially, we determine the optimal complexity of the regression tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ccp_alphas</th>\n",
       "      <th>impurities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.627846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001011</td>\n",
       "      <td>11.628857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001024</td>\n",
       "      <td>11.629881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001024</td>\n",
       "      <td>11.630906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001036</td>\n",
       "      <td>11.631941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ccp_alphas  impurities\n",
       "0    0.000000   11.627846\n",
       "1    0.001011   11.628857\n",
       "2    0.001024   11.629881\n",
       "3    0.001024   11.630906\n",
       "4    0.001036   11.631941"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.DataFrame(fit_trees.cost_complexity_pruning_path(y_basic_train, x_basic_train ))\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IG7NtCahWit9",
    "papermill": {
     "duration": 0.04778,
     "end_time": "2021-07-22T21:33:47.142808",
     "exception": false,
     "start_time": "2021-07-22T21:33:47.095028",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, we can prune the tree and visualize the prediction rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jRxMQEMDo0aNRVhZvFWWhSZMm3Lx5kzVr1nD06FEcHBz4888/kUgkig5N+MCJV7ggCISHhzNo0CA6deqEkZERXl5e/PXXXxgYGCg6tEpHRUWFCRMmEBAQwNChQ5k2bRr169fn8uXLig5N+ICJZEAQKrHc3FwWLFiAs7MzV65cYfv27Vy+fBk3NzdFh1bpGRoasmbNGm7duoW2tjZt2rRh+PDhREdHKzo04QMkkgFBqKROnTpFnTp1+P777/nss8/w9/dnxIgRKCkpKTo04TkNGzbk2rVrbNy4kTNnzuDo6MiSJUvIz89XdGjCB0QkA4JQyYSEhNC3b1+6deuGpaUlDx484Pfff0dfX1/RoQkvoayszJgxYwgICGDUqFF88803uLm5ce7cOUWHJnwgRDIgCJVEdnY2P/74I7Vr1+bOnTv8/fffnDt3DhcXF0WHJryhqlWrsnz5cry8vDA0NKRjx44MHjyYiIgIRYcmVHAiGRCED5xcLufIkSO4uLgwf/58pkyZgp+fHx999JG4JFBBubm5cfnyZbZt28bly5dxcnJi4cKF5ObmKjo0oYISyYAgfMCCgoLo2bMnffr0wd7eHh8fHxYuXIiurq6iQxPek5KSEh9//DEBAQFMmDCBb7/9ljp16nD69GlFhyZUQCIZEIQPUFZWFt9++y0uLi48fPiQf/75h9OnT+Po6Kjo0IQSpq+vz9KlS7l//z7m5uZ07dqVfv36ERoaqujQhApEJAOC8AGRy+UcOHAAZ2dnfvvtN2bOnImvry/9+/cXlwQ+cK6urly4cIHdu3dz69YtnJ2d+emnn8jJyVF0aEIFIJIBQfhA+Pv706VLFwYMGICrqyuPHj3i559/RltbW9GhCWVESUmJIUOG4O/vz5dffsnPP/+Mi4sLx44dU3RoQjknkgFBqOAyMjKYNWsWderUISgoiCNHjnD8+HHs7e0VHZqgILq6uixatAgfHx9q1KhBr1696NWrF0+ePFF0aEI5JZIBQaig5HI5e/bswcnJiT///JNvv/2WR48e0atXL0WHJpQTTk5OnDlzhv379/PgwQNcXFz4/vvvycrKUnRoQjkjkgFBqIAePXpEhw4dGDJkCI0aNeLx48d8//33aGlpKTo0oZxRUlJiwIAB+Pr6Mn36dBYtWkTt2rU5ePAgcrlc0eEJ5YRIBgShAklLS+Prr7+mXr16REZGcvLkSQ4ePIidnZ2iQxPKOR0dHX755RcePnxI7dq16d+/P127diUgIEDRoQnlgEgGBKECkMvl7NixA0dHR9asWcNPP/2Ej48PXbt2VXRoQgVTq1Ytjh8/zuHDhwkICMDV1ZXZs2eTmZmp6NAEBVKSi3EiQSjXHjx4wBdffMGVK1cYOHAgv//+O9bW1ooOS/gAZGdns3jxYhYuXIiRkRG///47gwYNEmWolZAYGRCEciolJYUvv/yS+vXrk5CQwNmzZ9m3b59IBIQSo6Wlxbx583j8+DH169fno48+omPHjjx+/FjRoQllTCQDglDOyGQyNm/ejIODA5s3b2bRokU8ePCAjh07Kjo04QNlZ2fH4cOHOX78OOHh4bi5uTF9+nTS0tIUHZpQRsRlAkEoR7y8vJg0aRI3btxg2LBhLF68GAsLC0WHJVQiubm5/P777/zyyy8YGBjw22+/MWzYMHHp4AMnRgYEoRxISkri888/p2HDhmRkZHDx4kV27twpEgGhzGloaDBnzhz8/Pxo0aIFI0aMoG3btvj4+Cg6NKEUiWRAEBRIKpWybt06HBwc2LVrF3/88QdeXl60adNG0aEJlZy1tTX79u3jzJkzxMbG4u7uzpQpU0hJSVF0aEIpEMmAICjIrVu3aNq0KRMmTKBnz574+/szZcoU1NTUFB2aIBTq1KkT3t7e/Prrr2zcuBFHR0e2bNmCTCZTdGhCCRLJgCCUsfj4eD799FOaNm2KRCLhypUrbNmyBTMzM0WHJggvpK6uzowZM/D396dDhw6MHj2ali1b4uXlpejQhBIikgFBKCNSqZSVK1fi4ODA/v37WbFiBXfu3KFFixaKDk0Q3oiFhQW7du3iwoULpKWl0bBhQyZOnEhSUpKiQxPek0gGBKEUxcbGAnDt2jUaNmzI5MmTGThwIAEBAUycOBEVFRUFRygIb69t27bcu3ePpUuXsnPnThwcHFi/fj0ymYykpCTy8vIUHaLwlkRpoSCUkj///JMZM2YwZMgQtm/fTsOGDVm5ciWNGzdWdGiCUGKePn3KzJkz2b59O40aNUJDQwNdXV1OnDghyhErEJEMCEIpCAoKwsXFBZlMhq6uLosXL2bMmDFiJED4YF25coUJEyYUrl64atUqPv/8cwVHJbwpkQwIQilo2LAhd+/eLfz54cOHuLi4KDAiQSh91tbWREREAAWTDnNzcxUckfCmVBUdgCB8iJYvX87169dxc3PDwsICJycnRYckCKXu1q1bBAQEEBwcrOhQhLckRgaEUhMeHk5CQoKiwyi3jIyMxKZDQoUlXt+vVtFe32JkQCgV4eHhODs5kZWdrehQyi1tLS18/fwq1BuGIMC/r29nZ7KyshQdSrmlra2Nr69vhXl9i2RAKBUJCQlkZWezekJHHMyrKTqccicgOonP13qQkJBQYd4sBOGZhIQEsrKy2PzHzzjZ2yk6nHLHLyiE0dO+q1Cvb5EMCKXKwbwabrbGig5DEIRS4GRvh7urmA/zIRCLDgmCIAhCJSdGBoRy6YJPOIkZOZhX1SU0LpWIxHRcrAzp6m7H70fukJaVy/zhrbjgE45PeAKhcaks+rg11wNiOH0vhPnDW72wXYlUhqrKy3Pg7Rcfc+ZBKNundAcgLD6NT1edZt/03lTR0QBg0cFb6GioYW2sR+9G9iX/4AWhEjl7+QaJySlYmJkQHB5JeFQMdZxq0b5FY7buO0L003h0dLQYObA3U75fyMGNy17YjkQiQVX15X/SvHx8OXLmIplZWcz/5kvU1Qs2BBs3/QfcajvQ2L0OTdzrlMZDrBDEyIBQLrWrY01gdDLH7jxhWGtnAFo5W6Kqosw3/RoXOe7LHvXR1lAjTyqjdW3LYm09jkhkzekH/HXci5jkDO4EPWXN6QeFX0kZOYXHfty2NtZGegBk5uZz5HYQHeraFGnPSE8LJSXIyZOWxkMXhEqlU+um+AWFcOjUeT4Z1BuAts0boa+ny+Qxw9DW1uSTQb2xtjCjpo1VkfsmJqewbf9RlqzZwplL10lLz2D5pl2FX1du3Ss8dv+xM3w3dTxtmjbk2t0HhbebGRuSnSPWQxDJgFAuyWRyUjJzkchkSF+zVerm8w9p52qFjkbxrX/vhcSx6OAtrI30mNDZDSsj/TeO4apvFBKpjLtPYrn0OKLw9rEd6zCpmzsPQuPJl4iEQBDeh0wmIyU1DYlEilRa9PUkkUiIS0jCyvzFO3pOnbeYp3EJDO/fk+4dXjwa+Dq/fDOZmRNHs/vgiXe6/4dCXCYQyqW1Zx7wcdvaqCorsfrUgyK/23TOB++wBO4EPcUnPIHLjyJp4lCdBjVNMdDRLHKsu50JW7/sRmBMMlsvPKSTmy0N7c1oaP/iN5eTXiF4hyVw5HYQvRvZ07meLXmSW7SpbcUV3yhMq2jzKCKB4NhU1FWVUVMVywsLwvtYsXk3Y4b0Q1VVhb827iryuyNnLtKrU5uX3nf7XwtITcvgxHlPdLW16NW5LZPHDHvhsQN7duaXP9eTmZXFLzMns37nfj4dPpA/1m0nLSOTOs4OJfq4Khqx6JBQKry8vGjQoAHnfhxcItUEB24Eoq2hSlf3V5cxXX4cSXh8GiPa1H7vPkvTg9B4Oszby927d6lfv76iwxGEt/Ls9X396I4SrybYe/Q0Olpa9OjYusjt4VFPWbdzP7/M/KJE+ysN9x760azXiAr1+hYjA0KF0L9prTc67kVzBgRBqDgG9+rywtutLcwqRCJQUYlkQKjQUjNzWXPmQZFJhaXp7INQHoYnkJ6dz/eDmxXevveqPzHJGehoqPFRSycWHbyFmooyw1o5k5Gbz9Ijd5jZtxF1bMSaC4LwplLS0lm+aRffTZ1QJv2dunCFB48DSM/I5JdvJgOQl5fP0nXbyMzOwd3FkV6d2rJw5Ub0dHTo1bktNW0s2X3oJOev3mL9b/PKJM7SIJIBoUxsOueDRCrDwbwaxvpa7L3mD4CTRTUehicwf3grFh28xWed3Tj/MJyIhHRMq2hjYajH4VtB9G9ai3PeYZhU0UZVRYVu9e3489hdzKvpFuvrun80Fx9GIJPLGdOhDt/tvkJXdzu8nsRS08yAurbGHL4VhI6mGp3dbNh52ZemDua0cDLHwlCP6KQMjtx+UtheS2cLXK2NALj0KJJfhrVk3ZkHhMWnYWOsX3jMypP3sK9elcuPIunRoAau1kasPHmPWf2b0L2+WKVN+HCt3b4PiUSKo70tpkaG7Pp3Mp6zQw28Hwew5Puv+XnZWiaPGcbZy9cJi4zGzNgIKwsz/jnuweBeXTh96RpmRoaoqanSs1Mbflu1GYvqJsX6unLrHh6eN5DJZHw2cjDf/LKUHh3bcPv+Q2rZWVPP1YkDJzzQ0damW7uWbN13mOYN69G6aQOszM2IjInl4Mlzhe21adqQurUL5gucv3qLxd9+xcrNfxMSEYWdlQXq6mrM+mIsEdFP2b7/GFqaN0hLz0BHSwt1NVU8b3phZmJEFb3i70UViagmEMqEm60xeRIZadm5nPMJZ3R7VzrUKbpMp0xWMH0lMycfIz0t7gbHAtDGxZJa1asSEJ2MnpY6yRk53AiIpkfDGvRuVLNYX0duB2FtrIexvhahcakY62sxsJkDVXQ06N+0FlGJ6XSuZ8vUng04dS8UDTUVBrdwxMJQ750fn3k1XeYPb0ValihREiofd1dn8vLzSUvP4PSlq3w6YgCd2jQrcoxUWlAVlJGZhbFhNW7ffwRA+xaNcaxpi39QCHp6OiQmp3D19j36dG1P/+4di/V14IQHtlbmmBoZEhwWibFhNYb06YpBFT0G9+5CZHQs3dq3YubEURzzuISGujrD+/d4aUXCm4iOjWfl5r+ZNn4E+RIJTvY1GN6/Bxt3H+TSjTs89Avk/iN/QiKi3rkPRRMjA0KZSM7IRUtdFf+oZLrVt2PTuYcoKRWMDDhbGrLp3EMehMYBEBiTjKu1EZJ/3zyUlZQw0tfC2dKQtOw8nCyqUb+mKRvOemNqoF2sr96N7Ln8OBJjfS1sjfVRVlIq/J2ykhLNHM1ZfuIeV/2i6Opuy4EbgUXub15Nl8+6uL3wcbRxseTPY3dJy8rDxlifzecfMrq9K78duo2ykhI6mmq0drFk8aFbnH0QxtCWTgTHpnDhYQR+UUnYmlRBT0u9pJ5WQSgXklNT0dLUwDcohF4d27Bu+36UlJRwdqiBi2NN1u3Yz72HvgAEPAmjbm0HJFIJAMrKyhgbVqW2Q03S0jOo7VCTRvVcWbV1D9WNjYr11b97Ry5cu4WJYTXsrC1QVv7vM62ysjItm7izdO02Lt+4S8+Obdh79HSR+1tWN31pxUH7Fo35bfUWUtMzsLOyYP3O/YwY0JP+Y6cyqGdnLt+4S/sWjflx6Woiop/SsVVTWjZ2ByAsMgY7K4sSeT4VQVQTCKXiTaoJfMLieRiewNBWzmUcneKJagKhInuTaoIHj/158DiAkQN7lXF0iieqCQThLdSxMS6RCXV5Ein/XA8o/LkyJheCUN641XbErbbje7eTl5fP30dOFf5cGZOLsiCSAaHCU1dVYWgrZ674Rr33NXuJVFZk74M7QU+58ySWE17BLB/Xgev+0SSmZ5MnkTKtV0Ny86VM2Xie3o1q0r1BjRJ6RIIgPKOursbIgb24dOMOqWkZ79VWXEISW/YeJi4hiX7dOmBmbMivKzbSu3Nbenduy1c//IadtQVO9jXo1Lopk+bMx97WGqlMyvTPRpXMAyqnRDIglBvPVxzUMK3CmfuhBMQk8+OQFkzecI7mjhY8CI3DzdaEgOgkFn7cmm4//8Po9q4kZeQUzvg/cjuI4KeppGXnMb1PQ+b9fQ1nS0O61LMpnCR45HYQ0UmZAGiqqzCqnStA4d4Hc3d6AtDQ3oz6NUzxj0rCxlif9We9+WVYS+b9fRWJVMYGD2/6NrEvnPwoCMKLPV9xYG9nzclznvgFhbJw7hTGz/iRVk0acNfnMQ3q1MY3MJg/fpxJ2wFjGD9iIInJKYUz/g+c8CAoNIK09AzmfPkpsxYsw9XRnm7tWxZOEjxwwoOopwVzkDQ1Nfh02AAATIyqMXPiaK7duc+TsEhaNKrHxwN7FiYZJkbVkEikSCQF8xlycvNITk3DudaHn+iLagKh3Hi+4iA7T4KyshIymZyQ2FQMdbUY08EVNRXlgu+qBaduDdMqDG7hSGBMcmE7Z+6HYWqgTRVtdaKSMnCzNSYlMwfpO/7BPusdRie3opsVKSkpEZuSSWxKFhd8Irj0KPLdH7ggVALPVxxkZ+egrKyMTC7jSWgkhlUNGD9iIOpqaoXfAWraWjGsX3f8n4QWtnPi/JWCUj59PSKjn+Lu6kRSSupr9zB5xjcwmDOXrjO8X/div5v1xVimjf+YUxeukp2TQz0XR36aMQnvxwEvaOnDIkYGhHLj+YoDuRw01VSRyeVIZTJUVQoqAjTUCvYCUKLg5+DYVDZ6+GBr8t8GRF3dbQmKSUFPSx0zAx0yc/PJk0gJS0jD+t91AV619fDzex80tDfjzP1QFo8sWBrV1dqIVafuY6CtgYWhHj8NbVEilycE4UP3fMWBXF7wiV0mkyGVSQu3HtZQL6i0Ufq3AuhJaARrtu2lhvV/K4v27NiagOAw9HV1qG5qTGZWNnn5EkIjorG1NAd4YUkiQNTTOEZP+45h/bpzw8sbe1trDp44R3ZuLvXrOHP64jVi4uKxsjBFVUUVb99A/tywA1sr89J8asoFUU0glIqS3pvgZebu9GT+8HfbrUyRRDWBUJGV5t4Ez5v+0+8s+f7rUmu/tIhqAkEoA7s9fXG1NqKOjfF7JQL3Q+K46hdFaFwav45ohaqKMnkSKStO3CMrN5+6tsa0c7UusrRw0NMU7ofEEZeaxTf9G2NmoFOCj0wQhG37j+JW2wG32o7vlQj8c9yDJ2ERJCalsOjbaYW3L9+0i6zsHIJCI/hu6gQWr9pEVYMqtGrsThV9PW7d8+HImYus+21ehV434G2JZEAol37Yc425A5qw8dxDejeqyTnvcGKSM2hQ07TwmGejAs++/7LveuFyxWM6FEwIfNXSwvXsTPAKjiU5M6dwYSJ1VRW+6t2QqMR0dl/xQ0VJucjSwk1qVScpI4d8qYxqukW3SxYE4c3M+fVPfpw+iTXb99KvWwfOXLpOdGwcjeu5Fh7zbFTg2ffvfltZuFzx+BEDAV65tPCAHgWXCibOnl+k78ljhrF5zyHaNGuIqqoKaRmZyOVyrC2q42RvRyM3F3wDQypVIgBiAqFQTnWvb8cJrxBikjMwNdAmN1+ClZEeV3yLL/cplclJSMsuslzxmxrToQ5taluRnPnffWKSM1l7xptJ3dyLHV9Q3dCc7g3s8Pp3uWRBEN5Or85tOXLmItFP46luYkRubi7WFtW5dP1OsWOlUinxiclFlit+E3K5nIUrNjJuWP9iv7vp5UPT+nWJiH7KmCH9WDBrSmFScerCVbq2a/Fej68iEsmAUC41rlWdnZd9aeFkQVauhKikDJSUlJA9N8XFpIo2f1/xIzAmudhyxc88W1r42dezUQGA43eCWX7CC6/gWLQ11Nh8/iHZeRKG/XEMI30trvpG0drFkhNewfxx9C4DmjpgpKfFn8e9uPI4CnuzqmX6nAjCh6JZAze27D1M66YNyMrOITImtuD1/VzFj6mxIdv/OYb/k9BiyxU/82xp4Wdfz0YFAL5bvIKwyBiu3r6HVCpl/c79ANz1fkyDugULkxno67H/2BmWbdhBi0YFyf+J85706FDx5iG9LzGBUCgVZTWBsKISEwiFiqysJhBWVBVxAqEYGRAEQRCESk5MIBRKVUB0kqJDKJfE8yJ8CPyCQhQdQrlUEZ8XkQwIpcLIyAhtLS0+X+uh6FDKLW0tLYyMim/RKgjlnZGREdra2oye9p2iQym3tLW1K9TrW8wZEEpNeHg4CQkJJdbe3r17WbRoEWPGjGHSpEkl1u6r3Lx5kylTptCyZUsWLlxYuFJaSTAyMsLa2rrE2hOEslTSr+9Xyc3NpXfv3jRr1owffvjhre575swZZs+ezbZt23BxcSmdAF+gor2+RTIgVAibN29mzJgxTJs2jd9//71wudKycOzYMfr168fgwYPZtm0bKioqZda3IAiwdu1aPv/8c3x9fXF0fLttkaVSKbVr18bZ2ZlDhw6VToAfAJEMCOXe33//zbBhwxg/fjyrV68u00TgmX379jFkyBBGjx7NunXrUFYWc28FoSxIJBIcHBxo1KgRe/bseac2nn2Y8PHxwdXV9fV3qIREMiCUa4cPH2bAgAEMGzaMLVu2KPSP8Pbt2/nkk0/44osv+PPPPxWSlAhCZbN9+3ZGjhzJ/fv3cXNze6c28vPzsbe3p2XLluzcubOEI/wwiGRAKLdOnz5N79696dOnD7t27SrR6/Xvau3atXz22Wd88803/PrrryIhEIRSJJPJcHV1pUaNGhw7duy92lq5ciVffvkl/v7+2Nu/fNfSykqMdQrl0qVLl+jbty+dOnVix44d5SIRAJgwYQJLly5l0aJF/PLLL4oORxA+aIcOHcLX15e5c+e+d1tjxozB2NiYxYsXl0BkHx4xMiCUOzdu3KBTp040bdqUo0ePoqlZ/jYEmj9/Pt9++y1Llizh668r3harglDeyeVyGjZsSJUqVTh//nyJtLl48WK+/fZbgoODsbS0LJE2PxRiZEAoV+7du0fXrl2pV68ehw4dKpeJAMDcuXOZPXs206dPZ/Xq1YoORxA+OGfOnMHLy4s5c+aUWJuff/45urq6LFmypMTa/FCIkQGh3Hj8+DFt2rTBzs4ODw8P9PX1FR3SK8nlcqZNm8aff/7Jli1b+OSTTxQdkiB8MFq3bk1ubi43btwo0bk5P/zwA4sXLyYsLAxjY7FvyjNiZEAoFwIDA+nQoQPm5uacOnWq3CcCAEpKSvzxxx98+umnjBkzhr179yo6JEH4IHh6euLp6cncuXNLfJLu5MmTUVZWZtmyZSXabkUnRgYEhQsLC6NVq1Zoa2tz+fJlTExMFB3SW5FKpXzyySfs2bOHAwcO0KtXL0WHJAgVWteuXYmOjub+/fulUk48Y8YM1q1bR1hYGAYGBiXefkUkRgYEhYqOjqZDhw6oqalx7ty5CpcIAKioqLBlyxb69OnDwIEDOXv2rKJDEoQK6+7du5w+fZrZs2eX2roiX331Fbm5uaxcubJU2q+IxMiAoDDx8fG0adOG9PR0PD09sbW1VXRI7yUvL49+/fpx4cIFTp06RevWrRUdkiBUOAMGDMDb2xs/P79SXfp74sSJ7Nu3j9DQUHR0dEqtn4pCjAwICpGcnEynTp1ISkri3LlzFT4RAFBXV2f//v00a9aMHj16cPPmTUWHJAgVyuPHjzlw4ACzZs0q9T1AZs6cSXJyMuvXry/VfioKMTIglLm0tDQ6derEkydPuHjx4ge3VnhGRgZdunTh8ePHXLhwgXr16ik6JEGoEEaOHMmFCxd48uQJ6urqpd7fqFGjOHv2LMHBwWhoaJR6f+WZGBkQylRWVhY9e/bEz8+P06dPf3CJAICuri4nTpygZs2adOrUCV9fX0WHJAjlXnBwMLt27WLGjBllkggAzJo1i5iYGLZu3Vom/ZVnYmRAKDM5OTn07t2ba9eucebMGZo3b67okEpVYmIibdu2JTExkcuXL4v10AXhFT777DMOHDhAaGgo2traZdbvoEGD8PLywt/fv9wse64IYmRAKBP5+fkMHjwYT09Pjh49+sEnAgCGhoZ4eHigp6dHhw4dCA8PV3RIglAuRUdHs3nzZqZNm1amiQDAnDlzCA4OfuftkT8UYmRAKHVSqZThw4dz4MABDh8+TLdu3RQdUpmKjIykdevWqKiocPnyZapXr67okAShXPn666/ZuHEjYWFhVKlSpcz77969O+Hh4Xh7eyt0m3RFqpyPWigzMpmMcePGsX//fvbs2VPpEgEAS0tLzp07R3Z2Nh07diQ+Pl7RIQlCuZGQkMCaNWuYPHmyQhIBKNhr5NGjRxw5ckQh/ZcHYmRAKDVyuZxJkyaxZs0aduzYwbBhwxQdkkL5+/vTunVrLCwsOH/+vFj5TBCA77//nt9//52wsDCMjIwUFkfbtm3JzMzk1q1bJb4EckUgRgaEUiGXy5kxYwarV69m/fr1lT4RAHB0dOTs2bOEhYXRrVs30tPTFR2SIChUWloay5cvZ8KECQpNBKBgdODOnTt4eHgoNA5FESMDQqmYN28eP/30E3/99ReTJ09WdDjlyp07d+jQoQPu7u6cOHGizCdMCUJ5sXDhQubNm0dISAjm5uYKjUUul9O4cWN0dHS4ePGiQmNRBDEyIJS4xYsX89NPP7Fw4UKRCLxAw4YNOXHiBLdv36Z///7k5uYqOiRBKHNZWVksXbqU0aNHKzwRgIJdSOfOnculS5e4evWqosMpc2JkQChRK1asYPLkyXz33Xf89NNPig6nXDt37hw9evSgW7du7N27FzU1NUWHJAhlZvny5UybNo2AgABq1Kih6HCAggnPdevWxcbGhuPHjys6nDIlkgGhxGzatImxY8fy1VdfsWTJkko5CedtHT9+nH79+jFgwAB27NhR6uuxC0J5kJeXR82aNWnbti3bt29XdDhF7Ny5kxEjRuDl5YW7u7uiwykzIhkQSsTu3bsZPnw4EyZMYNWqVSIReAv//PMPgwcPZtSoUaxfv77S1jkLlcfGjRsZN24cjx49onbt2ooOpwiJRIKjoyP169dn3759ig6nzIhkQHhvBw8eZNCgQYwYMYJNmzaJP2bvYMeOHYwcOZJJkybx119/iWRK+GBJpVKcnJyoW7cu//zzj6LDeaH169czYcIEHj9+jJOTk6LDKRPiXVt4L6dOneKjjz6if//+bNiwQSQC72jEiBGsWbOGFStWMGvWLESOLnyo9u3bR1BQEHPmzFF0KC81cuRIzM3NWbhwoaJDKTNiZEB4ZxcvXqRbt2506tSJ/fv3l9lOYx+yZcuWMW3aNH788Ue+//57RYcjCCVKJpNRr149zM3NOXXqlKLDeaVly5Yxffp0goKCsLW1VXQ4pU58jBPeyfXr1+nZsyetWrVi7969IhEoIVOnTmX+/PnMmzePJUuWKDocQShRx48fx8fHh7lz5yo6lNf69NNPqVq1Kr/99puiQykTYmRAeGteXl60b9+eunXrcvLkSXR0dBQd0gfn22+/Zf78+axcuZKJEycqOhxBeG9yuZxmzZqhpqaGp6enosN5IwsWLOCnn34iJCTkg99gTIwMCG/l0aNHdO7cGUdHR44dOyYSgVLy888/M3XqVCZNmsSWLVsUHY4gvLfz589z8+bNCjEq8MzEiRPR0NBg6dKlig6l1ImRAeGNBQYG0qpVK0xNTblw4QLVqlVTdEgfNLlczmeffcaGDRvYtWsXH330kaJDEoR31r59e9LS0rh9+3aFqpaZO3cuf/75J+Hh4R/0e54YGRDeSFhYGB06dKBq1aqcPXv2g35RlBdKSkqsXr2a4cOHM2LEiEq9vapQsV2/fp0LFy4wZ86cCpUIQME8Hrlczl9//aXoUEqVGBkQXisqKorWrVsDcPnyZSwsLBQcUeUikUgYOnQoR44c4ejRo3Tu3FnRIQnCW+nVqxdPnjzh4cOHFbL8eNq0aWzdupWwsDD09PQUHU6pqHj/K0KZiouLo2PHjuTn53Pu3DmRCCiAqqoqO3fupFOnTvTt25fLly8rOiRBeGMPHjzg2LFjzJ49u0ImAgBff/01GRkZrF69WtGhlBoxMiC8VFJSEu3btyc2NpbLly9Tq1YtRYdUqeXk5NCzZ09u3ryJh4cHTZo0UXRIgvBSMpmMCxcusH79em7evElgYCCqqqqKDuudjR8/niNHjhASEoKWlpaiwylxFTNNE0qVh4cH3t7edO3alcjISDw8PEQiUA5oampy+PBh3Nzc6Nq1Kzdv3mTHjh3IZDJFhyYIxVy/fp2OHTuyZ88eevfuXeE34frmm2+Ij49n06ZNig6lVIiRAaGInJwcjIyMMDU1JTExkfPnz1O/fn1FhyU8JzU1lY4dOxIUFERKSgoeHh506NBB0WEJQhGXL1+mTZs2qKqqIpfLCQ8Px9zcXNFhvZfhw4dz5coV7t27h7a2NpqamooOqcSIkQGhiFOnTpGZmUlYWBgWFhZYWVkpOiTh/1SpUgVbW9vC/Qu2bt2q4IgEobisrCwAtLS0uHDhQoVPBAAmT55MeHg4bdq04ccff1R0OCVKJANCEX/88QcA+vr69OjRg6pVqyo4IuFF+vTpUziZ88CBAwqORhCKa9asGV26dOHevXu0atVK0eGUiJkzZ2JgYICfnx+pqamKDqdEicsEQhF3797l4cOHDB8+vEJP9qksLl++THR0NEOGDFF0KILwwfPy8qJnz57ExMTQrVs3Tpw4oeiQSoxIBgRBEAThDSUlJdGmTRsGDhzIvHnzFB1OiRHJQAkLDw8nISFB0WEojJGREdbW1ooOo9yr7OfJ64jz6MXEefNy4px5P2IcuASFh4fj7ORIVnaOokNRGG0tTXz9/MWL8hXCw8NxdnYiKytb0aGUW9raWvj6+onz6DkF541z4cQ8oShtbW18fX3FOfOORDJQghISEsjKzmHliPrUMtFVdDhlLjAug0k7vEhISBAvyFdISEggKyubtdMG4mhlouhwyh3/iDgm/LFfnEf/p+C8yWLLisU429dQdDjlim9QMKO+mCnOmfcgkoFSUMtEl7pWBooOQyjnHK1McKtZ8cuthLLlbF8D97ouig6jXPL19VV0COXSm1xCEcmAIAiCUKE9jYtHWVmJESNGKDqUculNLruJZKAcuegXR1JmHtUNtAhNyCQyOYva5vr0qGvOco9A1FSVaWFvhLqqMkvP+LN2ZMMXtiORylBVefkSEh6PY3kUlUp6joRve9UuvH3Mpls0rWlIG0cTHM0+zJ25PgTn7wWSmJaFuZE+oTFJRMSn4GJrRrfGTizZe5HUzBx+HdeD8/cC8Q6OITQ2id/G9+L641BO3vLj13E9XtiuRCpF9RVLxq44dIWM7FyMDXT5pHPDIn09s3D3OXQ0NbA2MaBPC9cSf+zCuzl78SoJSclYmpsSHBpBWGQ0dWo70KtzexYsW0NqWhq//zSHuw8esmDZGr77ehL1XJ3Zsvsf0jIy+PLTT4q1KZfLkclkr1xmeOnqTTzyD2Tjsl+5dO0Wew+fwNHerkh7076dj52NJc617OnUtsU7Pb6U1HRkMjkbvv8MRxsx2vY8/7Boxv205rWXUEQyUI60dTJh0Qk/vMKS+aV/HX475UfLWsb4xaThH5tOHYsqqKoo4Wimh4le0WUwn6bmcPbRU1Kz82lkVw2ratocexBd+PsW9ka4WFQBwDMgnh/7urLhcjBhiZnYGOoAYKyvSWauFBXlirXfeGXT3r0W83d6cDcgkoWf9mDh7nO0rlMDVRUVZg3twOwNxwuPa+9ei7kbT5AnkdK6bk1O3vIr0taj0Kdc9g4mTyKlX0tXYpMzuOMfUfj7j9rWo5q+NgBf9G1JelYui/ecL9bXM4b6OuTmS8jJl5TysyC8jU5tWzBv8Z/cvufN0p/n8NOSFbRr0RRVVVW+n/4FX3+/AIAGbq706frf0tZtWzblyCmPwp/lcjm3vB5w9ZYXMpmMscMHcef+Q3wDgwqP+XzUMNTU1AD46vMxhW1raqijr6dLXl4+Uqm0MIkwMTZEIpGSL8l/78fpaGNOPUfb926nMhIrEJYjMpmclOw8JDI5Utl/FZ8SmRwLAy3Gta7BZs+QYvdLz8ln7gEf8mVyhje1oUkNw3fqf9HAunzZsRZbrhTvQyg/ZDIZKRnZSKQypNJXb1K06eQt2rnbo6OpXux39wKjWLj7PNYmBnzWqxnWJq9ebTItK4eFu88xbUCblx7zaY+mfNG3JQ+CosmXSN/sAQmlTiaTkZKShkQiQSp99/+XU+cvs2zdVuq5OjPts9FUNajyxvdt0qAev347nZp21ly+frvw9tlTPuOrz8dw6pzYmluRxMhAObL+cjAjmtqgoqzE2ktPCm+vXV2f/XciWXU+iBa1jIrdT09TjY2jG5GQkctJnxisDXVoWcuI8W1qvrCfVg7GLPcIJC0nHxtDHbZeDeWjxlasvfiE9BwJze2L9yGUH2uOXmdk54aoqiiz6si1Ir/bcOIm3sEx3PaPwCc4hksPntC0tg0NHaww0C267ap7LQu2zx5GYGQ8W07dpnNDBxo5WtHI8cX7UQz9ZQdt6tbg4oMn9G9Vp0hfuXn5mFTV41HoU55EJ6KmpoKaasXepe5DsnzDNsYMH4Sqqgp/riu6l8WaLbu4/9CXm3fvY1itKmcvXeWxfxA1bIqfB906tKFbhzbc837EX+u38fGgvnRq2+Klw/s7/znC/Ye+nL14FT1dbTxv3CEkPJKfZ01l35GTdGjVjIMnzhD9NB4ri+ql8thLSkp6Jqv2nmbO2P5l0t/p6w/wDgwjPTObnz7/CIC8fAl/7jpBZk4u9RxsaORiz/K/T6KsrMzo3m2pZV2dsze8WX/wHHsXTXur/sSiQyXIy8uLBg0acOar1iVSTXDIKwptdRU6u5oVud3/aTonfWKY2snhvfsoSd4RKXReepm7d++KnQ5f4dl5cnHpxBKpJvjnsjfamup0a+z0yuMuez8hLDaZjzu9eK5JefHgSTRtv1olzqP/8+y8uXlq/3tXE+w5dBwdbW16dm5XIscp2u5/jvLJ5Jl4bvyJ24+CkEhlONqaY1JVn92nrwLgbGeJT2AYi6aMYMHGA0wc3IVzt3wIi0nAzNAAK1NDDpy/ycBOzTh7wxvTalVQU1WhR6v6/L79KObG1cjJzSuSDFx94M/5Wz7IZHLGD+jI7OW76N7CnTuPg7G3MsPN0YaDF26hq6VJl2b12H78Ms3qOtDK3QlLU0Oi4pI4dPFWYXut3WtTp1bBdf1Zy3eycPJwVu87Q7cW7tiaGxceFxmbyM6TnjjamKOvq42ethZnbzygf/smPA6O5ObDQBZNKZhMed8/lFZjv3/t60mMDJRjfetbvPB2RzM9McFPKDSgdd03Oq513RePFAmVz0d9XzyJ9F2PK0/qOdpx7YE/aRlZ3PcPZVzfDoTFxBMZl1R4jFRWcHktIzsX46r63Hn8BCtTQ9o2csHBujor/j6FvZUZ0XFJXHvgT+82DbEyNWL3qStF+jp04RZ17K3JzM4lOCoO46r6DO7cnKCIpwzs1JSLtx/Rrbk7jV3tWbzlMBrqqgzt+m6TJJ+JSUhm1b4zfPfpAORyOesPnEOOHDVVVTxu+aCkBA8Cw/AJDC9MLN6ESAYqoNTsfNZdesKMrq/+JFhSXlZ9sO92BE9Tc9DWUGVsK7syiUV4c6kZ2aw+eo1ZQzu8/uAScOaOPz4hMaRn5fLDJ12AgmHNvw5eISs3D7ca5jR0tOLItUc8DntKcxdb7C2MueMfwfGbj1n5ZX9sTKuVSazCi6WkpvHX+m18P/2LMunv5LlLPHjkR1p6Bgvmfg1AXl4ev6/eRGZWNu51ajOgZxdCwiMZ8fnXHN+1niOnzhEQHIq9rTWjhg4o1mZyWgZaGur4hUbTo2V91h88h5JSwchA7ZpWbDh4jvv+oQAEhEVTt5YNkn/n3igrKWNcVZ/aNSxIy8jCyc6CRi41WbP/LGaGBsX66tuuMZfuPMK4qj525sYoK/03+VpZSYkW9Rz5Y9dxPO/50r1lffZ7XC9yfwuTakwa3PWFz027hq78vv0oaZnZ2Jobs/HQeYZ1a8mgmUsZ0KEpnl6+NHNzQCaXk52Ty9i+7TH9N8bwmIS3SgRAJAOlavOVEKQyObVMdTHW02DfnUig4JP9o6g0fu7nym+n/BjfpiYX/OKISMrCVF8Ti6paHLkfTT93C877xWGip4GqihJdXavzl0cg1Q00i/V140kil/zjkcnljG5px7zDD+niYoZXWDI1THSpa1mFo/ej0dZQpVNtU3bfDKexXTWa2xthUVWL6JTst64+aFHLiNUXnmCvpVYGz+aHa8OJm0ilMhysjDE20GXPhfsAOFub4BMSw6/jCioGPu/VnHP3ggiPS8asqh6WxlU4ePUhA1rVxcMrAFMDPVRVlene2Jk//rmMuaF+sb6uPQrlwv0gZDI547o3Ye6mk3Rr7MTdgEhqmhviVtOcw1cfoqOpTueGjuw4d5emzja0dLXD0tiAqIRUjlx7VNheyzp21LEruNZ76cET5o/tztqj1wmLTcLGtBrqaqpMH9yWyPgUdp+/R58Wrnzeuzkz1x2jTwtXtDXUaVDLAv+IOJEIvIU1W3YhkUhxqlUDE2Mjdu0/AkBtR3sePPLl958KKga+/HQkZy5eISwiCjNTY6wtzNl/9CQf9e3BqfOemBkboaamSq8uHVi8fB0W1U2L9XXl5h3OXrqKTCbn81HDmPHDQnp2bs/te97UqmGDe53a/HPsNDra2nTv2IYtfx+geeP6tGnWGCuL6kRGP+XA8dOF7bVp3gQ3l4IPMuc9r/PbD7NYsWE7IeGR2Flboq6uzuwpnxERFcO2vQfJzMriwLHTdGlXsA2ynp4OWpoaZOe8eNn3zs3civxcp5Y13oFheAeG80nPgsmv4/oVJMjzJw0F4KPOzYvc57tPBxb5+cfPBr+wrxZujrRwcyz8+dnQ/POXEhZOHl7470Yubz4616WZG12eeyxj+7YH4Mqmn4scN2148dGbZ3G8DVFNUIrcrAzIk8pIy5ZwzjeOUc1tae9UdPnZf0eryMqVYKSrgVdYMgCtHYywN9UlMDYdXU1VkjPzuRGcSPe61entVvw689EH0VgZamOsp0FoQiZGuhr0b2BJFW01+rlbEJWcTScXU77sUIvTD5+irqrMoEZWWFTVKtbWmzI30OLnfq6kZb9/SVBlVq+mOXkSKWmZOXjcDWBst8Z0rF+ryDHPqksyc3IxrqLDnYCCxLKtW00cLI0JiIhHT1uD5PQsrj8Oo1fT2vR9QZ3/4asPsTGtiomBLiFPkzCuosOgNm4Y6GoysHVdohJS6dLIkWkD23Dylh8aqqoMaeeOpbHBOz++mMQ01h67zhd9WwKQlJaFtoYa2hoFFQ5n7gbQqUH5mv9S3tWv60Jefj6p6RmcPn+Z8Z8MoXO7lkWOkcoKqgYyM7MwMTLktpc3AO1bNcPRvgZ+gU/Q19MlMTmFKzfv0Ld7Jwb0Kv4p9Z+jp7GzssTUyJDgsHBMjAwZ2r8nBlX0+ahvDyKin9K9Y1u+mTyeo2cuoKGuzoiBfd5rQmD00zhWbNjOV5+N4dK1W+RLJNzyesA5z+v0696ZudMmkpuXT3BYxOsbA+rWsmFE91bvHM8zefkSdpzwLPz6kIiRgVKUnJWHppoKAbHpdHU1Y/PVUJSUCkYGnKvrseVqCA8iU4CCdf1dLaoUvukrKylhpKuBk5k+6dkSHM30qG9TlY2ewZjqFx8Z6OVmjmdgPEa6GtgYahcdrlJWollNQ1aeD+JqUCJdXM046BVV5P7mBlpvVX3wSQtbfj/tj7KSEjoaYtb4+0jOyEZTXRX/iDi6NXFm48mbgBLO1ibUtjFl48mb3H9SMGoTEJlAHTszJP++0SsrKWFURQdnG1PSsnJwsjKlgYMl647fwKxq8XklfVq4ctn7CUZVdLE1q4ayctFhzeYutvx1wJMrPiF0a+zEP57eRe5vYVSFz3s3//9mAWjjVpM/9l8iLSsXG9NqbDp5i6Ht3Rnyy3b6t6rLlYchdGrgwK7zXgzr8N9EptO3/Vkyodf7Po2VSlJKKlqaGvgGBNGrSwfWbtmNkpIStR3tcXVyYO3W3dzzLhjB8Q8Koa6rExLps3NGGWPDarg41SI1PZ3ajvY0dndj5aYdVDcxLtbXgF5dOO95AxOjathZWxU9Z5SVadW0Ib+v2sila7fo1bkdew4VXXvC0tzshYsWQUFisnj5elLT07GztmTdtr/5eFBf+o78jMF9unPp+i26d2xL945t+Skvnw6tmnH6gif3H/oSHROL5QtGMkqTuppqkaTC08uXlIwserVu8F7tJqdlMvqHlfz0+UdYmhqyau9p9LS1GN69Faev3ycxJYPc/HxmjOz9vg/hpUQ1QQl6k2qCh1GpPIxKZUjjD28zDVFN8GbepJrAJzgGn5CYIn80KwtRTfBir6smuP/QF+9Hfoz8qJ8ColOs56sJ3mbRofUHPAorD2pamnLq2n38Q6OZ/8VQPl+wnpb1nPDyC6G+kx1+oVEsmTaSDp/9xKf9OpCYmk5dextSMrKQSqUERcaSlpHFrNF9mbtiNy41rejavB6WpgXrvhy6cIuo+IJJjFrq6oz5d9hfKpXxx67jGBnoUd/JjhvegaSkZ6Kursro3u34dfNBFk4eztyVu/lxwmBU37JkV1QTlFOuFlVwtXjzhTpeJk8i44BXZOHPH2JyUZnVqVGdOjXev+46L1/C/sv/fbqvjMlFZVHP1Zl6rs7v3U5eXh5/H/zv0/2HnFw8X3mQnZOHspISMrmc4MhYDKvoMa5fB6Yu2cK4fh2YvXwXADUtTRnSpQVTfttMXXsbAE5eu08rd2eUgMi4JOo52RGXmFpYtfAqDwJDyc7J5fLdCFLTs1BVVaahS030tLU4dPG/xZmUlEp3ZViRDFRQt0OTqKKlRrc67/4HIz0nn903w4lJzUFHXZUurmYcuR9Fdp6Ur7o4EpqQyeWAePS11BjTUlQLVETqaqpFEoArPsGkZubQo2ntV9zr1TKyc/llhwe6Wup0bOCAhVEV1h+/gVQqY9rANhy59oi0rBwCI+P5Y2If1NXE20xFoq6uXpgAXLp2i8MnPejTreM7tyeRSIrsf/AkNJyd+4+gq6PN5HEfM/+P1VSrWgV1NTVGDOrD19//ip2NFaZGhoweNvD1HbyH5ysP5PKCJZNlMjlS2X/7u2j8e/4++2P8JDKWdf94YGfx3/yv7i3dCQx/ir62FtWNDMjMyiFPIiEsJh6b6gWXXvq2a/zCGOo71aC+Uw12nPCkbi1rDKvosXJvwYTLUb3boqqizPK/T2Kgp/PWowJvQ7xKFeT5SgM7Y13OPoolMDadeb1dmPr3PZrVNORBRCpuVlUIeJrBggF16PmnJ6Na2JKUmVc40//o/WhCEjJJy87nqy4O/HjkMc7V9ehU26xwcuDR+9HEpGYDoKmmwsjmtkDByoXj29Tkt1N+DG1izeH7UQxtYo13ZCpnHj3lXniKWM+gnHm+8qBmdUNO3fEnICKen0d3ZdJfB2jhasv9oCjq2VvgHxHP4vE96TxzLWO7NyEpLYs6dgULWB2++pAnMYmkZeYwc0g7vtt8ito2pnRp6Fg4WfDw1YdEJ6YBoKmuyuiuBW9mgZEJONuY0K2xMz9sPY2rrRnjezQlJTOH07f90NJQJeRpJhrqaiIRULDnqw7sa9hy4uxF/AKfsOj7mYybNofWzRpx98FDGri54hvwhGXzv6V1r6FMGDWUxKRk3FwKRhr+OXaaoJAwUtPS+XbaRL75eTGuTg5079i2cKLgP8dOExXzFAAtTU0+/bhg1bz/3/9g4859WFY3IzcvD4C4hER+mPklPYd9yqghA0jPyCQ+IZFmDd1L/fn5/8oDgI97tAag7pSCT/3PZuYv+KKg8qChcw3GD3h1cjRxcJe3juX5uQjP+gJwsC6blRlFNYGCPF9pkJ0nRVkJZHI5oQmZVNNRZ1QLO9RUlAq+qxZkpHZGOgxsaEVQbEZhO2cfx2Kir0EVLTWiU3Jws6xCSlY+0jecCiKRyohPz8WiqhaDGlpx0ucpDyNTUVNRJiE9l6GNrcnMlRCakFkqz4Pwdp6vPMjKzf9vWDMmEUN9bcZ2a4KaqipjuzVB/d9PETXNDfmobT0CIuML2zl9xx+zqnpU0dEkKj6VejXNSU7PRiZ7/XnjVrM62bn5HPT0wUCneDVKTFI6P4/uhqOlMVEJqSX34IW39nzVQVZ2NsrKSsjkMoJCwzGqVpUJnwxFXV2t8DuAfQ0bhg/ojX/Qf3uUnPC4SHUTYwz09YmIfkr9Oi4kpbzZMPj/y83NpVPbFlhUN8Xzxh3atWzK6s070dbWIi4hkZ5d2rPkx9mcOl8+9irYccIT78Cwwp/fpWwP4OD5WyzZfrTwcgMU7Bkxa/lOlmw/WjCnIC6JlXtPMfHXDew86YmXXzBDZi8r0n9pEWm7gjxfaSCn4BO7XA5SuRxV5X+Hp/59M1eiIBkISchk05UQbIx0Ctvp4mrKk7hMdDVVMft318E8iYzwxCysqxXsNter3suXvD3p85SurgWZZ/6/C2+oqyrTrY4ZVbXVWXPxCYkZuZgbvHsJolBynq88kANa6qrI5DJkMvlzw5r/njf/XmJ8Ep3I+uM3sDP7r46/W2MnAqMS0NPSwKyaPpk5eeRLpITFJmNtWrBh0cu2IFZWViZfIiMnL5+RnRuio6XO+n9HLKYOaM2ei/f584An0QmpGFXReWEbQtl4vupALpejpalZMAwulRYOOWuoF5R4PnufCQoOY/XmnUX2JujZuR0BT0LR19XB3MyEjKws8vLyCQ2PxNaqYKXUAT1f/mn4+f0PPh7cj617DpKVlc2cqZ9z/sp1srJz+HhQX7S1tLjgeYPIqKfUf88ll9/Gt6v+Zt74gaw74EHfto05e9ObmPhkGtb+r8Lqmz93sGjKiMLvP6zdV7hk8bN1C161vHC/9gUja5MXbSr8fVJaJlrq6kwb1oOPZv9B33aNmTS4K9P/2Ea/do3R1tSgZ6v3q1R4UyIZUJAOzsVLYoY2KThpXPsVXAL4uV/Bm/EPfQpeFO7WVV977f7TNjXeKo7nEwVzAy0mtbcv/Lm9swntnU1edDdBQV5Ujz+8Y8Gbxa81ChYf+XVcwfefR3cDoIGDJZ/2aPrKdj/r9eJywZeZ3K9oTfuPn/z3h2BSn/dbblUoOV3bty522ydDChbEeTbZ8Pef5gCwaN5MABq51+Hz0cOL3e95k8eNfKs4Phs1jM9GDSv8+fmJjoP7dC9y7Po/5r9V2yWhZ6sGHLvsRXR8MmaGBuTm5WNlZsRlL18cbYt+mJLKZMQnp+EfGl24ZPGbkMvlLNl2lDF9/tvnwchADxtzY9Yd8MCkasH7fmJqOtqaGmhrapTcA3wDIhkop/6+FV6s8uBZcvA2HkSkcC0ogdDELOb3cy389Pj8UsJDGlux+2Y4T1NzaO9sQp5Ehk9UKuGJWSwYUAc1FXE1qaLadc6LYe3fv4LgflAUVx6GEPI0iUWf9kD1373olx+8gm94LKumFF8WVqg4tu05SF0XJ+q5OhcmB+9i/9FTPAkNJzEpmcXzvim8/a/1W8nKziEoOIw/F3zL5l3/EB0bR9f2rdBQV+em1wOOnDrH+j8WYGdtWRIP6a00rVOLfl/9xoSBncjKzSUyNgknO3Nk8v8ug5hUq8LOk54EhMUUW7L4mVctLzxvzV6S0jK45u1P3Vo2bDl6kbF92yOTycnKyWVYt4IEe+eJKwz/d/7Ak8hYzt/ywTckEltzE/RfcFmupIhkQIF+OvKI2T2c2XwlhJ5u5lzwiyMmJYf6Nv/tK//dwYf83M+18PuC476FyxOPalEwSvCqpYTdrAzwCksmJSuvyEJEzy8lrKOhiru1ARs8Q9BUU6G5vRFtnUyYd+gheRKZSAbKue+3nOK7EZ3YcOImvZu7cM4rkJikNBo4/PemOnvDcX4d16Pw+0/bzxQuXzy2WxOAVy41XM/egjsBkSSnZxc5jyb3a8nsDUUXmRHKp1k//8bPs6ayessu+vfowpkLnkQ9jaNx/f82uvr6+wX8/tOcwu/f/vpH4ZLFEz4pmNT2quWFB/67guHnM74v0veXn37C5l37adu8MTra2jRyr8OKjTvQ0tSkkXtdGrnXxTfgiUISgWcOLp1R+O+fJ35U7Pd1axVMKBzereAP9f8vWfw6z7YhfubZ8sLPjxQAfDm0W+G/a1qasumHiW/Vz7sS7/IK1K1OdU76PCUmNQdTfU1y8mVYVtPialBCsWOlMjkJGblFlid+U6Nb2tHawZjkrLzC2/5/KeEGttVYNLAuPpEFE762Xg2ljaMJOhoiXyzvejSpzfGbvkQnpmFWVY+cfAlWxgZ4+oQUO1Yqk5OQmllk+eI3Na57E9q61SQ5I7skwxfKSJ+uHTl86hzRMXFUNzUmJzcPG0tzLl69WexYqVRGfGJSkSWL34RcLufXP9cUVhI878bd+zT9t0KgSYN6rFg4j3s+j4GCDYtedElDKDvinV6BGtlVY+ja64xpWYPsPCnRKdk4mOoie64SwERPgz23wgmKSy+2PPEzr1pK+IR3DCEJmYTEZ9K/vuULlxIOS8jkyP1oEjPz6OBswtaroVwOiKdJjWrUtzHAQFu91J8L4d01cbZm4A9b+bRnU7Jy84lKSMXJyrhIZYCJgS67z3sRGBlfbPniZ1611PCx648JjkkkOCaRQW3c2HTyFmO6NWbPxft4B8dw/l4g7d1rvfC+QvnQrJE7PYd9ysQxw8nKziEiOgZnB3vkz50npsbGbNt7CP+g4GJLFj/zquWF5y5YStK/ex24uTixcec+xo8cUli+CBAcFsH+I6dITEqmS/uCT9knPC7x14Lv3vsx+odFv/6gSuZNnxOxHHEJepPliD9kYjniN/MmyxFXZmI54hd73XLEldlJj4v0GzXxjUpjKyNtbS18ff2wtn75SrViZEAQBEGo0MxMCkbCduzYgbPz+y/J/KExMjJ6ZSIAIhkoFYFxGa8/6ANUWR/3u/KPiFN0COWSeF5ezTcoWNEhlDvPnhNnZ2cxmvSORDJQgoyMjNDW0mTSDi9Fh6Iw2lqaGBkZKTqMcs3IyAhtbS0m/LFf0aGUW9raWuI8+j8F5402o76YqehQyiVtbW1xzrwHMWeghIWHh5OQULwaoCTI5XKGDBmCkZERK1eufKv7BgcHM2jQIL777jv69u1bKvHBmw1HCaV7nvy/6Oho+vTpw9SpUxk+/NWLyfy/nTt3smzZMg4fPoy5ednNbxDn0YuV5Xlz8OBBfvnlF/bv34+d3dttVDZx4kSSkpLYvXt3qe+294w4Z96PSAYqkGPHjtGrVy8uXbpE69ZvX4YzYMAAvL298fX1RVVVDApVFhMnTmTfvn2Ehoaio/N2ywNnZmZia2vL4MGD3zoBFSouiUSCk5MT9erVY//+tx/BunTpEm3btuXYsWP06NGjFCIUSppIBioIuVxO8+bNUVVVxdPT853auHv3Lg0bNmTXrl0MHTr09XcQKryYmBjs7Oz4/vvvmTPn3VaWmz9/Pj///DMhISFUr142O6gJirVr1y6GDx+Ol5cX7u5vv3ugXC6nVatWyGQyrl69WmajA8K7E8lABXH+/Hk6dOjAiRMn6Nat2+vv8BLdunUjMjKSBw8eoKws1pz60M2YMYN169YRFhaGgYHBO7WRkpKCjY0NEyZMYPHixSUboFDuyGQy3NzcsLKy4sSJE+/czokTJ+jRowfnz5+nXbt2r7+DoFAiGaggOnbsSHJyMnfu3HmvLNvT05PWrVtz+PBhevfuXYIRCuVNYmIiNjY2TJkyhfnz32/zlzlz5rB8+XLCwsKoVq3a6+8gVFiHDx+mb9++eHp60rJly9ff4SXkcjkNGjSgWrVqeHh4lGCEQmkQyUAFcPPmTZo2bcr+/fsZMOD9N4Rp3bo1ubm53LhxQwzffcDmzZvHb7/9RlhYGMbGxu/VVnx8PDY2NsycOZMffvihZAIUyh25XE6TJk3Q0tLi0qVL793e/v37GTRoEDdu3KBJkyYlEKFQWkQyUAH07t2bwMBAHj16VCJD+6dPn6Zr1654eHjQoUOHEohQKG/S0tKwsbHhk08+YdmyZSXS5tSpU9m2bRthYWHo6em9/g5ChePh4UGnTp04ffo0nTt3fu/2pFIpLi4uODo6cvjw4RKIUCgtIhko57y9vXFzc2Pr1q2MHPl2e4i/jFwup1GjRujr63P+/PkSaVMoXxYvXsy3335LcHAwlpYlsxNcZGQkNWrUYP78+cyYMeP1dxAqnHbt2pGens7t27dLbNRw69atjBo1Cm9vb+rUqVMibQolTyQD5dzQoUO5ceMGAQEBqKmplVi7Bw4cYMCAAVy7do1mzZqVWLuC4mVnZ2NnZ0fv3r1Zt25dibb96aefcvToUUJCQtDSKr291YWyd+3aNVq0aMGBAwfo169fibWbn59PrVq1aN68Obt27SqxdoWSJaaTl2OBgYHs3buXmTNnlmgiANC3b1+cnZ1ZsGBBibYrKN7GjRuJj49n5sySX6num2++IT4+ns2bN5d424JiLViwgNq1a9OnT58SbVdNTY2ZM2eyZ88egoKCSrRtoeSIkYFybOzYsZw8eZLg4GA0NTVLvP3t27czcuRI7t+/j5ubW4m3L5S9vLw87O3tadWqFTt37iyVPoYNG8a1a9cIDAws8SRVUIz79+/j7u7O9u3bGTFiRIm3n5OTg52dHT169GDDhg0l3r7w/sTIQDkVHh7Otm3b+Prrr0slEYCCSxB2dnZidOADsnPnTiIiIpg9e3ap9TFnzhzCwsJKLdkQyt6CBQuoUaMGQ4YMKZX2NTU1+frrr9m2bRsRERGl0ofwfsTIQDn15ZdfsnPnTsLCwtDV1S21ftauXcvnn3+On58fDg4OpdaPUPqkUinOzs64uLhw8ODBUu2rb9+++Pn58ejRI1RUVEq1L6F0+fv74+zszJo1axg/fnyp9ZORkYG1tTUff/wxf/75Z6n1I7wbMTJQDsXGxrJ+/XqmTJlSqokAwKhRozAzM2PhwoWl2o9Q+vbv309gYOA7Lzv8NubMmYO/vz8HDhwo9b6E0rVw4UKqV6/OJ598Uqr96OrqMmXKFNavX09cnNimurwRyUA5tGzZMlRVVZk8eXKp96WhocH06dPZvn07YWFhpd6fUDrkcjkLFiygU6dONGrUqNT7a9y4MR07dmT+/PmIwcWKKywsjB07djB9+nQ0NDRKvb/JkyejoqLCH3/8Uep9CW9HJAPlTHJyMitXrmTSpElUrVq1TPqcMGECVapU4bfffiuT/oSSd/z4cby9vZk7d26Z9Tl37lwePHjwXuvXC4r122+/YWBgUKqXB55XrVo1Jk6cyKpVq0hJSSmTPoU3I5KBcmbFihXk5+czbdq0MutTR0eHqVOnsnHjRmJjY8usX6FkyOVy5s+fT/Pmzd9pa+t31aZNG5o3b86CBQvE6EAF9PTpUzZs2MCUKVPeemvr9zFt2jRyc3NZsWJFmfUpvJ5IBsqRjIwMli1bxrhx4zA1NS3TvidNmoSamhpLly4t036F93fx4kVu3LjB3Llzy3SvCSUlJebMmcO1a9dKZB17oWwtXboUDQ0NvvjiizLt18zMjHHjxrFs2TIyMjLKtG/h5UQ1QTmydOlSvvnmG548eYK1tXWZ9z979mxWrFghdqarYDp27EhiYiJeXl5lvvGUXC6nfv36GBkZcfbs2TLtW3h3SUlJ2NjYMHnyZIWUFoeFhWFvb8/ixYvLdBRUeDkxMlBO5OTksGTJEj7++GOFJAJQMHwnkUjE8F0FcvPmTc6dO8ecOXMUsgPls9EBDw8Pbt26Veb9C+9m+fLlSKVSpk6dqpD+bWxsGDFiBEuWLCE3N1chMQhFiZGBcmLNmjVMmjQJX19fhdb7l9X6BkLJ6NOnD/7+/gqt93+2M52TkxOHDh1SSAzCm0tPT8fGxkbh9f7P1jdYvXo1EyZMUFgcQgExMlAOSCQSFi1axKBBgxS+8M+MGTNIT09nzZo1Co1DeD0fHx+OHDnC7NmzFbrwj4qKCrNmzeLw4cP4+PgoLA7hzaxZs4aMjAyF7zzp6OjIoEGDWLRoERKJRKGxCGJkoFwob3sEjBs3juPHjxMSElJqSyEL72/o0KFcv369XOwRkJ+fj729PS1bthTLFJdjOTk52Nra0qtXL9avX6/ocEp9TwThzYmRAQWTyWT8+uuv9OzZs1wkAlCwM11cXJzYma4cK80dLd/Fs53p/v77b7EzXTm2adMm4uPj+eabbxQdCgD16tWjR48e/Prrr8hkMkWHU6mJkQEF++effxg4cCDXr1+nadOmig6nUHn61CkUVx5Hb7Kzs7Gzsys3nzqFovLz86lVqxbNmzdn165dig6n0PXr12nevDn//PMP/fv3V3Q4lZZIBhRILpfTsGFDDAwMOHfunKLDKcLb2xs3Nze2bNlS6muWC28nIiKCmjVrsmDBAqZPn67ocIr47bffmDt3Lk+ePMHKykrR4QjP2bJlC6NHj8bb25s6deooOpwi2rdvT1paGrdv31ZIVYwgkgGFOnXqFN26dcPDw4MOHTooOpxievfuTUBAgNiZrpz58ssv2bFjB2FhYejp6Sk6nCKezVQfOXIky5YtU3Q4wr+eVXw4Ojpy+PBhRYdTjIeHB506deLUqVN06dJF0eFUSiIZUKBWrVqRn5/P9evXy2U2fPPmTZo2bcq+ffsYOHCgosMRgLi4OGxsbJg1axbz5s1TdDgv9MMPP7B48WJCQ0MxMTFRdDgCsG/fPgYPHszNmzdp3LixosMpRi6X07RpUzQ0NLh8+bKiw6mURDKgIJ6enrRu3ZojR47Qq1cvRYfzUh07diQpKYm7d++Wy4SlsqkIq0QmJSVhbW3Nl19+qZDV7YSi5HI57u7uGBsbl+tVIo8cOUKfPn24fPkyrVq1UnQ4lY5IBhSka9euREdHc//+fZSVy29Rx/nz5+nQoQMnTpygW7duig6nUktOTsbGxobPPvuMxYsXKzqcV5oxYwbr1q0jLCwMAwMDRYdTqZ04cYIePXpw/vx52rVrp+hwXkomk+Hm5oalpSUnT55UdDiVjkgGFODOnTs0atSI3bt3M2TIEEWH80pyuZzmzZujoqKCp6enGB1QoF9++YVffvmF0NBQzMzMFB3OK8XExGBnZ8d3331XptsqC0XJ5XJatmyJXC7n6tWr5f71u3v3boYNG8adO3do0KCBosOpVEQyoAD9+/fHx8cHPz+/CjEx79ixY/Tq1YuLFy/Spk0bRYdTKWVkZGBra8uQIUMqzN4RkyZNYs+ePYSFhZXpFrnCfy5evEi7du04duwYPXr0UHQ4ryWVSnFycqJu3br8888/ig6nUim/49MfqMePH3Pw4EFmzZpVIRIBgB49elC3bl3mz5+v6FAqrXXr1pGamqrwJWTfxowZM0hJSWHdunWKDqXSmj9/Pm5ubnTv3l3RobwRFRUVvvnmGw4cOMDjx48VHU6lIkYGytjHH3/MpUuXCAoKQl1dXdHhvLE9e/YwZMgQbt26RaNGjRQdTqWSm5uLnZ0dXbp0qXCrQo4aNYqzZ88SHByMhoaGosOpVG7dukWTJk3Ys2cPgwcPVnQ4bywvL4+aNWvSrl07tm3bpuhwKg0xMlCGgoOD2b17NzNmzKhQiQDAwIEDqVWrlpgdrgBbtmzh6dOnzJo1S9GhvLVZs2YRExPD1q1bFR1KpbNgwQIcHBwYMGCAokN5K+rq6syYMYNdu3YRHBys6HAqDTEyUIY+++wzDh48SGhoKFpaWooO561t2rSJsWPH4uPjg6urq6LDqRQkEgkODg40atSIPXv2KDqcdzJo0CC8vLzw9/dHVVVV0eFUCj4+PtStW5dNmzYxevRoRYfz1rKysrC1tWXAgAGsXr1a0eFUCmJkoIxERUWxefNmpk2bViETAYARI0ZgZWXFwoULFR1KpfH3338TEhLC7NmzFR3KO5szZw7BwcEVNpmpiBYuXIi1tXWF3QlQW1ubadOmsWnTJqKjoxUdTqUgRgbKwJ49e7h27Rrbtm0jLCwMfX19RYf0zlasWMGUKVMICAigZs2aig7ng5WcnMyVK1eYNWsWdnZ2HDt2TNEhvZcePXoQGhqKj49PuV5X40MQFBSEo6Mjf/31F5MmTVJ0OO8sNTUVGxsbRo0aRbNmzfjoo48UHdIHTbwqS1lMTAxDhgxh7dq1tG/fvtzX+b7O2LFjMTIyYtGiRYoO5YN24MAB+vXrx+PHj2nRooWiw3lvc+bM4fHjx+VyXfwPzaJFizA2NmbMmDGKDuW9KCsr06FDB9asWcOQIUN4+vSpokP6oIlkoJQ9++Ofl5fHoUOH8PX1VXBE70dLS4uvvvqKLVu28OjRI+Lj4xUd0gdLKpWioaHBihUryM/PV3Q476VFixa0adOGBQsWEBYWRk5OjqJD+uDEx8fz8OFDtm7dyldffVVhL0c+4+vry6FDh8jLywOo8B+kyjuRDJQyNTU1ADQ1NTl9+nS53CTkbQ0aNAgtLS2GDBnCuHHjFB3OBykyMhIAOzs7bty4UXgeVWTjxo3jzp07NGzYkC1btig6nA/O2LFjGTp0KFpaWgwaNEjR4by3xo0bc/r0aTQ1NQHE5NNSJpKBUmZoaMj48eO5ffs2HTt2VHQ4JeKXX35BKpXy6NEjUlNTFR3OB+mTTz5h7Nix3Lt3DysrK0WH897kcjkzZsxAR0eHxMREsrOzFR3SByc1NZVHjx4hlUr5+eefFR1OiejYsSO3b99mwoQJGBoaKjqcD5qYQCi8tbi4OAYOHIinpye1atUiICBA0SEJFcCNGzfo2bMniYmJfP311yxZskTRIX1QatWqRVBQEK1bt2b//v0YGxsrOiShAhHjLsJbMzEx4cKFCwwdOhRzc3NFhyNUEE2bNuXRo0d07dqV9u3bKzqcD0737t2JiYlh9+7dFWapc6H8qJAjA+Hh4SQkJCg6jHLLyMgIa2trRYdR7ojz5uXEOSO8LfF6erWK9pqqcCMD4eHhODs5kpUtZiO/jLaWJr5+/hXqRCxt4eHhODs7kZUlrlW/iLa2Fr6+fuKcEd5IwevJmaysLEWHUm5pa2vj6+tbYV5TFS4ZSEhIICs7hxUfuWBvoq3ocMqdWyEpfH8sEE9PT5ydnRUdTrnh6+tLVlY262YMx8HaVNHhlCsB4bGM/22nOGdeIjc3V2yy9H8KXk9ZbP7jZ5zs7RQdTrnjFxTC6GnfkZCQIJKB0mZvok1di4q7kl9piEzJYcHpJwAVdhnS0uZgbUo9e0tFh1GuxCaloawkzpmXUVFWRiqTKTqMcsnJ3g53VydFhyGUgAqbDAjFJWXmkZMvY+2XfXCwEGU4z/O494T5f19SdBjlUmpmNjI5LB/oQC1jMdr2vPMBSSw+F86OHTvEqMlzfH19RfL4ganUycDFgESSsvIx19cgNCmbyOQcalfXpburCYM3eNHJyYgedUyQyWHOIT+2jar3wnYkUhmqKi9fssE7Mo1Tj+PJzJUyt5s96qoFx07d+xgXc13qW1ehgXWVEntcDhaGuNWoXmLtfQgCot59otP5u/4kpmVgbmRASEwiEXFJuNiZ07tFXdYfvUJSWib2FiY0rm3L9JX/sOfHFy/EJJFKUX3FLO97ARH89vdZZg3vQt2aFoW3rzp4iezcfJ5Ex/PN8C7M23iUxs62jOjchMTUDP4+fwddTQ0+69saNdV3n0Vey1ibOua673z/D1FgfME1cWdnZ+rXr6/gaD4cZy/fIDE5BQszE4LDIwmPiqGOUy2aN6zHlr2HiUtIol+3DliZmzHl+4Uc3Ljshe1IJJJXLkZ01/sxC1ds5Nup43Gr7cjsBX9iamxIQnIKv8z8AoCAJ6EcOXuR0Ihopo4bwbW7DwgMCaemjSWjBvcpjYdfLlXqRYfaOhgSFJfJ8YdxDGlYUCLXomZVAEx0NcjIlaKipISlgSa2hkU/MSVl5rPnTjQrLoZyISCJ9BwJ66+EF37dDEkuPPawdyxfd6xB85pVuR2WUni7sZ46Ofli+LG8a9/AEf+IOI5c9WZE54IVJFu71QKgVV17IuKS0dZUx8qkKnbVi47IJKVlsvPsLZbtPYfHHT/SMnNYdfBS4de1h//t1+7uYEWPZsW3hp7Yrw3GVXUZ1a0ZqsrKGOrrkJWTh7KyEltP3cBQXwfEUq1CBdKpdVP8gkI4dOo8nwzqDUDb5o0wMarGzImj6d+9A8HhkVhbmFHTpuiiW4nJKWzbf5Qla7Zw5tJ10tIzWL5pV+HXlVv3Co9tULc2vTq3LfxZIpWQnpmJUVWDwtscatoy/bNRtGjkTnRsPPq6OmhpaJCTk1uqz0F5U6mTAZlMTkq2BIlMjlRWtMJyxRAXRjWzZOuNyBfed+4Rf+LS8xhUvzqdnI3eqf+53eyZ3M6Wf+6JDTjKM5lMRmpGFlKpDKm0aPLmZGPGX1MGExAR+8L7zlh1gNikNIZ0bETXJi7vHMNt3zAaO9tiYWzA718MpGMjZw5evk9OXj4dGjhhYVSFaz5P3rl9QShLMpmMlNQ0JBIpUqm0yO98A4M5c+k6w/t1f+F9p85bzNO4BIb370n3Dq3eql9jw2p8N3UCsfGJRW73vOlFXEIirZs2oG/X9sz5chy5eXkEh7/4/f9DVKkvE2y4GsHwRuaoqiix7kp44e2xabns84ohLj2PXnVNXnjf1UNdScuR4OGbgLa6Cl1djPm05Ytnjfapa8rvHsFk5UmZ09WebTciGdnUktWXw0jPkeBSveIMzaZm5rD6+C1mDW5dJv2d9QrCJzSW9Kxc5o0oWKgmL1/K8iPXycrNp66dGd0bOfD7gavoaqnTo5Ej13zDSc7IJiE1ix9GvP/iNqsPezKya1NUVVRYdfC/eQcp6VlsOHaV3HwJtSxffJ5snPUxqZnZnL75GB0tDXo0c2VivzYvPDY4Op4LXv74hT3F1syQ/Re9GNOjOfcCInCvVfDpyD88lpM3HhIRl8yk/m1ws7dk59lbZObkMXNop/d+rKUlNVvChutRfN3epkz6OxeQxKOYTDJypczpbAtAnkTG6itRZOVLqVNdl+Z2VVh2KQIrAw0+bW7B0YcJhCZmk5SVz7xuNcokToCUlBSWLVvGDz/8UCb9nThxgvv375OWlsbChQsLb1+yZAkPHz5ky5Yt3Lt3j71795KVlcX333+PoaEhO3fuxMPDg82bN793DCs272bMkH6oqqrw18ZdhbdHPY1j9LTvGNavOze8vGnesF6x+27/awGpaRmcOO+JrrYWvTq3ZfKYYS/s50loBB6eN/ANCMbOyoLImFiWb9pFFf2C99z1O/fTqJ4rcxf+xaBenXnoF0R0bBz3H/kT/TQOS7PKU3lUqZOB8a3+++PtaKrLoQdPuRmSQufaxnzR1rbwd5EpOWiqFR9E0ddUpb+72Wv7qWupT13L/yofRjYtmM3+eevSe2PcePoOEqkcBwtDTAx02HPpIQBOVkb4hMby6+jOLNx7mc97NObc/WAi4lMwraqLpVEVDl17zICWLnjce4KJgS5qKsp0a+TAsoPXMDfUK9bXdd9wzj8IRi6TM7ZrQ77d6kG3hrW4ExiNvXk16tqZcfi6Lzqa6nRuYM/O8w9o6mRFCxcbLI30iUpM48gNv8L2WrnY4Gpb8CK86B3C/FGdWHviNmGxKdiYGqCupsLXA1oSmZDG7osP0FJXJS0rF20NNdRUVbgTEMUfE7rz9bqThMelYG1i8F7P5aTn/ng725jxz8V7XH8YTLemLkx/7g9wRFwyWhrFNxSqoqPF4PYNXttPDXNjNnzzceHPY3o0BwouH7g7FCQDjtamOP5faeTz8wve15abMUhlcmoZa2Gkq84/9+MAcDDR5tHTTH7qXoPfz4cxrpkFF4OSiUzOxURPDQsDDY4+TKBvHWPOByZjoquOqooSXZyqseJyJNX1i5fm3QxN5VJQCjK5nFFNqvPjyRA6O1XjXmQ6NYy0cK2uy7FHCeioq9DBoRp/e8XS2EafZnZVsKiiQXRqLscf/TcXpLldlcLE2vNJCj90q8HG69GEJ+dgXVUTdVVlprS1Iio1l71esfR0NeLTZuac8i34lNjLtWCEb+bhwBc+N6tWrUIikeDs7IypqSnbt28HwMXFhfv37xf+QZ86dSqnT58mNDSU6tWrY21tzd69exk6dCgnT57EzMwMNTU1+vTpw6+//oqlZfHqFk9PT86cOYNMJmPSpEl89dVX9O7dm5s3b+Lg4ED9+vXZt28furq69OjRg02bNtGyZUvatm2LlZUVkZGR7N+/v7C9du3a4ebmBoCHhwdLly7lr7/+IiQkBDu7gtLA6dOnM3XqVADOnTvHmDFj8PLy4ujRo9SoUYPq1atTpUrJzG36cuzwwn/XdqjJ3qOnuXrrHj06tubGsZ2FvwuPeoqmZvFzp4q+LkP7dnttPzVtrdj25/zCn//6eVaR3386fCAAlw9uKbzN1cmezm2av/Fj+VBU6ssE/6+vmxmdaxdfz9vSQJM5Xe0VENG7c6tRnTyJlLSsXDzuPWFMl/p0cC/6aedZuVRmTh5GVXS4GxgNQJu6dtQyNyQgMgE9LXWSMrK57htBzyaO9GlWfEb14eu+2JgYYGygS2hsMsZVtBnYyhUDXU0GtHAhKjGNLg1qMa1fc07dDkBDTYWP2tTB0ujdS0NjktJZe+IWX/RqSr5UhqOlEUPa1GGrhxcj2rux5vgt4lIyXjmx810NaOtOt6bFh/ytTKoyb3TPEu+vLNU11yVfKiMtR8qFgGRGNq5O21pVixzz7EpJVp4UI1017kVmANCqhgH2xtoExWehq6FCclY+N8PS6FbbkJ6uxS+lHXuUgHVVDYx11QlLysFIV41+biZU0VKlTx1jolNz6ehYjS9aW3LGLxENVSUG1jPBosq71/w/Tctl4/VoPmtZPIGSy+X8eTGCEQ1fPPm2QYMG5OXlkZqaysmTJ/n888/p2rVr0efm3yHvjIwMTExMuHnzJlCw4Y6TkxO+vr7o6+uTmJiIp6cn/fv3f+EOg/v27cPOzg5TU1OePHmCiYkJw4YNo2rVqgwdOpSIiAh69uzJ7NmzOXLkCBoaGnz88ccltqnVyJEjOXToEPfu3UNNTY0LFy7g7e3NvXv3CAkJKZE+nje4Vxd6dCw+2mhtYVY40U8oXZV6ZOBlUrPzWX8lgumdymao8JxfAo9iMkjPkTC3W0HSkSeRsfpyGFl5UupY6FHfugprL4ejrKzEiMYWpGTn4xWeyqlH8fwxqDbW1YruXZ6ckY2Wuir+kQl0b+TAxtN3UUIJJysjalubsOn0XR4EF8xVCIxKpI6dKZJ/3+WVlZQwqqKDk7UxaVm5OFka0aCWBetP3sG0avFLGn2aOXPJJxTjKtrYmBgU2XdcWVmJ5s7W/HX4OlcehdG1kQP/XHlU5P4Whvp83uPFWzu3rWvHsoPXSMvKxcbUgM1n7jKkTV2GLtxD/xYuXHkcRps6dizYc4nIhFTau9VAIpMjkclo6WqLuWHZrUWRkpHN6kOXmD2i6+sPLgFnbj3GJzia9KwcfhhTkITk5Uv4a/8FsnLzcKtpSUMnGw5fecDj0Biau9bE2cbshRULhY8hOx9NNWUC47Po7FSNrbdiUKJgZMDZVJutt2LwiS744x8Un41LdR0k/863UVZSwlBHDUcTHdJzJTiaaONuqcfmGzGY6KkX66unixFXglMw1FHHuppm0fNGSYmmtlVYdSWS6yGpdHYy5JBPfJH7m1cpGN5/kVY1DVhxOYL0HCnWVTXZdiuGQe4mfLLjMX3qGHM9JJXmdgb87RWLX2wmbeyrsv9+3L8JTCou1XVQUS46KTMpKQktLS0eP35Mnz59WLVqFUpKSri4uFCnTh1Wr17N3bt3AfDz86NevXpIJJKCx6OsjLGxMa6urqSmpuLi4kKTJk1Yvnw51asXTz4GDRrEuXPnMDExoUaNGigr/5fUKisr07p1axYvXszFixfp3bs3u3fvLnJ/S0vLwk/5/69jx44sXLiQ1NRU7OzsWLNmDZ999hk7duzg3r17nDlzhtq1awOgoaFBv3790NYumEAdGhqKnZ0dycnJL2y7pKWkpbN80y6+mzqhTPo7deEKDx4HkJ6RyS/fTAYgLy+fpeu2kZmdg7uLI/27dyQkIoqRk+dwdNsKbnp5c9fbl8TkFBbOmYqaWsX8s1rh9ibw8vKiQYMGnJrcCK/wNCQyObVMtDHWVWf/vxPxHE10eBSTwU+9HFhyNphPW1pxMSCJiORsTPU0sDDQ5KhPLH3dTDnvn4ixngZqKkp0qW3M8guhVK+iQU6+rEgycDMkmUuBScjkMKqZJT8cC6BzbWPuhadSw1ibOub6HPOJRVtdhY5ORvx9J5rGtgY0q1EVCwNNolNzOO4TV9he8xpVcTEvGHL/4VgAP/R0YMPVCDo7GxX5wx6VksPeuzHUMtFGT1MVPQ1VzvsnMr1TDWQyOd8c8uO3/gWf1r2j0ui6/DYXFo15YWmhT8hTfEJjGdbOrVT+b8qzfZ4+TPjrCBeXf8UdvzAkUimOVqYYV9Vjz7k7QMFkQJ8nUSz8rB+/7jjF533bcP6uH+GxSZhW08fSpCqHLt9nQNv6eNzxxaSqHmoqKnRv5srSPeewMKpCdl5+kWTg2sNgznv5I5fJGderBXPXHaZbUxfu+Idjb2GMm70lhzwfoKOpTpfGtdlx5iZNXWrQsm5NLI2rEhWfwuErDwrba+VmT50aBX8A56w7zILxfVhz+DJdm7hga/ZfJUNkfDK7zt5m5rDOQMFExh/H9ERbU52dZ29Rp4ZFYTKw98Jdxi/eyanP672wtPBhTAaPYjL5qH7luX76zIEHcUzeH8Ddu3dfWFp4//597t+/z6hRo8o+OAV69j58/egO3F2dWLt9HxKJFEd7W0yNDNl18AQAzg418H4cwJLvv+bnZWuZPGYYZy9fJywyGjNjI6wszPjnuAeDe3Xh9KVrmBkZoqamSs9Obfht1WYsqpuQnZNbJBm4cuseHp43kMlkfDZyMN/8spQeHdtw+/5DatlZU8/ViQMnPNDR1qZbu5Zs3XeY5g3r0bppA6zMzYiMieXgyXOF7bVp2pC6tR0AmPnLUhZ/+xUrN/9N946tsLP6L9mMiH7K9v3HmDJuOGu37yMzO5vJY4bxy7J1/PLNF6zYvJtG9Vxp07Qh9x760azXiJeeN+VRhb5MUNdCj3ypjPQcCef9E/mkiSXtHIqWdsn+zXUy86QY6arjFZEKQCv7atgb6xAYl4WehgrJmfncDEmhu6sxveoUf9M76hOHVdWC66hhiVkY6arTv54ZVbTU6OtmRnRqDh2djJjczpYzvgmoqyozsH51LAw03/nxPU3LZcPVCD5rbU17RyMex2RwIyQFNZWCTyzn/BNp7/jmlQx17MxKJBHIy5ey68KDwq+Kpp69Jfn5UtIyc/C47cvYHi3o2KDoKmrPqksycnIxMtDjrn/BBNM27g44WJrgHx6LnrYmSelZXH8YTK8Wdejbql6xvg553sfGtBrGVXUJiUnEyECXQe0aYKCrxcC27kTFp9ClcW2++qgDJ288Ql1NlSEdGmJpXLVYW28qJjGVNYc8mTygLVBQ3qijqY62ZvFP52/CtbpuiSQCeRIZe7xiC78+BPXq1SuRRCAvL48tW7YUflU07q7O5OXnk5aewelLV/l0xAA6tWlW5JhnlTgZmVkYG1bj9v2CEcL2LRrjWNMW/6AQ9PR0SExO4erte/Tp2p7+3TsW6+vACQ9srcwxNTIkOCwSY8NqDOnTFYMqegzu3YXI6Fi6tW/FzImjOOZxCQ11dYb374GV+evnd71MdGw8Kzf/zbTxI7h84y75Egm37j3k/JWbfPbxINZs20dQSARqr1jzoLyruJHz35BmQGwmXWobs+VGJEpKBSMDTma6bL0RiXdUOgBB8Zm4VtcrfJNXUlLCUFcdJzMd0nMkOJrq4G5dhU3XIjDRK35NslcdEzyDkjHSVce6mhbKRYY0oamdAasuhXEtOJnOzkYcelD0zc68iuZLqw1a2Vdj+cVQ0nMkWFfTYtuNSAY1qM7ILQ/o42bK9eBkGtsaIJNDdp6UkU0LslUPvwQW9HEskefybairqRRJKq48CiM1M4cejd89FolUxpJ/rpCamcOvozuz68IDnsQkYWdWlRHt6/H7P1fQUFcl9GkySz59/cShV0lOz0JTQw2/8Kd0b+bKhmNXUVIqGBmobVedjceu8iAwAoDAiDjq1LBA8u+1YGUlJYwMdHG2rU5aZg5O1qY0dLJh3RFPTKsVvyTRt1U9Lt0PwNhAD1uzakXOGyUlZZq71uDP/Re44h1Et6Yu7L/oVeT+FsYGL60+aFuvFn/sPUdaZja2ZoZsOn6NoR0b8tG8DQxo484V7yd0auTMzrO3GdapEVC8YkFf592T1belrqpcJKm4FpJCWraUrrXffbVMiVTOn5fCSc2R8lP3GnhHZXDKN5HMPClzO9tyxi+JmLRcjj1K4PCn5XdETF1dvUhScfHiRVJSUujbt+97tbty5UoSExNxcHCgZs2a7Nq1C5lMxpgxY4iPj+eXX37h0KFDGBgYvFc/yampaGlq4BsUQq+ObVi3fT9KSko4O9TAxbEm63bs595DXwACnoRRt7YDEulzl1AMq1LboSZp6RnUdqhJo3qurNq6h+rGxT/s9O/ekQvXbmFiWA07a4til1BaNnFn6dptXL5xl54d27D36Oki97esbvrS6oP2LRrz2+otpKZnYGdlwfqd+xkxoCf9x05lUM/OXL5xl27tW9KtfUvy8tfSvmUTop/GIZfLsbUyp1mD8nuOvU6Fvkzwor0JHkan8yg6nY/+XUSoMnndZYKXeb7yoGb1apy+G0hAVCI/fdyBL1Ydo0Vta+49icG9ZnX8IxNYNLYLXeZuYWyXBiSmZ1PH1pTUzBwkUhnBT5NIy8xlxqBWfL/Ng9rWJnRuUKtwsuDh675EJxUkaFrqqozqVHQIbfbmM/w6ujNHb/rhFxFPVV0txnVtyNIDV8nJkyCVyfhuWLu3fm6ev0zwor0JvJ9E4RMcxfBOL5678CF73WWCl3m+8sDOUAsP/yQC47P5rqstXx0IpKldFbyjMqhroUtgXBa/9KxJ73UP+KRxdZKy8nGprkNathSJTE5IYjbpuVKmtbXip9MhOJnq0NGxWuFkwWMPE4hJK1gERlNNmY8bFT2/vz8RzE/da/DL6RBmd7LlnH8SOhoqtKhhQEhiNscfJfBF67efXPe6ywQv83zlQa1atTh27Bi+vr4sWbKE0aNH06ZNG+7cuUPDhg15/Pgxy5cvp3nz5kycOJGEhATq1atHSkoKEomEwMBAUlNT+f7775k+fTp16tShZ8+ehZMF9+/fT2RkQT28lpYWEyb8N6T++PFjfv/9d/r27Yu1tTXLli1DSUmJhQsXYmJiUlj98LbJwP9fJniRB4/9efA4gJEDe71V2x8CcZmgHHA11yuRRCBPImPPnejCL4BrT5I5+Sj+Nfd8veUXQ1nrGY5PVDpP4rNY5xnO7EN+JGbm8ef5ENZcDmP2Ib/XN1RCnq88yMrNR1lZCZlMTsjTZAz1tBjTpQHqqiqM6dKgcLnbmtWrMbh1HQKfW+b3zN0gTA10qaKjSVRCGm41qpOckY3sHTZ56dXEiRkDW5GbLyU0NhklJZgzpA1yOYUTHUtS3ZoWJZII5OVL2Hn2VuGXp3cQx675vFebGdm5TF62h9//9mDb6RtExCXz/cajzFl3mISUDC7eC6DHzJWkZJTt9szPVx5k58tQUlJCJpcTmphDNR01PmlcHTUVpcLvAHaGWgyoZ0JQwn+xegQkYaKnjr6mClGpudQ11yUlS4JMVjKfU/Z4xTKkjOc7PF95kJWVhbKyMjKZjKCgIIyMjPj8889RV1cv/A5Qq1YtRowYgZ/ff6/9Y8eOUb16dQwMDIiIiKBBgwYkJSUVW6jnZWrXrs369evx9fUlMDCQOXPmMGXKFM6cOVMqj/t5brUdSyQRyMvLZ9v+o8xdtJxpP/z2Xm1JJBJ+XraW6T/9DkBgcBh/bdzJl98tJCEphQMnPOg0ZDxA4cqGsxf8yS9/rnvvx1HeVejLBG9ry/XIwgmHNQy1OeuXQGBcJt/3qMW0fY9pVqMqDyLTcLPUJyA2k/l9HOm16g6jmlmw/ko4LtULJvwd84klJCGbtBwJ0zrY8fOJQJzMdOnoZFQ4R+CYTywxqc99kmlS8GnUPzaDwNhMXM31UFNRoqaxNg+jNbgZmoKaijJKSkqk5UjQ1yy7/5rnKw/kgKa6GjK5HKlMhsq/pXnqagVJwLNR7icxSWw4dQdb0/+ubXdtVIug6ET0tDQwq6ZLZk4eeRIpYc/V+r+oNPGZjafv4BMSy+2AKFIzc/AJfUpMYjrm1fTJzM5j1bGbr90HoqRtOHa1cLJhDQtjTt98REBEHD+P68WkpX/Tok5N7gUWLArkFx7LbxP7s/XkDcb2bE5iaib8+zfosOcDnkTHk5aZw8xhnfluwxFq21anS5PahfMDDns+ICohBQAtDTVGdy+odVZVUSYjO5f4lHQa17blyFVvxvduSUpGNqduPWZE58ZcfxT8ovBL1fOVB3JAS00ZmRykcnnhTPxn+3A8qxQIScxmy81obKv9d3mis2M1niRko6epgpm+Opl5UvKlMsKTc7CqWnDci0oTn9lyM4ZHMRncjUijt6sxSy+Ek5knZU4nW3IlMtJzC+YLlaXnKw/kcjlaWlrIZDKkUmnhWvrPtkV+9twEBgaycuVKatasWdhO79698ff3R19fH3NzczIyMsjLyyMkJARbW1sABg4c+MIYkpOTWbVqFbm5uTg6OmJsbMzq1atRUVFh7NixeHl5cePGDVavXs3s2bNL8dko8PwkQ3s7a06e88QvKJSFc6cwfsaPtGrSgLs+j2lQpza+gcH88eNM2g4Yw/gRA0lNS6dzm2akpmVw4IQHQaERpKVnMOfLT5m1YBmujvZ0a9+ycF7AgRMeRD0tmLCtqanBp8MGAKCqqsp3UycUJgO1athw/7E/V2/fR01Vlf7dO3LtTsE8KH09XSaPGcbPy9YWLpn8IatUyUBdCz1uhqaQniMhO1+KspIScjmEJmZTTUedT5paMuugH580teTHYwULj9Qw0mKAe3W+OehXmAx4+CbSrIYBKEF0ag51LPSJT899o08y+VI55gaajG1hxZzD/izq50QfN1N0NVSISslBCZjZuSYLTgWV2R++Tu7F11AY/u+cgF/tCl5cv44umJX+88iCCT317c0Z17XhK9v97CXlgi8ztktDxnb5r82O7v+9KX77DpcGSkI9e0uuPwwmLTOH7Jy8fz/hyQmOTsCwig5je7bgq+X7GduzBXPXHwaghrkRH7VvyLTl+6jz74z9U7ce07JuTZSUlIhKSMGtliVxSenFlsF+kfiUDLo1cWFQu/r8sOkYJi+Ym6AI7R2qFbvt2ZwA138X//mpe0FFzvddCxa2qWepx6gmrx65G9fs7RZRGtWkOqOa/HfZoK5F0Usd83vW/P+7lLpu3YrPaxk9ejQAy5YtK/J9yZIlADRu3JhJkya9st0pU6a8cQxVq1Zl7ty5RW5r06bo/JNTp069cXvvy93Vmau375GWnkF2dk7Ba0ku40loJIZVDRg/YiCTv/2V8SMG8s0vfwAFiwYN69edL+YuKJzxf+L8FVo3bYCSkhKR0U9xd3UiNj7xnbeZHtSzM3o62kREP6WKftH3QolEQlxC0ntNPqwoKlUy8PyEQ7m84BN7wSdgOar/90mGfz8BBydks/l6BDbPlft1rm3Ek/gsdDVVMNPXICtXQp5UTnhyNlb/HtfzBRUJALXNdPnn3lNWXQ6jRc2qXAlK4kFUOmGJWUzvVIPMPCnrPMORyORl+gn4dXZdeEAdW1Pq/F9y8LbuP4nhyuMwQp8ms3BMl8LHuPzIDfzC41n5RS9ikzM4cO0xYbEpjO3SgIiEVPwjEzhw9RGHvh+OzjvOin8Xz082lCP/d9REhlQmR0X5/0ZN/j1pgqMTWH/0SpFNi7o1dSEoMg49bU3M/h3pyJNICXuaiI1pwR/VPq1ePPlIW0ONS/cDiUpIoV4tKxo62fyvvfuMjqrqAjD8pvfeCymEJCShhN6r9KJ0ARWQpiiKCKiA4ifSBakqHUQBQenSS+gdQgvplfTe+8x8P0JCYigBkkzCnGetrIFkcs+eu2Yme87dZx82HLxIoVTK1KFvcScokpt+4Ww+fIkv3y1ffS1Pu27H4WGlUy45eFn3ojK5HJZKeHIuP/ZxQvXxZYe8QinT9wXS18OUlvb6ZdoLQ9E1/wvBqSwf5FI5D6gSbd26FU9PTzw9PYEnycHL+vvvvwkKCiIxMZFly5aVfH/QoEF07NiRHj164OzszPz589HT02PAgAEkJSVx8uRJDA0NX5iAVJbSRYYyWdEndqlUikRaarbk8SWT4tmS4LBHrN22m7p2T+p8+nXrSEBIOPq6OlhZmJGVnUN+QSFhj6JxsC1KNJ+2CqHYuj/+5o6PP9e875Odk8vtew8JiYjku6kfce7qTe74+LN19wHGDHuHgyfO0r/70wt43zQKlQw8bRle8W6FDR6v+Z/bv+hN4/u+RbvSNamjz4dtnl94NP4ZqwSeRllZqeTYxdrXe/IJ65ue1f8pptj3f5zm2xGd2Xj8Fm+3rs/pO8HEJGfQrN6TT2rFBX7Ftz/u8CppWTy2Z1HL3ee1F/Z0suJWUBQpmbllKus/e7s1M7cUXce0MNLF1daUq76PUFdVoWvjujR3tiEyIa1aEwGA7i3KX9Yo3rmwkdNAABZ9XHQ7b0LRVGJTlzpM6N/+ucedNKDiezuYGOjyy5fDy3zvh3FPrsWaGuqyZ171NGV5mnnHQ/m6mz1br8XQ18OUs4EpxKbn06TOk9bVxQV+xbcLT4aVtCwe3bLoU/3z2gs3stHldmQGqdmFlO4FtOVaNP0bmiKVgrGOWpn2wlfD0jDXU0evGi+5/ddXX33F/Pnz+eWXXxgyZAjHjh0jKiqKVq1aldzniy++YMWKFSW3s2bNKmlZPGnSJIDnthcu7mA4ceLEMmNbWlqSmZmJiooKJ06cIC0tDR0dHdTV1dm6dSseHq++cdar6Nm5XbnvFU+/N55TtBJp6ZxpACya/QUAzRt78PGoYc897uQPR7xUHB99MJSPPnjS9bFL2xYl/7Y0M6XTriezk89LKt40NeejZw1VnBwogj4tXTl8I4CYpAwsDXXJyy+kjqkBF33Cy91XKpWRmJZVpmVxRY3r2ZxOjRxIec7vdG1cl6+GtifgcYHi7vP3Gd6p4cs/KDkoTg4URU83E475JhObno+Fnjp5hVJsDDW4HJJa7r5SqYykrIIyLYsrakwrKzo4GZKaU7QkLSotj/iMAs4FpXLhKWNdDknDNzYLn5hMIlJyX/XhvZYBAwawf/9+oqKisLKyIjc3F3t7e7y8vMrdVyKRkJCQUKZlcUXIZDLmz59fZhUBFK1omDlzJr/++isFBQW4u7szatQo1q9fT1xcHGPHjiUzM5Pg4Jq722VxciBUPYWaGaioXTej8bDWK5kteFX3ItO5HJJCeHIOP/Z3KZkS/+d2DDHpeeioq9DjcddDQy1VWjsa0cX11ddcv65WrrYMmbeTib2bk51fQFRSBq62pmVqISwMddl59h4BUUnlWhYXe1574X+v+RESm0JITDJD2zdgy4lbfNijGbvP3+d+aBxn7oZgoqfF2XuhRCWmM6ZH0bKc+2FxL6xRkJf/dvR7VXcCH3HhXjBhMYksnjQQVZWiyw8L/zyGlroaDlYmNHGx4/tNh2jp5sD7PVpx8X4QtwMekZCSwcwPemEph3qCFnb6vPf7Az5sbU1OgYTotDyczbUpXQ5hrqvGbu84ghJzyrUsLva89sJHHyYSlpxLaFIOAxubse16DKNaWjGnl2NJv4LcAmmZ9sJfdi2asXuUmoedUfX1Uyitbdu29OrVi88++4zs7GwePXqEu7t7mRU2lpaW/P777/j5+ZVrWVzsee2FZ86cWbLXgaenJxs2bGDMmDH8/PPPpKen07lzZ7p168Z3331HREQEPXr0oF27dixbtoyEhIRK28+gsmz75xCN3V1o7P56PVT2HD5FcPgjkpJTWfzt1JLvr968g+ycXILCHvHdFx+x5NfNGBka0KFlEwz09bjufZ+DJ86y/qfvy3QgfNMpbDLw45FAvunpxNYrkfRtaI6XfxKx6Xk0rfNkV645hwKY29+l5HbhsaCS1sWjH+88+Lw2w41s9bn9KJ2U7IIyU+JtnYxYez4CAzNtVJWVyMgtRCaTYSunN6zS/vn2yZTb07b/La4ZGNG5EQCzh3d+qeP3a1V2TfKHPYouLQzr2JBhHZ988v9vn4SVH/d9qXEq25xNh/hudB82HLrIO+0bc/qWH9FJaTR3fXKJ6Ju1+1j08cCS27lbD5e0LB7Xr2iK9HnthT2d63DTP4KUjOyS50tqRjYqysp8MewtZq7bT4v6Dpjo65Cdm4+yshKaamqkpGeRXyjBWE8bedk+ukHJv2f3dCz38+KagWFNii4XfdXt5Xbs7O1e9hLfqJZPnh9tHQ1L/j3jrfLHfdU6hcpSukhv8eLF5X5eXDMwevRoAH788ceXOn7pbYgBPv74YwBmzZpV5vvLly8v8///brJUnWYtXMkP0z9l7R+7Gdj7LU6cu0J0XDwtPZ88j6bPXcbSOdNKbr/76ZeSdsUT3y9aQfG81sKD+xZN8X8ycz6lfTZ2JFt27adTm+aoqqqQnpmFTCbDzsaK+vUcadHYA9/AUIVKBECBLxP08jDjmE8CMel5WOhpkFcoxdZQk0sh5TfgkEhlJGXml2ldXFFj2tjSoZ4xKTlPfsfaQJO5/V1Iyy0kKjWX91pa820f5zJJhVCz9G3TgMNX7hOTlIalsT65+QXUMTfiwt2gcveVSGUkpmaWaVlcUeP7taNzExdSSv1O6Q18bMwMWTZ5CN1auLHv/B38H8Xx4/i36de2YUnLZEGo6fr36MzBE2eJjk3AytyUvLw87GysOHflZrn7SiQSEpJSyrQrrgiZTMaiNZsYP3JQuZ9du32f1k0b8Sg6lrHDB7LgmyklScUxr0v06lK+vuFNp7AzAy3sDRm52ZuxbeuUTG26mOuU7GUAYKanzu5bMQQlZJdrXVzseW2Gjz6IJzQph9CkbAY1sWTb1UhGtbbl51MhKCspoaOhgr6mKrtvxXApOIUO9cov1XoVAVEVu9aoSCLi017r91u5OzL423VMfLsD2Xn5RCWk4mpnWeb5YmGsz46TNwiMjC/XsrjY89oLH7p0j9CYRIKjEhnapSmbD19mbN+2SKRSVv59hlbuDvhHxHH06gMexafw6aBO3A54VNSSODuXGcO7v9ZjDEyoeNKiKB49rjXw9fWVcyQ1y+uejzbNGtN/9Gd8MvpdsnNyiYyJo75z3bKXJM1M+GPPv/gHh5VrV1zsea2Fv1uyhqSUNC7d8Kaxuwub/9rHhPeGcOveQ5o1KioMNtTX449/DnHuys2SQsIjZy6wcu7Xr/X4aqM3rh2xIotMzaXjsivkFlR+h743xbPaESuy49cfMuJ/G6mkhn9vHBVl5Vdew/6me147YkVWG9sRK+zMwJvI1lCT9e81YNTWe/z555+4uT2725+i8fX15f3335d3GDWShbE+UhniOfMMeXl5Jd0ChSLi9fTmqbXJQFC8mNJ8mvScop7lbm5utSYjrU4BEW/G1rmVqficiOeM8LL8gkLlHUKNVBvPS61LBkxNTdHW0mTyLh95h1JjaWtpYmr67F7uisjU1BRtbS0m/rRd3qHUSNraWuI5I1RY0etJmw+nfifvUGosbW3tWvWaqnU1AwAREREkJia++I6V4OLFi0yZMoW1a9fSokWLF/9CKStWrGDv3r0cPnwYPb3X61nwMkxNTbGzq3hXREVRnc+bxYsXc+zYMQ4fPoy2dsWX/EmlUt59910sLCxYs2ZNFUZYlnjOCC+rOl9PGRkZ9O3bl8GDB7/U/gwA169fZ9KkSaxatYp27apvlUBte03VymSgushkMtq3b49UKuXy5ctllnhVRExMDI6OjsyZM6fcml/hzRUXF4eDgwMzZ85kzpw5L/37O3bs4L333qtVxUeCUJXmz5/Pjz/+SGhoKFZWVi/+hVJkMhlt2rRBTU2NCxcuVFGEtZ/C9hmoiPPnz3P58mVmz5790okAgJWVFWPHjmX58uVkZWVVQYRCTbR8+XLU1NT47LPPXun3hw0bhpOTEwsWLKjkyASh9snKymL58uWMGzfupRMBKOrTMXv2bC5evMj58+erIMI3g5gZeI4ePXoQHx+Pt7f3KyUDAGFhYdSrV4+lS5c+s52o8OZISUnB3t6eTz75pFxnuJexYcMGPvroI3x8fESFv6DQli9fzldffUVQUBD29i/XubKYTCbD09MTS0tLjh8/XskRvhnEzMAz3Lhxg5MnTzJr1qxXTgQAHBwceP/991m6dCl5eXmVGKFQE61Zs4aCggKmTp364js/x6hRo7C2tn6thEIQaru8vDyWLl3K+++//8qJABTNDsyaNYsTJ05w82b5LoeCmBl4poEDB/Lw4UMePnyIyuMNY16Vn58f7u7urFu3jgkTJlRShEJNk5mZib29PSNHjmT16tWvfbyVK1cybdo0AgMDcXQs3+9fEN5069ev5+OPP8bX1xdX19fbuEgikeDm5kaDBg3Yu3dvJUX45hAzA0/x4MED9u/fzzfffPPaiQBA/fr1GTx4MIsWLaKwsLASIhRqonXr1pGens6MGTMq5XgTJkzAyMiIJUuWVMrxBKE2KSwsZPHixQwZMuS1EwEAFRUVvvnmG/bt24ePj1ia/l8iGXiKRYsWYWdnV6kdtmbNmkVISAi7d++utGMKNUdubi7Lli1j1KhRlbacSFtbm6lTp7J582ZiYmIq5ZiCUFvs2rWLkJCQSl2J9f7771OnTh1x+e0pxGWC/wgODsbFxYWVK1cyefLkSj12nz59iIiI4N69eygrizzsTbJ27Vo+/fRTfH19cXFxqbTjpqWlYW9vz/jx41m6dGmlHVcQajKpVErDhg1xcHDg8OHDlXrs1atXM3XqVAICAqhbV77bW9ck4i/SfyxevBgzMzPGjRtX6ceePXs2Pj4+HDx4sNKPLchPQUEBixcvZujQoZWaCAAYGBgwefJk1q5dS1KS2I1SUAwHDhzg4cOHzJ49u9KPPX78eExMTFi8eHGlH7s2EzMDpURGRlK3bl3mzZvHV199VSVjdO7cmaysLK5fv/5aqxSEmmPbtm2MHj2au3fv0qhRo0o/fkJCAg4ODkybNo25c+dW+vEFoSaRyWS0aNECPT09vLy8qmSMxYsXM2fOHEJCQrCxsamSMWobMTNQyrJly9DR0eHjjz+usjFmzZrFzZs3OXXqVJWNIVQfqVTKwoUL6devX5UkAgBmZmZMnDiR1atXk56eXiVjCEJNcfLkSW7dulWlXVsnTZqEtrY2y5Ytq7IxahsxM/BYQkIC9vb2zJgxgx9++KHKxpHJZLRs2RIdHR3Onj1bZeMI1WPPnj0MGTKEK1eu0Lp16yobJyoqirp16zJ37ly+/vrrKhtHEOStU6dO5OTkcO3atSqdPZ0zZw7Lli0jPDy8Vm0oVFXEzMBjK1asQFlZmc8//7xKxylujXnu3DkuXbpUpWMJVUsmkzF//ny6du1apYkAgI2NDWPGjOHnn38mJyenSscSBHkpbhn8qi3gX8aUKVNQUlJixYoVVTpObSFmBiiq2Lazs2PChAnVUrEtlUpp1KgR9vb2lV4pK1SfY8eO0bt3b06dOsVbb71V5eNV5UoXQagJqnvF1bRp09i0aRPh4eEYGBhU+Xg1mZgZAH755Rfy8vKYNm1atYynrKzMzJkzOXLkCN7e3tUyplD55s+fT6tWrejatWu1jOfk5MSIESNYsmQJ+fn51TKmIFSX27dvc/ToUWbNmlVtS6+nTZtGTk4Ov/76a7WMV5Mp/MxAVlYWDg4ODB06tFqfEIWFhbi6utK0aVP+/vvvahtXqBznz5+nU6dOHDx4kP79+1fbuD4+PjRo0IBNmzYxduzYahtXEKrakCFDuHPnDn5+fqiqqlbbuJMmTWLPnj2EhYWhra1dbePWNAo/M7Bx40ZSUlIqrYVsRamqqvL111+zZ88e/Pz8qnVs4fUtWLCAhg0b0rdv32od18PDgwEDBrBo0SIkEkm1ji0IVcXX15e9e/fy9ddfV2siAPDVV1+RnJzMxo0bq3XcmkahZwby8vJwcnLirbfe4vfff5fb+N26dWPr1q3VPr7wam7evEmLFi3YuXMnw4cPl9v4f/31F++++261jy8IlW306NGcPn2a4OBgNDQ0qn38UaNG4eXlRXBwMOrq6tU+fk2g0DMD27ZtIzo6mpkzZ8plfA0NDaZPn86ff/5JWFiYXGIQXt6CBQtwdnZm6NChchm/efPm9OjRgwULFqDAubzwhggNDWX79u3MmDFDLokAwMyZM4mKimLbtm1yGb8mUNiZgcLCQurXr0+TJk3kes2+uGZh2LBh/PLLL3KLQ6iYmnLNvrhm4dChQ/Tr109ucQjC6/rkk0/4+++/CQ8Pl+s1e3nVLNQUCjszsHv3boKDg6u0y1VF6Ojo8MUXX7Bp0yaxM10tsGjRIurUqVOpO1q+ig4dOtCuXTvmz58vZgeEWismJobNmzczdepUuRfvzZw5k+DgYIUt6FbImYHidf52dnYcOXJE3uGQmpqKvb09EydO5KeffpJ3OMIzhISE4OLiwvLly/nss8/kHQ5Hjx6lT58+nD59utqWNwpCZZo+fTobN26sMev8e/fuTWRkJHfv3lW4nWUV69E+dujQIXx8fKpkR6xXYWhoyOTJk/ntt99ITk6WdzjCMyxevBgTExPGjx8v71AA6NWrF02aNGHBggXyDkUQXlpSUhJr165l8uTJNSIRgKKdZR88eMC///4r71CqncIlA8UtZDt27Ei7du3kHU6JL774AqlUyqpVq+QdivAUUVFRbN26lalTp6KlpSXvcICi1tazZs3i9OnTXLt2Td7hCMJLWbVqFTKZjClTpsg7lBLt27enQ4cOCnn5TeGSgVOnTnHjxo0aMytQrHhnulWrVpGRkSHvcIT/WLZsGdra2nzyySfyDqWMQYMGUb9+febPny/vUAShwtLT01m1ahUTJ07EzMxM3uGUMXv2bK5fv87p06flHUq1UrhkYMGCBTRv3pzu3bvLO5Rypk+fTmZmJmvXrpV3KEIpiYmJrFu3js8++wx9fX15h1NGcWvrQ4cOce/ePXmHIwgVsnbtWrKzs5k+fbq8QymnR48eNGvWTOEuvylUMnD58mXOnj1bLTtivQpbW1vGjBnDsmXLxM50NciKFStQUlKqUdOZpY0YMQIHBwcWLlwo71AE4YVycnL4+eefGTNmDDY2NvIOp5zinWW9vLy4cuWKvMOpNgq1mqBv376Eh4dX245YryIoKAhXV1dWrVrFp59+Ku9wFF5aWhr29vaMGzeOZcuWyTucZ/rtt9+YPHkyfn5+ODs7yzscQXimNWvWMGXKFAICAnBycpJ3OE8llUpp2LAhjo6OClNMWDP/IlaBO3fucOTIEWbOnFljEwGAevXqMXz4cJYsWUJBQYG8w1F4v/76Kzk5OdW2o+Wr+vDDD7GwsGDx4sXyDkUQnik/P58lS5YwYsSIGpsIwJPLb4cPH+bOnTvyDqdaKMzMwLBhw7h16xb+/v41vrvUgwcPaNiwIVu2bGHMmDHyDkdhZWdn4+DgwODBg/ntt9/kHc4LLVu2rKRxSp06deQdjiCUs2XLFsaOHcuDBw/w8PCQdzjPVVhYiIuLCy1atGDXrl3yDqfKKUQy4Ofnh7u7O2vXrmXixInyDqdCBgwYgK+vLw8fPkRFRUXe4SikVatW8eWXXxIYGIijo6O8w3mhzMxM7O3tee+998QSVaHGkUgkuLm54eHhwb59++QdToWsW7eOSZMm4evri6urq7zDqVI1d768Ei1evBhra2tGjx4t71AqbPbs2QQEBLB37155h6KQ8vPz+emnnxg5cmStSAQAdHV1mTJlChs2bCA+Pl7e4QhCGXv27CEwMLDGLet+njFjxmBlZaUQl9/e+JmB8PBw6tWrx08//cQXX3wh73BeSo8ePYiPj8fb27tGrn54k23cuJGJEyfi4+ODm5ubvMOpsJSUFOzs7Jg8ebJYXSDUGDKZjCZNmmBhYcHx48flHc5LWb58OV999RVBQUHY29vLO5wq88bPDCxZsgRDQ0MmTJgg71Be2qxZs7h7926N2D9BkRQWFrJo0SIGDhxYqxIBACMjIz755BN++eUXUlJS5B2OIABw+PBh7t69K/eN4V7FxIkTMTAweOP3jXmjZwZiY2NxcHDgu+++q1VTU8VkMhnt27dHJpNx6dIlMTtQTXbu3MnIkSO5efMmzZo1k3c4Ly0uLg4HBwdmz57Nt99+K+9wBAUnk8lo27YtKioqXLhwoVa+j82bN4958+YRFhaGpaWlvMOpEm/0zMDPP/+MhoZGrV2vX9z84sqVK5w7d07e4SgEqVTKggUL6NWrV61MBAAsLCwYP348K1asIDMzU97hCAru7NmzXL16tcY2e6uIyZMno6GhwfLly+UdSpV5Y2cGkpOTsbe35/PPP6/VfdtlMhlNmzbF1NSUkydPyjucN96BAwcYMGAAFy5coH379vIO55VFRETg5OTE4sWL+fLLL+UdjqDAunXrRnJyMrdu3aq1yQAUXbZdvXo14eHhGBsbyzucSvfGzgysXr0aiURS64oG/6t4Z7pTp05x/fp1eYfzRpPJZCxYsIAOHTrU6kQAwM7Ojg8++IClS5eSl5cn73AEBXXt2jVOnz7NrFmzanUiAEU7y0okEtasWSPvUKrEGzczEBcXR2xsLF26dGHUqFGsWLFC3iG9NolEgoeHB/Xr12f27Nk0adKkxjdOqm0ePnxIQEAAAwcO5NixY/Ts2VPeIb22gIAA3NzcWLVqFa1bt661lz2E2qewsBBvb2/mzZtHQEAAPj4+Nbrza0VNmTKFP//8Ey8vLywsLLCwsJB3SJVH9ob5/PPPZY6OjjJVVVXZ2bNn5R1OpZkzZ44MkCkrK8uOHj0q73DeOM7OzjJHR0eZh4eHLC4uTt7hVIqMjAxZjx49ZObm5jI1NTVZfn6+vEMSFMTRo0dlysrKMkA2Z84ceYdTac6cOSNTVVWVOTo6yj7//HN5h1Opan+q9h85OTmEh4ejrq7O999/L+9wKkVaWhoLFy5EXV0dqVRKYWGhvEN642RmZhIaGoqfnx9btmyRdziV4vjx45w4cYL4+HgKCgreiE9mQu1QUFCAVCpFXV2dhQsXkpaWJu+QKsUPP/yAuro64eHhb9zltzfu3SEkJASpVIq7uzs7d+6UdziVwsDAgFOnTqGhoQEg1o9XgfT0dAA+++yzN6bgbtCgQSxdurTkWq1IBoTqkpqaCoCmpianTp3CwMBAvgFVkh07duDm5oZUKiUoKEje4VSqN+7C8wcffICxsTHbt29HTU1N3uFUmo4dO3L37l0++OADOnXqJO9w3jjDhw/H2dmZr7/+Wt6hVBolJSWmTZuGtbU1O3furPUFXELt0alTJ9q1a8cff/xRa9p5V4S1tTVXrlxh5MiR9OvXT97hVKo3roBQEARBEISXI+YNBUEQBEHBVfgyQUREBImJiVUZS62Vl5dXcj1feCImJgYAKysrOUdS84hz83ympqbY2dnJO4waR7wPP5t4H362iryeKpQMRERE4Fbfleyc3EoJ7E2jrKyEVCqutvyXOC/PpqykhFRcoXsmbS0tfP38REJQSkREBG5ubmRnZ8s7lBpJWVkZqVQq7zBqJG1tbXx9fZ/7eqpQMpCYmEh2Ti6rB9fD2VSr0gJ8E5wJTGHJmUjWTx+BSx1zeYdTYwQ8imfi0p1snPMxrvY28g6nRvEPj2b83N/4bUIXnK2N5B1OjRMYncKkDV4kJiaKZKCUxMREsrOz2bpmKW7OTvIOp0Y5euYc/1u8gk3zp+HqaCvvcGoU/9BIxs1e9sLX00utJnA21aKhte5rB/cmCUzIAcCljjme9cST8L9c7W3wdHWQdxg1krO1EY3tTeUdhlDLuDk70aSRh7zDqFH8AoMBcHW0pYlbPTlHUzuJAkJBEARBUHAiGRAEQRAEBVdjmg6l5RSy8WoM07rUqZbxTgek4BObRWaehFnd7QHIL5Ty26VosgukNLTSoamtLocfJuMXl0UrB31a2umz524COuoqjGttiZpK9eRSqZk5/HbgAjPf61Et45244cv9kGgysvP434d9Sr7/15lbxCSmoaOlQZcmzhy+4kN4XDKTB3YkJCYJ78BHJKVnM29c9TXjSM3I4tfdx5k1blC1jHf8yh3uBUaQkZXD3EnvApBfUMjKHYfJys3D08WBAV1aEhYdz4ff/8K+n78iJCqOJb8fYNbYQTRytq+WOAHSsvNYd+I+Xw1oXi3jnbwXwYOIJDJy8pkztBUA+YUS1hy9S3Z+IY3sTWnqaM6Kw94Y6mjQxsUKZSUl7kckEp6QzqL32qOmKj6fyFNqWjqrNmxlzvTPq2W8o6fPcveBL+mZmSyYPQOA/Px8lv26iazsbJo08qBBfRcOHjtFWEQkX3w8Fr/AYG7euU98QiJzZnyOlUX11GqlZmTyy/aDzP54ZLWMd+zCTe4HhJCemc2PU8YAkF9QwIrf95KVk4dnfSf6dW7Fkk270dXWon+X1ly87UNKWgYJKWnMe/w7FfXSycDW67FIpDKczbQw1VFjz90EAFzMtfGJzWJub0eWeT1ifGsrzganEpmSh7meOjYG6hzySWJAQ1POBKZirquGqrISPesbs+ZCFFb66uXGuhaezrmgVKQyGNPSkh+Oh9HD1QjvyEzqmmjRwFqHf32S0FFX5i0XI/66HU9Le33aOOhjY6BBdFoehx8mlxyvraM+HpY6AFwISeN/vRzYdDWGiJRc7Iw0UVdVZkonW6LS8tjtnUA/DxMmtLHi28Oh9Hc3Yfm5SKz01ckrfHoV+MZ/L1MoleJaxxwzQ112nbkNQH07C+6HRrNo4jss3H6CSe904MxtfyLiUrAw1sPWzIj9F+4yuJMnp275Y26kh5qKMn1ae/Dzbi9sTMu38rz8IIQz3gHIpDLG92vL7I2H6N3Kg5v+EdSzMaWxkw37L95DR1ODni3c+PPkdVp7ONK+oRO2ZoZEJaZy4OL9kuN1aOREw7rWAJy9E8iCCW+z9uBFwmKTcbA0LrnP6r3ncNHVwtnWnC+GmrPb6zYxyemcvu3P/8b0Ye2Bi1x9GIaedtklPhv2nqJQIsHVwRpzIwN2Hr8EgJujDfcDI1g85X0WbNrLJ8N6cvr6fcJjErE0MaSOhQl7z1xjSPc2nLx6FwtjA9RUVejboRnL/jiEtVn5fcUv3fXnzPX7SKUyJg7uxszV2+nTrik3HwZTr44ljV0d2Od1HV0tTXq2acwfh8/TppELHZq4YWthQlR8MvvPPtkuumMTNxo+/iPuddOHRZ+9x29/HycsOh4Ha3PU1VSZMfodIuOS2H70Alk5uew/e4PurRsD0LR+Xfp1ePaOgZvP+FAokeFibYiZvhZ/XwkEwNXaiAcRScwf2ZYl+2/yUY+GnHkQyaPEDCwMtLE10eXAjRAGtnLi9P1HmOtro6aqTG9Pe1YcuYO1kU65sa4ExHD2QSRSmYxxXT34btcVenk6cCskHicLAxrZm3LwZgg6Gmp0b2THjot+tHK2pF19a2yMdYlOzuTgzdCS47Wvb00DOxMAzj+M4sfhbVh/8gHhCenYm+mjrqrCl/2bEpWcyV8XA2jpZEFGTj4ymYw6Jrq4WBvRpYEt3/11hXyJRCQDFbR263YKCyXUd3bC3MyEHf8cAMDd1Zm7D3xZ9uNs5i5dxecTxnDi7AXCH0VhaW6Gna01/xw8yrsD+nLszHkszc1QU1Olf8+3WLJ6HTZWluXGunj1BifPXUIqlTLpw/eY8b+F9OvxFjdu38XZyYEmDT3Yc+goOjo69OnWma07/6Fty2Z0atuKOjZWREbHsvffYyXH69SuFY093AA4c/4yP/0wizUbtxEa8QhHuzqoq6sz84tJPIqKYduuvQzu14sZkyeyY89BYmLj0dTQIDkllfyCAkyMDMvFu373YQoLJbjWrYO5sSE7D3sB4OZkxz3/UH6aMYH5a3fw6Xtvc+qyN+HRcViaGlPHyoy9Jy4wtFcnTly6hYWJIWqqqvTr3Iqlm//B2sKk3FiXbvtw+oo3UpmUj4b15Ztlm+jTuSU37gfgbG+DZ30n9p68iK62Jr06tGDbgZO08XSnY/OG2FqaERWXyL5Tl0qO17F5Ixq5FnVs9Lp2h8XTx/PrjoOERcXiYGOJupoaX41/l8jYBP48eBotTXXSMrPQ1tJEXU2VG/f9WP3tZKbM/5Xw6DjsrSu+q+JLv/IaWetQIJGRnivBKzCVUS0s6exctiJa8ng5WXaeBFNdNbwjMwDoUNeAeqZaBCVko6uhQkpOIdci0untbkw/j/In+l+fJOyMNDHTVSM8JRdTHTUGNjLDQEuVdxqaEJ2WRzcXIyZ3sOGEXwoaqsoMaWyGjcGrrzWNTc9n09UYPm5XtP47ObsAbXVltNRVyC2U0qmeIVYG6lwNzyj3u571bCkoKCQ9K5dTN/0Z16cN3Zq5/ufcFC19yczJx9RQl1v+EQB08nTGpY45/o/i0NPSIDkjmys+ofRv24AB7RuVG2v/xXvYWxhjZqRHaEwSpga6DO3cBENdLYZ0akJUYho9W7rz5bCuHL3mg7qaKsO7NsPWzPCVz42NqSGLJr5DWmZR0eSlByHEp2bQvqETE/q2ZcO/lwmOTnzqG7qnqwP5BYWkZ+Zw4uo9xg94i+6tyj6uJ+cmFzMjfW4+LOr93bmFBy52VviHRaOno0VyWiaX7/rzdqfmDOzSsvy58bqGvZUZ5sb6hETFY2akz7AebTHU02FI9zZExSXTu60n0z7oz5GL3mioqzKiV3tsn/Jir6iYxBR+/fs4n4/owwVvPwoKJdzwCeLsTZ8X/m5jezMKJBLSc/I5ff8RYzq707VB2Rmy4tdUVm4Bpnpa3AqJB6Cjuw3OloYERqeip6VGcmYuVwJj6dfUkbeb1y031sEbIdiZ6WGmr01oQjqmeloMbl0PQ211BrVyIio5kx6N7ZjS15Njd8JQV1VhWFsXbIxfvXA4NiWL9Scf8EmvRkQmZ/JBRze+H9qaQ7eKkoqtXg/p7GGLjsab0z68qjVt1ID8ggLSMjI4fuY8E0ePoEeXDmXuI5EUvZ6ysrIxNzXhhvddALp2aINrPSf8AoPR19MlKTmVi9duMqBPDwb371VurD3/HsPRzhYLM1NCwiIwNzVhxKD+GBrq8+6AfjyKjqFP9y58/dlHHDp+Gg0Ndd4fOoA6Nq/eQyM6No41G3/ny0njALhw5QbxCYl0bNsSv8BgFs/5mnd6d+eG971yv9vErR75hYWkZ2Rz4tItJgztQ/e2ZZPxknOTnYOZsSE3H/gD0KWVJy4OtviHPkJPV5vktAwueT/k7bfaMKh7+3Jj7Tt5EXsbC8yNDQmJjMXM2IB3e3fGSF+Xob06EhmXQO+OLZg+diiHz11DQ02Nkf26Ymtp9srnJiY+iV92HGTKqIEUFBbiVteOkf26sHnPcUYP6MEvOw4Sl5SC2ktuc//SyUBqTiGaqsoEJmTT2dmQ32/EcjawaOMcN3Ntfr8ey/2YLACCEnNQU1Gi8PEbmbKSEiY6ariaa5ORK8HVXItWdvqcD0rlVED5zXf6eZgQlZaHirISdkYalG6trqykRGt7fU4FpLDmQhQ96hvx39br1gYaTGhjVfJVPCsARYnJmgtRxGfkY2ekybYbseQUSBi9ww9THTWuhBVtXLPbO4FhnkXTUMM8zdnlncCtiAw8LLTLxZuSmY2mhhp+EXG81cyVjYcvc+pW0ZPM3d6KTYcvczcoCoDAyHjUVVUofPykVFZWwtRAFzd7S9Kzc6lvZ0EbD0e8vAM4dsO33FgD2jciMiEVVWVlHCxNUFZ+8uCVlJRo6+HI8esP+Xn3GXq38ijzcyj6w/7JgA4lX8WzAgCdPZ1ZvvsMccnpOFgas/nIFQAW7zjJT3+dQkNdjbvBUXy/+TDKSsr4hMWQXygBmQx7C2NauTmUPzfpWWhpqOMXFkX3Vg3ZsO8UJ68VvZDdnWzZuO8Ud/yL/jgEhMegrlbq3CgpYWakj3tdW9Izc6jvaEvbxq6cvn6fY5e9y5+bLq2IjEtCVUUFR2szlJWePM2VlZRo5+nK0ct3WPbHIfq0b1Lm5wA25sZ8OqxXyVfDUlP7XZp7sOyPQ8QmpeJgbc6m/afJyctn6FfLMDPS58JtX3q19WTGqLdp4VGPzs09CI6M48z1B+w6cZn0rJzy5yYrF001VQKiU+jaoA5bvB5y5sEjANxsjdni9ZC74UWNZoJiU1FXVS5JnJSVlDDV16K+jRHpOfnUtzaijbMlXj6RnLgbUW6st1vUJSopE1UVJRzM9FEu9aJRVlaijasVJ+5GsPLwHXp5OpT5OYC1sS4f92hY8lU8KwBFicnKw3eIS8vG3kyfrV4PyckvZOTKY5jqa3LJLxoDbQ323wjm1+P3aO1iyVavh5x7GEVgTCqpWW/WLnBVKTklFS1NDXwDgujRpQPrtu7ghNcFABq4ubDu9x1433sAgH9QCGpqahQWSoCi9fhmpsZ41HchLT0Dd9d6tG/VnJNnL3Lk5NlyYw3u14uIqGhUVVVwtK9T9vWkrEyH1i04ctKLxavX0b/nW+VeT7bWlnw+cUzJV/GsAEDXjm1ZsnodMXHxONrVYf3vO8nJyWXABx9hZmrCucvX8L7/kJnzlqCsrMx9X3/MTE1YsmY9Zy9excWpfMKbnJaBloYGfiERdG/bjPW7D3Py8q2ic1PPng27j+DtW/RBwz8sEnU1VQolxedGCTNjA9yd7EnPzMbNyY52Tdw5fcWbo+evlxtrYPf2RMYmFJ0bW0uUlEufGyXaNW3A0fM3WLr5b/p2alXm5wA2FqZMfu+dkq/iWQEoSkyWbv6b2MQUHGws2fj3UXJy8xg8ZS5mxoacv3mfLq088Q2JYN1fh3mrTRMKCyUUFkro2Lwh1uYv9+GmQnsT3L59m2bNmnHso4ZPXVr4ICYLn9gs3m2ieOvs995N4LO9QZxdOeWpSwvvBUdxPySa97q3kEN08nMnKJLOU1ZyYdOPz1xaeC8wnHuB4bzfp2P1Bidnd/zD6DDuO059P+iZSwvvRyTyICKJEe1dn/rzN9nd8ES6/bCXW7du0bRpU3mHU2MUvw9fO77vqUsL7zx4yD0fP0a9Wz31MzXJzj0HGT15Ohd3LH/q0sK7/iHc8w/hg7e7ySE6+fL2DaL9yKkvfD1VSgFhAysdGliVvz75svILpey7/6TVZh1DDdJzJfRyK39d+GW8+/tDurkY0dfdmLDkXA48SKKeqRYT2lix7UYsGbkSghJzWNy/LuqVfM2ykZMNjZxev+lOfkEhf5998im4OLm4cC+YtKwc+rVp8MrHzszJY+b6gzhYGmNmqEeXJs5s+PcShRIpXw7riqlB1fSWaORsXykFdfkFhew+ebnk//aWZqRmZtG/4+sVzvWfsohe7TwZ0LklMYkpnL/tS0hUHHMnvctd/zCW/H6AnQu/wFDv9Z/7/9XQzpSGdq/fgyC/UMKeq0+2WrUz1SMtO58+TR1e+ZgJ6Tlsv+BHQloO/ZvXRUNNmT1Xg5DKYGR7V2JSs8oVEgpVz7OBO54N3F/7OPn5+fy179+S/9vXsSE1LZ13end/5WNmZmUxbc58HO3qYGFmSl0HO3bvP4xrvbp8PnEMt+8+4NyVa4SEPWL5vG9Rfckp7hdp7FqXxq7lZxFeVn5BAbuOniv5v721BWkZmfTv0ua1jpuSnsnob5bw45Qx1LE045ftB9DT1eb9/m9hamTAul2HCYqI5qcZE173ITxTla4mKF1s6GisyamAFAITcviupz1f7g+mtb0+96IzaWStS2BCDvP6OrL9VhyjW1iSnF1Ycpx/fZIITcolI6+QqZ1smXsinPrm2nRzNSqpD/jXJ4mY9HwANFWV+aDFk8IJc101MvMkqCgroaGqjL6GCgUSKRKpDE01ZcKSc9FQVa70RKAiShcd1rU25fh1XwIexfHjuP58umIX7RrUxTswkibOtvhFxPPTpAH0mLYGVRVlktKzS6b3D1y8R3B0IulZuXw1ohvfbfoXdwdLerZ0L6kTOHDxHlGJaQBoaajyYe+iJ7CqijKZOXkkpGXS0s2Bg5fuM7F/e1Izszl23Zf3q3lWo3SxoZOtJccu38E/LIr5k0cyacF62nvW57ZfKE3rO+IXFs3SqaPYctCLCQO7kZT2pJZjv9d1giJjSc/M4ZsPBzB7zU48nGzp1bZJSX3Afq/rRCUUFZlqqaszdkDXkt+3MDEgMzsXFWVlmrs70dzdiWV/HCItI5suLRpw5V5AtZ4XKFtsWNfCgBN3wwmITuWHd1vz+eaztHW15k5oAp6OZvhHp7DovXb8cc6PD7u6k5TxpJ34wZshhMSlkZ6dz/S3m/G/3VdxszWmR2O7kvqAgzdDiE4uuuSnpa7C6M5Ff2jM9LX4om8TrgXGEhqfToM6JqRl56OkBBaG2uy6HFCukFCQn9LFhvXq2nPkpNfj6+7fMH7qN3Rs05Jbdx/QrHEDfAOCWDF/Dhv/3MVHo0eSlJyCfZ2iDzN7/j1GUEgYaRkZfDt1Ml/PXUwDNxf6dOtSUh+w599jREXHAqClqcmEUcMBUFVRJSMzi4TEZNq0aIqmhgb6+nrkFxQgkUho2rgB173vkpyairJy9b0Ply42rGdnzdHzN/APfcSCqWP5+PuVtG/WgNsPg2jqXg+/kEcs++Yjtuw5zoRhfUhKTS85zr6TlwiKiCY9M4uZE4cza/lmPOo50Ltji5L6gH0nLxEVX/RhV0tDnXFDegMgkUjY+PdRBnVrB8Dfx86jrqaGTAZqqqocOH2Ztk3cCYqIrtJzUaVnvXSxYU6BFCUlJaQyCEvOxVhbldEti5bnFd0WXZt0NNZicGMzghKfXFs9FZCCuZ4a+pqqRKXn08hKh9Scwgr3vV892JkxLS3YdiOWZnX0mN3DHgdjTa6EpROXXsCcng44m2kRnVb91yxLFx3m5BWU9KwPiUnERF+HcX3boq6mUnILUNfGlHe7NiMwMr7kOMeuP8TCSA8DHU2iElNpXM+WlIyckmvLz5OQmknvVu4snPA2p276VdljrajSxYY5uflPzklkHCYGeowf2A11NVXGD+xWUiTjZGvJ8J7tCAiPKTnO0cveWJoYYqCrTWR8Mp71HUhJz6rQOQHYOGcSEwZ1Y+P+0wAcOHsDazMjHG3kdzmsdLFhTl4hykpKyGQyQuPTMdbV5MMu7qirKhfdPl766mihz9A2zgTFppYc58TdCCwMtDHQ1iA6OZNG9qakZOaWFCq+iH9UCmfuP2JYG2dC4tP4ol8TJnZryFmfyKp42MJrKF1smJ2d+7iHv4ygsHBMjY34aPRI1NXUSm4B6jna896Qd/APCik5zpGTXlhZmGOor8+j6BiaNvIgOSUNiVTywhjiE5Po1+Mtls6dxbEz52nVzJOF387AycGO81eKrsV/POY93urQluSU1Co5D09TutgwOyevZD+V4EcxmBjpM2FYH9TVVJkwrA9qao/fa+ysGdG3CwGhT57rRy9cx9LMCAM9HSLjEmniVo+U9IwKvdfc9QshJzePszfu4XXtDgWFhTRv6EL7ph7sP3WJK3d8OXfjHvf8gsskIJWtSmcGShcbygAtNWWkMhkSKag8LmhTVy26La5TCk3OYeu1WByMnqwI6OFqRHBiLnoaKljqqZOVL6VAIiMiNY86RpoAT12NABCXkc8/dxKIy8ynv4cpNx9lcC0snfCUXL7pZodPbBa/XowiJj0fE53qr2YuXXQok8nQ1FBDKpUhkUhRefxmrv74SVhcyhUSlciGfy/haPXkMfdu5UFQVAJ62hpYGuuTlZtHfmEh4bHJ2FsUXWZ55ymrEgC0NdQ5dzeIqIRUPJ1tae5qx4ZDlyiUSpk6tEvVPfhnKF1sWHRO1IvOiVSKqkpRQqTx+E2r+HkTHBnL+j0ny/yh7tO+KYERMehra2FlakhW9uNzEpOAvVVRtj7gKasRAGITU9lx7CJxSakM7NqSY5fvsHHfaXq18yQiNpHktExu+ASxcb8q0z94uwrPRlmliw1lMtBUU338mpKi+vgTlbpq0TlSenxyQuPS2XTaB4dSn9B7edoTFJuKnqY6lobaZOUVUCCREpGQgZ2pHsBTVyMAxKRkMWnDGYa1deZGcBwmepps9XqIirIS73Woj6GOBisP3yE9J1/MCtQApYsNZTIZWpqaSKVSJBIJqipF7y0a6kVLu4ufM0Gh4fy25U/qOjzpZd+vR1cCgkPR19PF2tKczKxs8gvyCYuIxKFOUb3U4H7lVyMAaGtp4nXxCpGPk4irN725cPUGoeGP+HHml+w/coLgsAiCQsIYMaj6Xk+liw1lyNDU0EAqkyKRSlF5/F6jrv6f95qIaNbtOoxjnSerJfp2akVAWBT6utpYmRmTmZNLfkEhYVFPlvcN7N7uqTE09XCmqYczfxw8RSPXupga6rNme9FS0Q8H9WT0wKL+MuHR8ZgYVt3rqVIKCCvTnKOhzO3t+OI71hAvKiCsCt+sP8Ciie9Uy1ivqiIFhJXp65V/snjK+1U+TmWoSAFhZZq94zLzR7at8nEqiyggfLoXFRBWpmnfzWfZj7OrdIzK9KICwso046cNVXrtvrJVtICwxnX4qE2JgLzU9ERAHmpLIiAPtSkREGqG2pQIVLfalAi8DLm3I97lHY+H5euvRrgXncnl0KLp/x97O6L6uAYhr1DK9APB9HU3oZ6ZFsd9k4lIzeOjtlbciMggJCkXR2NNhjetWcsit5+8QcO61q+9EuFOYCQX7gcTFpPE4o/fKZlmX7j9BFrqajhYGtO3TQOW7jqNrpYG/dp44OUdSHpWLgGR8ayYPLjkMkVN8eeR85WyEmHfmWsER8WRlJrJws+KWoxKpVJm/bITU0M96tla0rdDU376/SC62pr069iM5LRMvG48wEBXm4mDX726uqrsvOhPAzuT116JcDcsgUt+MYQlpLNgZFtUVZTLtRZ2tzHmiHcYEYkZTOrZiMDoFLxDE0hIz+GrAc2xNCzfi0Oombbt2ksjj/qvvRrhn0NHCQ4NJyk5hSX/m1ny/VXrt5Kdk0NQaDgzJk+sEe2FK6p4+v51VyPsPXmR4IgYklLTWTRtXMn3P5u3Bic7ayQSKcP7dGblH/tQUVZm7OBeqCgrs/OwFzramnw64u2SuoWqUC0zA/NOhFMgkbLhSgzRaXnsuBXHz16P8Ap80mhoztHQMrcLT0Ww6WoMv1+PLblPdFoeG67ElHz5xGaV/KyRtS6aasqk5hRSur/Olmux9G9QdG29nqkWn3awoZW9HnEZ+ehpqKCpqkxuQcUKyirbnM3/UlAo4df9F4hKTGXb8Wss2nGiTBHfN+sPlLmd+/tR1h68yKbDT5bSRSWm8uv+CyVf90OeVJ16Otuipa5GSmZ2SQOZ1IxsVJSV+GJoF675hnPmdgDpWbnIZDLUVFXRVFcjMS0TTXVVuSUC3/66k4LCQn7ZfYyo+GS2HjrLws17OXn1Scexr1f+Web2f+t289vfx9m471TJfaLik/ll97GSr/uB4SU/G9i1FdM/eJvM7CfFqsnpWWipqzN1ZD/+PHqB09cfkJ6VjVQmQ11Vle1HLmCgK98/cj/svkpBoZS1J+4TnZzJH+f9+OnALU7ff1Ryn9k7Lpe5nbfnOutPPmCL18OS+0QnZ7L2xP2SrwcRSSU/a+xghqa6CilZuSXPm+LWwh92cScwOpV6VoZ83seT1s6WxKZmoaGmSkpWHvkSKca6r94FVKga3/y4hIKCAlat30pkdCybt+/mx2WrOX7mfMl9pn03v8zttwuWsWbjNtb9vqPkPpHRsaxav7Xk667Pk6ZoQ/r35uvPPyYj88l7M8DnE8dgYWbK+PffxbVeXWZMnki7Vs0r1F64OsxesYWCgkLWbD9AVFwiW/ceZ8G6nZy4dKvkPjN+2lDm9vvV2/h1x0E27D5Scp+ouETWbD9Q8nXP/0nr7kHd2zNj3NAy7zcAuXkFpKZnYmNhyvV7/vRs35xB3duz++g5tuw9jomhPkr8p6NeFaiWZKBnfWOO+aUQm56HhZ46eYVSbAw1uBxavjJSKoWkrIIyLYsrakxLSzrUNSD18e9EpeURn5nPuaBULoSkAnA1LJ2EzALaOBjQx92EqZ1tyZNICU/Ofc6Rq0bf1g04fOUBMUlpWBrpk5tfSB0zIy7cCy53X4lERmJaZpl2xRU1vl9bOns6k5Lx5EmoVKqzXIFEQn07C4Z3bcbWY1eJTU5n3vj+uNSxICox9bUe46vq16EZ/56/RXRCCpYmhuTlF1DH0pTztx+Wu69EKiUhJb1Mu+KKkMlk/PT7Aca+82Q5oamhHvbWZqzfexJzI30KCgtxdbBhZK/2bDnoRXxKGh/060RmTh4hUXGV9nhfRu+mDhzxDiM2NQsLQ23yCgqxNdHlkl/5pUcSmYzE9Jwy7YoramxXDzq625JSqjNg6dbCAJf9Y0hIz6GdqzUBMSn8b1hr+jZ14HZIwus/UKFSvdOrGweOnSI6Ng4rCzNy8/Kwt7Xh7OVr5e4rkUpISEwu07K4ImQyGQtX/MaED4aX+9nVW960bt4EeLn2wtWhf+fWHDp7lej4JCxNjcjNL6COlRnnb94vd1+pREpCclqZlsUVIZPJWLJxF+MGPymyzMnNo3H9uvxv8iju+YfQs30z7geEcvG2D2qqKuTmF9CtTVNsLEy5ePvFrc1fR7V87Gthp8d7fzzkw1ZW5BRIiU7Lx9lcC2mp2kVzXXV234knKDGnXMviYsXthZ/mqG8SYUm5hCbnMrCRKdtuxDKqhSVzejpwOTSN9FwJD2KymHcynHcamOIbl0Vsej4PYrKJzch76kZJVa2VuwODv9vAxP7tyc7LJyohFVc7izLnxcJQjx2nbhIYGV+uXXGx4tbCT3Po8n1CY5IIjkpkaOembD5yhbF92iCRSln5jxet3Ozp7OnM/D+O8yghha5NXLgd+IiV/3gRlZhWZQ2HXqR1QxcGfrmEj4b0IDsvj8i4JOo72pR9zhgbsP3oBQLCo8u1Ky5W3Fr4ab5fu5vk9Ewu3/OnkbM9Ww95MW7AW0ilUrJz8xnZuwOeLg7M2/gPkXFJdG3ZkNYNXVi98wiJqRnYvmS7z8rSsp4lw34+wvi3GpCTV0h0chYu1kZlz42BNn9dCiAoJrVcu+Jixa2Fn+bwrVBC49MJiUtjSOt6bPV6yLvtXBi58hgDWzlxyS8acwNt5v59jQEtnXgYmYyZnharjhStIviyX5MqPw/Cy2nToin9Rozjk3Hvk52Tw6PoWNxcnJCVWv5mYW7Ktt178Q8KKdeyuFhxe+Gnmb1gKcmP9zpo3MCNTX/uZuLoEdy6c59mjYqea8XthYe907dMe+H09AxmfvFJlZ6DZ2nt6cY7n37PpOH9yM7NIzIukfqOdZCWPjcmhvx58DT+YZHlWhYXK24v/DRzVv3+eK8DHxq5OrJl7wlGD+jO/YBQVv2xHwcbCyRSKVKpjJzcPMYN6U1cUgp/HDxFVk4u30x4t0rPQY1bTVDbyGM1QW1Q3asJapPqXk1Q24jVBE9XnasJapvqXE1Q29Ta1QSCIAiCIFSvl7pMEJhYfsc1RfcotegabMCj+BfcU7EUnw//8Cg5R1Lz+IcXXdsPjC6/U6cgzsuL+AaWrylSdKGPiroB+oeKDpj/VdFzUqHLBBEREbjVdyU7p/qL7GqD4haWQlnivDxbcYtl4em0tbTw9fPDzs7uxXdWEBEREbi5uZGdXfHiYUVS1GZZPivDajptbW18fX2f+3qqUDIARU/ExMTEF99RAeXl5aGhIZZS/VdMTNE+AVZWTy/6VGTi3DyfqampSASeQrwPP5t4H362iryeKpwMCIIgCILwZhIFhIIgCIKg4EQyIAiCIAgKTiQDgiAIgqDgRDIgCIIgCApOJAOCIAiCoOBEMiAIgiAICk4kA4IgCIKg4EQyIAiCIAgKTiQDgiAIgqDgRDIgCIIgCApOJAOCIAiCoOBEMiAIgiAICk4kA4IgCIKg4EQyIAiCIAgKTiQDgiAIgqDgRDIgCIIgCApOJAOCIAiCoOBEMiAIgiAICk4kA4IgCIKg4EQyIAiCIAgKTiQDgiAIgqDgRDIgCIIgCApOJAOCIAiCoOBEMiAIgiAICk4kA4IgCIKg4EQyIAiCIAgKTiQDgiAIgqDgRDIgCIIgCApOJAOCIAiCoOBEMiAIgiAICk4kA4IgCIKg4EQyIAiCIAgKTiQDgiAIgqDg/g+2wRTAbPCF4gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_prunnedtree = DecisionTreeRegressor(ccp_alpha=0.00188444410871555)\n",
    "fit_prunnedtree.fit(x_basic_train, y_basic_train)\n",
    "# plt.figure(figsize=(30, 20))\n",
    "plot_tree(fit_prunnedtree, filled=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Leso28bjWit9",
    "papermill": {
     "duration": 0.050261,
     "end_time": "2021-07-22T21:33:47.921859",
     "exception": false,
     "start_time": "2021-07-22T21:33:47.871598",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Finally, we calculate the mean-squared error and the $R^2$ on the test sample to evaluate the out-of-sample performance of the pruned tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 of the pruned tree: 0.19978003635520836\n"
     ]
    }
   ],
   "source": [
    "y_hat_pt = fit_prunnedtree.predict(x_basic_test)\n",
    "residual_pt = (y_hat_pt - Y_test)**2\n",
    "MSE_pt = sm.OLS(residual_pt, np.ones(y_hat_pt.size)).fit().summary2().tables[1].round(3)\n",
    "R2_pt = 1 - MSE_pt.iloc[0, 0]/ np.var( Y_test )\n",
    "print(f\"R^2 of the pruned tree: {R2_pt}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FRT62uP-Wit-",
    "papermill": {
     "duration": 0.050968,
     "end_time": "2021-07-22T21:33:48.152253",
     "exception": false,
     "start_time": "2021-07-22T21:33:48.101285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Random Forest and Boosted Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0u9KdQZrWit-",
    "papermill": {
     "duration": 0.050209,
     "end_time": "2021-07-22T21:33:48.253970",
     "exception": false,
     "start_time": "2021-07-22T21:33:48.203761",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In the next step, we apply the more advanced tree-based methods: random forest and boosted trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python38\\lib\\site-packages\\sklearn\\ensemble\\_gb.py:286: FutureWarning: The loss 'ls' was deprecated in v1.0 and will be removed in version 1.2. Use 'squared_error' which is equivalent.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Applying the methods\n",
    "# random forest\n",
    "fit_rf = RandomForestRegressor(n_estimators=2000, min_samples_leaf=5).fit(x_basic_train, Y_train)\n",
    "\n",
    "# boosting\n",
    "fit_boost = GradientBoostingRegressor(loss='ls', learning_rate=0.01, n_estimators=1000, max_depth=2, subsample=0.5).fit(x_basic_train, Y_train)\n",
    "\n",
    "# Evaluating the methods\n",
    "yhat_rf = fit_rf.predict(x_basic_test)\n",
    "residual_rf = (yhat_rf - Y_test)**2\n",
    "yhat_boost = fit_boost.predict(x_basic_test)\n",
    "residual_bst = (yhat_boost - Y_test)**2\n",
    "\n",
    "\n",
    "MSE_rf = sm.OLS(residual_pt, np.ones(yhat_rf.size)).fit().summary2().tables[1].round(3)\n",
    "MSE_bst = sm.OLS(residual_bst, np.ones(yhat_boost.size)).fit().summary2().tables[1].round(3)\n",
    "\n",
    "\n",
    "R2_rf = 1 - MSE_rf.iloc[0, 0] / Y_test.var()\n",
    "R2_boost = 1 - MSE_bst.iloc[0, 0] / Y_test.var()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 of the random forest and boosted trees:0.20040, 0.28585\n"
     ]
    }
   ],
   "source": [
    "print(\"R^2 of the random forest and boosted trees:{:.5f}, {:.5f}\".format(R2_rf, R2_boost))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y3SItRnPWit_",
    "papermill": {
     "duration": 0.050943,
     "end_time": "2021-07-22T21:34:43.969202",
     "exception": false,
     "start_time": "2021-07-22T21:34:43.918259",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To conclude, let us have a look at our results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sj1p6gTaWit_",
    "papermill": {
     "duration": 0.051543,
     "end_time": "2021-07-22T21:34:44.072428",
     "exception": false,
     "start_time": "2021-07-22T21:34:44.020885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>MSE</th>\n",
       "      <th>S.E for MSE</th>\n",
       "      <th>R-squared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Least Squares (basic)</td>\n",
       "      <td>0.251136</td>\n",
       "      <td>0.016115</td>\n",
       "      <td>0.233558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Least Squares (flexible)</td>\n",
       "      <td>0.253460</td>\n",
       "      <td>0.016007</td>\n",
       "      <td>0.226466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.259000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.208943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Post-Lasso</td>\n",
       "      <td>0.257000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.215051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso (flexible)</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.208943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Post-Lasso (flexible)</td>\n",
       "      <td>0.257000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.215051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cross-Validated lasso</td>\n",
       "      <td>0.252000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.230323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cross-Validated ridge</td>\n",
       "      <td>0.251000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.233377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cross-Validated elnet</td>\n",
       "      <td>0.252000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.230323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cross-Validated lasso (flexible)</td>\n",
       "      <td>0.259000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.208943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cross-Validated ridge (flexible)</td>\n",
       "      <td>0.253000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.227269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cross-Validated elnet (flexible)</td>\n",
       "      <td>0.259000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.208943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.262000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.200401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Boosted Trees</td>\n",
       "      <td>0.234000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.285855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pruned Tree</td>\n",
       "      <td>0.262000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.199780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Models       MSE  S.E for MSE  R-squared\n",
       "0              Least Squares (basic)  0.251136     0.016115   0.233558\n",
       "1           Least Squares (flexible)  0.253460     0.016007   0.226466\n",
       "2                              Lasso  0.259000     0.016000   0.208943\n",
       "3                         Post-Lasso  0.257000     0.016000   0.215051\n",
       "4                   Lasso (flexible)  0.260000     0.016000   0.208943\n",
       "5              Post-Lasso (flexible)  0.257000     0.016000   0.215051\n",
       "6              Cross-Validated lasso  0.252000     0.016000   0.230323\n",
       "7              Cross-Validated ridge  0.251000     0.016000   0.233377\n",
       "8              Cross-Validated elnet  0.252000     0.016000   0.230323\n",
       "9   Cross-Validated lasso (flexible)  0.259000     0.016000   0.208943\n",
       "10  Cross-Validated ridge (flexible)  0.253000     0.016000   0.227269\n",
       "11  Cross-Validated elnet (flexible)  0.259000     0.016000   0.208943\n",
       "12                     Random Forest  0.262000     0.016000   0.200401\n",
       "13                     Boosted Trees  0.234000     0.015000   0.285855\n",
       "14                       Pruned Tree  0.262000     0.016000   0.199780"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.DataFrame(columns=[\"MSE\", \"S.E for MSE\", \"R-squared\"]) \n",
    "table.loc[0]  = [MSE_lm_basic[0], MSE_lm_basic[1], R2_lm_basic]\n",
    "table.loc[1]  = [MSE_lm_flex[0], MSE_lm_flex[1], R2_lm_flex]\n",
    "table.loc[2]  = [MSE_lasso.iloc[0, 0], MSE_lasso.iloc[0, 1], R2_lasso]\n",
    "table.loc[3]  = [MSE_lasso_post.iloc[0, 0], MSE_lasso_post.iloc[0, 1], R2_lasso_post]\n",
    "table.loc[4]  = [MSE_lasso_flex.iloc[0, 0], MSE_lasso_flex.iloc[0, 1], R2_lasso_flex]\n",
    "table.loc[5]  = [MSE_lasso_post_flex.iloc[0, 0], MSE_lasso_post_flex.iloc[0, 1], R2_lasso_post_flex]\n",
    "table.loc[6]  = [MSE_lasso_cv.iloc[0, 0], MSE_lasso_cv.iloc[0, 1], R2_lasso_cv]\n",
    "table.loc[7]  = [MSE_ridge.iloc[0, 0], MSE_ridge.iloc[0, 1], R2_ridge]\n",
    "table.loc[8]  = [MSE_elnet.iloc[0, 0], MSE_elnet.iloc[0, 1], R2_elnet]\n",
    "table.loc[9]  = [MSE_lasso_cv_flex.iloc[0, 0], MSE_lasso_cv_flex.iloc[0, 1], R2_lasso_cv_flex]\n",
    "table.loc[10] = [MSE_ridge_flex.iloc[0, 0], MSE_ridge_flex.iloc[0, 1], R2_ridge_flex]\n",
    "table.loc[11] = [MSE_elnet_flex.iloc[0, 0], MSE_elnet_flex.iloc[0, 1], R2_elnet_flex]\n",
    "table.loc[12] = [MSE_rf.iloc[0, 0], MSE_rf.iloc[0, 1], R2_rf]\n",
    "table.loc[13] = [MSE_bst.iloc[0, 0], MSE_bst.iloc[0, 1], R2_boost]\n",
    "table.loc[14] = [MSE_pt.iloc[0, 0], MSE_pt.iloc[0, 1], R2_pt]\n",
    "models_row = [\"Least Squares (basic)\",\"Least Squares (flexible)\", \"Lasso\", \"Post-Lasso\",\"Lasso (flexible)\",\"Post-Lasso (flexible)\", \n",
    "                    \"Cross-Validated lasso\", \"Cross-Validated ridge\",\"Cross-Validated elnet\",\"Cross-Validated lasso (flexible)\",\"Cross-Validated ridge (flexible)\",\"Cross-Validated elnet (flexible)\",  \n",
    "                    \"Random Forest\",\"Boosted Trees\", \"Pruned Tree\"]\n",
    "table.insert(0, \"Models\", models_row)\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TpqFypSbWit_",
    "papermill": {
     "duration": 0.052757,
     "end_time": "2021-07-22T21:34:44.392711",
     "exception": false,
     "start_time": "2021-07-22T21:34:44.339954",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Above, we have displayed the results for a single split of data into the training and testing part. The table shows the test MSE in column 1 as well as the standard error in column 2 and the test $R^2$ in column 3. \n",
    "\n",
    "We see that the prediction rule produced by the Elastic Net using the flexible model performs the best here, giving the lowest test MSE. Cross-Validated Lasso and Ridge, perform nearly as well. For any two of these methods, their testing MSEs are within one standard error of each other. Remarkably, OLS on a simple model performs extremely well, almost as well as best tree based method Random Forest. On the other hand, OLS on a flexible model with many regressors performs very poorly giving the highest test MSE. Notice that the nonlinear models, e.g. Random Forest, are not tuned. Thus, there is a lot of potential to improve the performance of the nonlinear methods we used in the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFol0C-XWiuA",
    "papermill": {
     "duration": 0.052773,
     "end_time": "2021-07-22T21:34:44.497668",
     "exception": false,
     "start_time": "2021-07-22T21:34:44.444895",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Ensemble learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P40FBHxRWiuA",
    "papermill": {
     "duration": 0.052345,
     "end_time": "2021-07-22T21:34:44.603135",
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     "start_time": "2021-07-22T21:34:44.550790",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In the final step, we can build a prediction model by combining the strengths of the models we considered so far. This ensemble method is of the form\n",
    "\n",
    "$$ f(x) = \\sum_{k=1}^K \\alpha_k f_k(x) $$\n",
    "    \n",
    "where the $f_k$'s denote our prediction rules from the table above and the $\\alpha_k$'s are the corresponding weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nYVckjqeWiuA",
    "papermill": {
     "duration": 0.052659,
     "end_time": "2021-07-22T21:34:44.710467",
     "exception": false,
     "start_time": "2021-07-22T21:34:44.657808",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We focus on the prediction rules based on OLS, Post-Lasso, Elastic Net, Pruned Tree, Random Forest, Boosted Trees, and Neural Network and combine these methods into an ensemble method. The appropriate weights can be determined by a simple ols regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "esemble_df = pd.DataFrame()\n",
    "esemble_df[\"y_test\"] = Y_test\n",
    "esemble_df[\"yhat_lm_basic\"] = yhat_lm_basic\n",
    "esemble_df[\"yhat_rlasso_post_flex\"] = yhat_rlasso_post_flex.flatten()\n",
    "esemble_df[\"yhat_elnet_flex\"] = yhat_elnet_flex\n",
    "esemble_df[\"y_hat_pt\"] = y_hat_pt\n",
    "esemble_df[\"yhat_rf\"] = yhat_rf\n",
    "esemble_df[\"yhat_boost\"] = yhat_boost\n",
    "esemble_df\n",
    "cvt = [\n",
    "  \"yhat_lm_basic\", \"yhat_rlasso_post_flex\", \"yhat_elnet_flex\", \"y_hat_pt\", \"yhat_rf\", \"yhat_boost\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>     <td>0.311</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>      <td>y_test</td>             <td>AIC:</td>         <td>1746.1107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2023-02-03 13:23</td>        <td>BIC:</td>         <td>1782.2366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>        <td>1288</td>         <td>Log-Likelihood:</td>    <td>-866.06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>6</td>           <td>F-statistic:</td>       <td>97.62</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>          <td>1281</td>       <td>Prob (F-statistic):</td> <td>3.74e-101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.314</td>            <td>Scale:</td>         <td>0.22591</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>             <th>Coef.</th>  <th>Std.Err.</th>    <th>t</th>     <th>P>|t|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>             <td>-0.0642</td>  <td>0.1671</td>  <td>-0.3840</td> <td>0.7010</td> <td>-0.3920</td> <td>0.2636</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yhat_lm_basic</th>         <td>0.2686</td>   <td>0.2212</td>  <td>1.2145</td>  <td>0.2248</td> <td>-0.1653</td> <td>0.7025</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yhat_rlasso_post_flex</th> <td>-0.4541</td>  <td>0.2093</td>  <td>-2.1690</td> <td>0.0303</td> <td>-0.8648</td> <td>-0.0434</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yhat_elnet_flex</th>       <td>0.0385</td>   <td>0.1552</td>  <td>0.2482</td>  <td>0.8040</td> <td>-0.2659</td> <td>0.3429</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>y_hat_pt</th>              <td>-0.3557</td>  <td>0.1130</td>  <td>-3.1481</td> <td>0.0017</td> <td>-0.5774</td> <td>-0.1340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yhat_rf</th>               <td>0.4789</td>   <td>0.0918</td>  <td>5.2158</td>  <td>0.0000</td> <td>0.2988</td>  <td>0.6591</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yhat_boost</th>            <td>1.0432</td>   <td>0.1897</td>  <td>5.4979</td>  <td>0.0000</td> <td>0.6710</td>  <td>1.4155</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>129.406</td>  <td>Durbin-Watson:</td>    <td>1.907</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td>  <td>0.000</td>  <td>Jarque-Bera (JB):</td> <td>702.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>      <td>0.283</td>      <td>Prob(JB):</td>      <td>0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>    <td>6.575</td>   <td>Condition No.:</td>     <td>159</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                   Results: Ordinary least squares\n",
       "=====================================================================\n",
       "Model:               OLS               Adj. R-squared:      0.311    \n",
       "Dependent Variable:  y_test            AIC:                 1746.1107\n",
       "Date:                2023-02-03 13:23  BIC:                 1782.2366\n",
       "No. Observations:    1288              Log-Likelihood:      -866.06  \n",
       "Df Model:            6                 F-statistic:         97.62    \n",
       "Df Residuals:        1281              Prob (F-statistic):  3.74e-101\n",
       "R-squared:           0.314             Scale:               0.22591  \n",
       "---------------------------------------------------------------------\n",
       "                       Coef.  Std.Err.    t    P>|t|   [0.025  0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept             -0.0642   0.1671 -0.3840 0.7010 -0.3920  0.2636\n",
       "yhat_lm_basic          0.2686   0.2212  1.2145 0.2248 -0.1653  0.7025\n",
       "yhat_rlasso_post_flex -0.4541   0.2093 -2.1690 0.0303 -0.8648 -0.0434\n",
       "yhat_elnet_flex        0.0385   0.1552  0.2482 0.8040 -0.2659  0.3429\n",
       "y_hat_pt              -0.3557   0.1130 -3.1481 0.0017 -0.5774 -0.1340\n",
       "yhat_rf                0.4789   0.0918  5.2158 0.0000  0.2988  0.6591\n",
       "yhat_boost             1.0432   0.1897  5.4979 0.0000  0.6710  1.4155\n",
       "---------------------------------------------------------------------\n",
       "Omnibus:              129.406        Durbin-Watson:           1.907  \n",
       "Prob(Omnibus):        0.000          Jarque-Bera (JB):        702.846\n",
       "Skew:                 0.283          Prob(JB):                0.000  \n",
       "Kurtosis:             6.575          Condition No.:           159    \n",
       "=====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_esemble = smf.ols(\"y_test ~ 1 +\" + \" + \".join(cvt), data = esemble_df).fit().summary2()\n",
    "ols_esemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Alternatively, we can determine the weights via lasso regression. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 712
    },
    "execution": {
     "iopub.execute_input": "2021-07-22T21:34:45.060856Z",
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     "shell.execute_reply": "2021-07-22T21:34:45.172952Z"
    },
    "executionInfo": {
     "elapsed": 37,
     "status": "ok",
     "timestamp": 1658250154363,
     "user": {
      "displayName": "Jhon Kevin Flores Rojas",
      "userId": "10267608749788811245"
     },
     "user_tz": 300
    },
    "id": "hlm-aAX4WiuB",
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     "start_time": "2021-07-22T21:34:45.002196",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Python38\\lib\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    }
   ],
   "source": [
    "esemble_df['ones'] = np.ones(len(esemble_df))\n",
    "cvt += [\"ones\"]\n",
    "\n",
    "rlasso_essemble = hdm.rlasso(esemble_df[cvt].to_numpy(), esemble_df[\"y_test\"].to_numpy().reshape(esemble_df[\"y_test\"].size, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "beta_rlasso = rlasso_essemble.est[\"beta\"]\n",
    "beta_rlasso"
   ]
  },
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   "source": [
    "The estimated weights are shown in the following table."
   ]
  },
  {
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>Weight OLS</th>\n",
       "      <th>Weight Lasso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Constant</td>\n",
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       "      <th>1</th>\n",
       "      <td>Least Squares (basic)</td>\n",
       "      <td>0.268603</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cross-Validated elnet (flexible)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pruned Tree</td>\n",
       "      <td>-0.355718</td>\n",
       "      <td>0.480044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.478922</td>\n",
       "      <td>0.603145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Boosted Trees</td>\n",
       "      <td>1.043210</td>\n",
       "      <td>0.000000</td>\n",
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      ],
      "text/plain": [
       "                             Models  Weight OLS  Weight Lasso\n",
       "0                          Constant   -0.064173      0.000000\n",
       "1             Least Squares (basic)    0.268603      0.000000\n",
       "2             Post-Lasso (flexible)   -0.454071      0.000000\n",
       "3  Cross-Validated elnet (flexible)    0.038513      0.000000\n",
       "4                       Pruned Tree   -0.355718      0.480044\n",
       "5                     Random Forest    0.478922      0.603145\n",
       "6                     Boosted Trees    1.043210      0.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1 = pd.DataFrame()\n",
    "nam_row = [\"Constant\",\"Least Squares (basic)\",\"Post-Lasso (flexible)\", \"Cross-Validated elnet (flexible)\", \"Pruned Tree\",\n",
    "                    \"Random Forest\",\"Boosted Trees\"]\n",
    "table1.insert(0, \"Models\", nam_row)\n",
    "table1[\"Weight OLS\"] = ols_esemble.tables[1].iloc[:, 0].to_numpy()\n",
    "table1[\"Weight Lasso\"] = beta_rlasso.to_numpy()\n",
    "table1"
   ]
  },
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   "source": [
    "Further, the $R^2$ for the test sample improves from $30\\%$ obtained by OLS to about $31\\%$ obtained by the ensemble method. We see that it is very powerful to aggregate prediction rules into an ensemble rule. Nevertheless, it is worth noticing that we should compare the ensemble method and the single rules on an additional validation set to ensure a fair comparison."
   ]
  }
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