{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning and Statistics for Physicists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Material for a [UC Irvine](https://uci.edu/) course offered by the [Department of Physics and Astronomy](https://www.physics.uci.edu/).\n",
    "\n",
    "Content is maintained on [github](github.com/dkirkby/MachineLearningStatistics) and distributed under a [BSD3 license](https://opensource.org/licenses/BSD-3-Clause).\n",
    "\n",
    "##### ► [View table of contents](Contents.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mls import locate_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection, neighbors, tree, ensemble, preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Study: Redshift Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is to predict the [cosmological redshift](https://en.wikipedia.org/wiki/Redshift) of a galaxy based on its brightness measured through 17 different filters.  Redshift is a proxy for distance or, equivalently, look back time, so is a key observable for learning about past conditions in the universe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Explore Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the data to train and test on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_hdf(locate_data('photoz_data.hf5'))\n",
    "y = pd.read_hdf(locate_data('photoz_targets.hf5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CTIO_U</th>\n",
       "      <th>CTIO_U_ERR</th>\n",
       "      <th>VIMOS_U</th>\n",
       "      <th>VIMOS_U_ERR</th>\n",
       "      <th>ACS_F435W</th>\n",
       "      <th>ACS_F435W_ERR</th>\n",
       "      <th>ACS_F606W</th>\n",
       "      <th>ACS_F606W_ERR</th>\n",
       "      <th>ACS_F775W</th>\n",
       "      <th>ACS_F775W_ERR</th>\n",
       "      <th>...</th>\n",
       "      <th>HAWKI_KS</th>\n",
       "      <th>HAWKI_KS_ERR</th>\n",
       "      <th>IRAC_CH1</th>\n",
       "      <th>IRAC_CH1_ERR</th>\n",
       "      <th>IRAC_CH2</th>\n",
       "      <th>IRAC_CH2_ERR</th>\n",
       "      <th>IRAC_CH3</th>\n",
       "      <th>IRAC_CH3_ERR</th>\n",
       "      <th>IRAC_CH4</th>\n",
       "      <th>IRAC_CH4_ERR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "      <td>17366.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.115481</td>\n",
       "      <td>0.016565</td>\n",
       "      <td>0.122138</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>-3.831100</td>\n",
       "      <td>-3.976799</td>\n",
       "      <td>-0.156267</td>\n",
       "      <td>-0.435271</td>\n",
       "      <td>-0.914184</td>\n",
       "      <td>-1.350993</td>\n",
       "      <td>...</td>\n",
       "      <td>-38.182465</td>\n",
       "      <td>-38.821423</td>\n",
       "      <td>1.621454</td>\n",
       "      <td>0.101949</td>\n",
       "      <td>1.469234</td>\n",
       "      <td>0.087894</td>\n",
       "      <td>1.220399</td>\n",
       "      <td>176.367126</td>\n",
       "      <td>1.041534</td>\n",
       "      <td>13.763525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.337539</td>\n",
       "      <td>0.002894</td>\n",
       "      <td>0.334996</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>19.517122</td>\n",
       "      <td>19.474924</td>\n",
       "      <td>6.818897</td>\n",
       "      <td>6.620780</td>\n",
       "      <td>11.951647</td>\n",
       "      <td>11.560015</td>\n",
       "      <td>...</td>\n",
       "      <td>49.144028</td>\n",
       "      <td>48.354370</td>\n",
       "      <td>5.608161</td>\n",
       "      <td>0.036535</td>\n",
       "      <td>4.562782</td>\n",
       "      <td>0.030426</td>\n",
       "      <td>7.416217</td>\n",
       "      <td>5705.130859</td>\n",
       "      <td>5.302016</td>\n",
       "      <td>880.659851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.528515</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>-0.069178</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-15.660400</td>\n",
       "      <td>0.048871</td>\n",
       "      <td>-11.669100</td>\n",
       "      <td>0.033175</td>\n",
       "      <td>-803.679016</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-51.631199</td>\n",
       "      <td>0.233581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.009948</td>\n",
       "      <td>0.015359</td>\n",
       "      <td>0.009744</td>\n",
       "      <td>0.004570</td>\n",
       "      <td>0.025961</td>\n",
       "      <td>0.007868</td>\n",
       "      <td>0.060697</td>\n",
       "      <td>0.005580</td>\n",
       "      <td>0.076885</td>\n",
       "      <td>0.009418</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>0.186954</td>\n",
       "      <td>0.075382</td>\n",
       "      <td>0.168157</td>\n",
       "      <td>0.070743</td>\n",
       "      <td>0.027263</td>\n",
       "      <td>0.317497</td>\n",
       "      <td>-0.037307</td>\n",
       "      <td>0.337097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.052541</td>\n",
       "      <td>0.015987</td>\n",
       "      <td>0.057218</td>\n",
       "      <td>0.005313</td>\n",
       "      <td>0.074789</td>\n",
       "      <td>0.012437</td>\n",
       "      <td>0.125512</td>\n",
       "      <td>0.008344</td>\n",
       "      <td>0.166042</td>\n",
       "      <td>0.015086</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094439</td>\n",
       "      <td>0.017297</td>\n",
       "      <td>0.443533</td>\n",
       "      <td>0.099319</td>\n",
       "      <td>0.390066</td>\n",
       "      <td>0.082867</td>\n",
       "      <td>0.384105</td>\n",
       "      <td>0.354340</td>\n",
       "      <td>0.316139</td>\n",
       "      <td>0.375890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.132249</td>\n",
       "      <td>0.017254</td>\n",
       "      <td>0.138051</td>\n",
       "      <td>0.006295</td>\n",
       "      <td>0.168073</td>\n",
       "      <td>0.017599</td>\n",
       "      <td>0.263416</td>\n",
       "      <td>0.011814</td>\n",
       "      <td>0.366929</td>\n",
       "      <td>0.021948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.400367</td>\n",
       "      <td>0.024287</td>\n",
       "      <td>1.126698</td>\n",
       "      <td>0.116828</td>\n",
       "      <td>1.001483</td>\n",
       "      <td>0.102136</td>\n",
       "      <td>0.997334</td>\n",
       "      <td>0.408356</td>\n",
       "      <td>0.841976</td>\n",
       "      <td>0.439014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24.254700</td>\n",
       "      <td>0.052267</td>\n",
       "      <td>22.725800</td>\n",
       "      <td>0.043257</td>\n",
       "      <td>67.487801</td>\n",
       "      <td>2.441870</td>\n",
       "      <td>116.202003</td>\n",
       "      <td>0.195579</td>\n",
       "      <td>207.223999</td>\n",
       "      <td>0.809728</td>\n",
       "      <td>...</td>\n",
       "      <td>351.015991</td>\n",
       "      <td>0.912681</td>\n",
       "      <td>187.001999</td>\n",
       "      <td>0.458180</td>\n",
       "      <td>121.485001</td>\n",
       "      <td>0.341520</td>\n",
       "      <td>85.501999</td>\n",
       "      <td>279307.000000</td>\n",
       "      <td>487.742004</td>\n",
       "      <td>60172.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             CTIO_U    CTIO_U_ERR       VIMOS_U   VIMOS_U_ERR     ACS_F435W  \\\n",
       "count  17366.000000  17366.000000  17366.000000  17366.000000  17366.000000   \n",
       "mean       0.115481      0.016565      0.122138      0.005541     -3.831100   \n",
       "std        0.337539      0.002894      0.334996      0.001927     19.517122   \n",
       "min       -0.528515      0.000044     -0.069178      0.000013    -99.000000   \n",
       "25%        0.009948      0.015359      0.009744      0.004570      0.025961   \n",
       "50%        0.052541      0.015987      0.057218      0.005313      0.074789   \n",
       "75%        0.132249      0.017254      0.138051      0.006295      0.168073   \n",
       "max       24.254700      0.052267     22.725800      0.043257     67.487801   \n",
       "\n",
       "       ACS_F435W_ERR     ACS_F606W  ACS_F606W_ERR     ACS_F775W  \\\n",
       "count   17366.000000  17366.000000   17366.000000  17366.000000   \n",
       "mean       -3.976799     -0.156267      -0.435271     -0.914184   \n",
       "std        19.474924      6.818897       6.620780     11.951647   \n",
       "min       -99.000000    -99.000000     -99.000000    -99.000000   \n",
       "25%         0.007868      0.060697       0.005580      0.076885   \n",
       "50%         0.012437      0.125512       0.008344      0.166042   \n",
       "75%         0.017599      0.263416       0.011814      0.366929   \n",
       "max         2.441870    116.202003       0.195579    207.223999   \n",
       "\n",
       "       ACS_F775W_ERR  ...      HAWKI_KS  HAWKI_KS_ERR      IRAC_CH1  \\\n",
       "count   17366.000000  ...  17366.000000  17366.000000  17366.000000   \n",
       "mean       -1.350993  ...    -38.182465    -38.821423      1.621454   \n",
       "std        11.560015  ...     49.144028     48.354370      5.608161   \n",
       "min       -99.000000  ...    -99.000000    -99.000000    -15.660400   \n",
       "25%         0.009418  ...    -99.000000    -99.000000      0.186954   \n",
       "50%         0.015086  ...      0.094439      0.017297      0.443533   \n",
       "75%         0.021948  ...      0.400367      0.024287      1.126698   \n",
       "max         0.809728  ...    351.015991      0.912681    187.001999   \n",
       "\n",
       "       IRAC_CH1_ERR      IRAC_CH2  IRAC_CH2_ERR      IRAC_CH3   IRAC_CH3_ERR  \\\n",
       "count  17366.000000  17366.000000  17366.000000  17366.000000   17366.000000   \n",
       "mean       0.101949      1.469234      0.087894      1.220399     176.367126   \n",
       "std        0.036535      4.562782      0.030426      7.416217    5705.130859   \n",
       "min        0.048871    -11.669100      0.033175   -803.679016     -99.000000   \n",
       "25%        0.075382      0.168157      0.070743      0.027263       0.317497   \n",
       "50%        0.099319      0.390066      0.082867      0.384105       0.354340   \n",
       "75%        0.116828      1.001483      0.102136      0.997334       0.408356   \n",
       "max        0.458180    121.485001      0.341520     85.501999  279307.000000   \n",
       "\n",
       "           IRAC_CH4  IRAC_CH4_ERR  \n",
       "count  17366.000000  17366.000000  \n",
       "mean       1.041534     13.763525  \n",
       "std        5.302016    880.659851  \n",
       "min      -51.631199      0.233581  \n",
       "25%       -0.037307      0.337097  \n",
       "50%        0.316139      0.375890  \n",
       "75%        0.841976      0.439014  \n",
       "max      487.742004  60172.000000  \n",
       "\n",
       "[8 rows x 34 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17366.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.990175</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.270472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.025000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.977250</td>\n",
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       "      <th>50%</th>\n",
       "      <td>1.773000</td>\n",
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       "      <th>75%</th>\n",
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      "text/plain": [
       "                  Z\n",
       "count  17366.000000\n",
       "mean       1.990175\n",
       "std        1.270472\n",
       "min        0.025000\n",
       "25%        0.977250\n",
       "50%        1.773000\n",
       "75%        2.782750\n",
       "max        9.824000"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(X[:500], vars=X.columns.tolist()[:6]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y['Z'], bins=np.arange(0, 6, 0.2))\n",
    "plt.xlabel('Redshift $z$');\n",
    "plt.ylabel('Galaxies / ($\\Delta z=0.2$)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Data Randomly into Training and Testing Subsamples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = np.random.RandomState(seed=123)\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, random_state=gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17366 = 13024 TRAIN + 4342 TEST\n"
     ]
    }
   ],
   "source": [
    "print(f'{len(X)} = {len(X_train)} TRAIN + {len(X_test)} TEST')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nearest Neighbor Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the K-nearest neighbors (KNN) of an input sample to estimate its properties with [KNeighborsRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_fit = neighbors.KNeighborsRegressor(n_jobs=8).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scores are calculated using the [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination) $R^2$, for which perfect accuracy is $R^2 = 1$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6305155321741681, 0.4180525678164691)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_fit.score(X_train, y_train), knn_fit.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_fit.n_neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main hyperparameter is the value of K: the number of nearest neighbors that contribute to the final decision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def knn_study(n=(1, 2, 4, 6, 8, 12, 16), max_score_samples=2000):\n",
    "    train_score, test_score = [], []\n",
    "    for n_neighbors in n:\n",
    "        fit = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors, n_jobs=8).fit(X_train, y_train)\n",
    "        train_score.append(fit.score(X_train[:max_score_samples], y_train[:max_score_samples]))\n",
    "        test_score.append(fit.score(X_test[:max_score_samples], y_test[:max_score_samples]))\n",
    "    plt.plot(n, train_score, 'rx-', label='TRAIN')\n",
    "    plt.plot(n, test_score, 'bo-', label='TEST')\n",
    "    plt.xlabel('KNN n_neighbors')\n",
    "    plt.ylabel('KNN $R^2$ score')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EEA7RWOvpMZxqDj0B5Bw5TMK7ywnsE0Xips2gKPj27EXQAw+i9fBULVd56vPmdF2RjDXn6vnA9TPW1tCT3DO7FpSURMjkxwjt3Q1d+05c+NdbZMbtJvfYH5gemYRn6zZqxxRCiCqRS3jUgrxTp+z2SXiFd6LJ1On49b0dq9XK2cULubjma6wWi8pJhRDi2mSLohY0HDCwzDRjWAeMYR2w5OSQ/NnHpK1bQ86h3zA9MglDqaO+hBDC1cgWRR3TGY2EPPwoIY9OoSAxgTMvvkDG7l1yuQ8hhMuSolCJT7fuhM57CY8WLUj6YAUJ7/5LjowSQrgkKQoVuTUMoOmMZ2g0YhRZ//uZ0/PmkP37IbVjCSGEHSkKlWm0WhoOGEjzWc+j8/Dk/OuvkvL5ZyiFhWpHE0IIQIrCZXiEtqD58/Pwu70fl7Z8y58L5pN//rzasYQQQorClWjd3Ql+cByNp07DkpHBny/N5dJ3W2RHtxBCVVIULsg7oiuh817C2DGclJWfcP6NJZjT09WOJYS4QUlRuCi9nx+Nn5xG0IPjyD12lDPznifr55/UjiWEuAE5XBR5eXn88ccfWK1WcnNznZlJFNNoNPjf3o/mc+ahb9iQC/96i6T/ex8lP1/taEKIG4hDRfG///2PO++8k0mTJpGUlETfvn356Sf5tFtb3Bs3pvms52nQfyAZu77nzPwXyDt1Uu1YQogbhENF8corr/DBBx/g7++PyWTilVdeYcGCBc7OJkrR6PUEjhxF0xnPYC0o5M9FC0hdvxaroqgdTQhRzzlUFHl5ebRpc+Xqp9HR0VjkAnd1whjWgdB5L+Fz8y2kro7l3KuLKLyYonYsIUQ95lBR6PV6MjIybPe6LrkbnagbOi8vTI9OwfTwRPLP/smZF18g84c4tWMJIeoph4pi8uTJ/PWvfyUxMZGnnnqKMWPGMGXKFGdnE5XQaDT4RkYROvclDE2akvifGBJi3sWSk612NCFEPePQZcZ79+5N69at2bNnD4qi8Pjjj9O6dWtnZxNV4BYYSLOnnyVt4zekrl1N7vFjmB6eiLF9mNrRhBD1hENFMXLkSNasWUNoaKiz8wgHaHQ6Agbdh7FjOIn/ieHca4tp0H8gjYYMQ6OXW44IIWrGoaEnT09PEhMTnZ1F1JBnq9aEvvAivlG9ubTxG/5c+BIFCRfUjiWEuM459HEzNzeXO+64A5PJhNFotE1ft26d04IJx2g9PDBNeAiviC4kffhfzrw0j8BRo/GLvt128IEQQlSHQ0Uxe/ZsZ+cQTuZz8y14tmpF4n//Q/LH/0f2rwcJHv8Qel9ftaMJIa4zDhVFt27dADh//jxms1n2VbgovX8DmkybQfrWLVz86kvOzJ1D8N8exjuii9rRhBDXEYeK4vTp0zz++OMkJSVhtVpp0KAB//73v+XIJxek0WppcNc9GDt0JOG9f3PhraX43X4HgX+5H63BoHY8IcR1wKGd2S+99BKPPPII+/fv58CBA0yZMoUXX3zR2dmEE7k3bUbzOS/gf+fdZGzfyqmZM0jfucNumZwjh0nbuEGdgEIIl+VQUaSmpjJs2DDb4xEjRnDp0iWnhRK1Q+tmIGj0AzSZ/g+sikLyRx+Q+H8fYFUUco4cJuHd5Xi0bKl2TCGEi3Fo6MlisZCeno6/vz8AaWlpTg0lapdXeCda/nMR5/+1jMzvd7Dv5wNY8gswPfQIxrAOascTQrgYh4rir3/9K/fffz8DBgxAo9GwYcMGxo8f7+xsohbpfHxoNvM5LixfRnbxDZES/r2cjN2d8YvqhVfXrmjdZB+GEMLBorj//vsJDQ1l165dKIrCvHnziIyMdHY2Ucty/zhC3rFjNB01kgvrN+LVuTO5R4+S8O/laI1GfG7rjm9ULzxatpJzMIS4gTlUFElJSWzatIl58+Zx8uRJXnvtNdq0aUNgYKCz84laUrJPImTyY4T27gbNW5Pw7nJMj04GIHPPbjLjdpOxczsGUwi+Ub3w6dETtwYNVE4uhKhrDu3MnjlzJq1atQKgSZMmdOvWjVmzZjk1mKhdeadOETL5Mds+CWNYB0ImP0b+mTN4dQwnZOIkWi15k+Bxf0Pn48PFr77k1DNPcW7pa2Tu+wGloEDlf4EQoq44tEVx6dIlxo0bB4C7uzsTJkxg9erVTg0malfDAQPLTDOGdbDbma0zGvHrE41fn2gKkhLJjN9DZtweEmPeRevpiU+37vj27IVHq9YyNCVEPebwUU9JSUkEBwcDcPHiRaxWq1ODCddiCDbRaOgIAu4bRu4fR8iI201mfBwZO3fgFmzCr2RoqmFDtaMKIZzMoaKYMGECQ4cOpXfv3mg0GuLi4njmmWecnU24II1Wi7FDR4wdOmJ5YCxZB34kc89uLsau4uLXX2HsGI5vzyi8u96M1t1d7bhCCCdw+H4UnTp14ocffkCn0/Hwww/Trl07Z2cTLk7n6Ylfrz749epDQXJy8dDUbhLf+3fR0NRt3YqGplq3kaEpIa5jDt/VxsvLiwkTJrB9+3a2bNlCSEgIPj4+zswmriOGoCAaDRlGwOAh5B79o+ioqR/iyfh+J27BwfhGRuEbGYVbQIDaUYUQ1aSxOrBz4YUXXgBg/PjxjB8/nt69e5OVlcWyZcucHtBRqalZKIr6+00CA31ISbmsdoxK1VZGJS+Xywf2k7lnN7lH/wCNBmNYR3yjovC+6ZYqD03dyD9DZ3L1jK6eD1w/Y03yabUaAgK8y53n0BbFb7/9xqpVq4iJiWHYsGHMmDGD4cOHV3n9devW8c4772A2mxk/fjwPPvig3fyTJ08yd+5cMjIyCAwM5PXXX8fPz8+RqEJFWg9P/KJ64xfVm4KUZDLj9pAZv4fE/8Sg9fDA+9Zu+EX1wqNNWxmaEsKFOXQehdVqRavVsmfPHnr06AFAXl5eldZNSkpi6dKlfPrpp6xevZrPP/+c48eP2z33lClTmDhxImvXrqVDhw7ExMQ4ElO4EENg0dBUy4Wv0PTpZ/G++VYu/7iXs4sXcnrWTFLXr6Uw9aLaMYUQ5XBoi6J58+ZMnDiRc+fO0a1bN2bMmEFYWFiV1o2Li6NHjx62Cwrec889bNq0iSeeeAKAQ4cOYTQa6dOnDwCTJ08mMzPTkZjCBWm0WoztwzC2DyPogb+S9dMBMuJ2k7o6ltTVsXiGdcAvqheFqal4tmlrd15HzpHD5J06Ve45IEKI2uNQUbz88sts2bKFW265BTc3N2699VaGDh1apXWTk5PtLvURFBTEwYMHbY///PNPGjVqxKxZszh8+DCtWrXi+eefdySmcHFaDw98e0bh2zOKwospZMbHFR01teI9cHNDY7USMGIUjcYMt7vkiBCibjlUFEajkSFDhtgejxkzpsrrKopiNx5ttVrtHpvNZvbt28fHH39M586deeONN1i0aBGLFi2qVsaKdsqoITDQ9Y8GUz1joA+NO7TC+rcHyfz9MMnbtpOycxcXP/+US+tWoxQUENAzEs/CHDwuJeLZpDF6Ly91M19F9Z9hFbh6RlfPB66fsTbyOXx4rKNMJhP79++3PU5JSSEoKMj2ODAwkNDQUDp37gzAoEGDmDp1arVfR456qjqXyxjUDP/R4/Addj8J771L9v9+RuvlxcXde7j4/S7bYjo/PwymkKL/QkIwmEwYTCHoGwag0Tq0+81hLvczLIerZ3T1fOD6GV3qqKea6NmzJ8uWLSMtLQ1PT082b97MSy+9ZJt/0003kZaWxpEjRwgLC2Pbtm2Eh4fXdUzhAvJOnSTv+HGajhpJwoZvafL3p3Br2JCCxEQKEhMoSEigICmRyz/uRcnJsa2nMRgwBAfjFlxSIMVfg01ytrgQDqjzoggODmb69OmMGzeOwsJCRo4cSUREBBMnTmTq1Kl07tyZf/3rX8yZM4fc3FxMJhOvvPJKXccUKqvoMughkx/D+6ab7Za1Wq1YLl8uKo/EBAoTir7mnzlN1oEfodSpQvqGDa9shZhMGEIa42YKQe/vL4foClGBSk+4s1gsbN26Fa1WS9++fdHri3pl48aNDBgwoM5COkKGnqrOFTOmbdyAR8uWGMM62PI5ctSTUlhAYXJy0dZHYsl/iRQkJGDNv3JIt8bdwzZ0ZdsKMZlwCw6u8E5/zspYV1zx91yaq+cD18+oytDTM888Q9OmTdHr9cTExPDqq68SGhrKZ5995vJFIa5vVbkMelVo3Qy4N2mKe5OmdtOtViuWjPRSBVI0nJV77CiX98ZfWVCjwa1RIwymENxM9sNY7i1aXDkSK7CbHJkl6q1KiyI5OZklS5YAMHToUGbNmmU730GI65lGo0Hv3wC9fwOMHTrazVPy8ylIurIfpLC4SHL+OIK11A2btEYjOj9/zr+xhMxN7bl87DgNBw5GazRizkhH5+Nb5zvVhagNlRZFYWEhBQUFGAwGmjVrxjvvvMOMGTM4duxYXeUTos5p3d3xaB6KR/NQu+lWRcF8Ka14KyTRNpRlTksl87dDAKR+vYrUr1cVP5EWna9vUSH5+aH380fv74/Oz7/osX8D9P5+RYWi09X1P1OIKqu0KJ599lnb9ZYAvL29Wb58OevXr6+TcEK4Eo1Wi1tAI9wCGuHVqejw7ZLhJtPge0nY+C0BQ4ah9/PHkpGOOSMDc3o65ox0zGmp5J08geVyOePHGk1RoRQXiM7fv/j7UsXi74fe1w+Nvs6PPxGi8qLo2rVrmWk6nc7uZDshblRXH5mladHW9tjnllvLXcdqNmPOzMScnl5cJum2MrEUF0ven2ewZGbaHa0FFBWKt3dxcfjbiuVKmRQXi68fWje32v8BiBtGtT6epKenk5mZSfPmzcvMKxmiEuJGkXfqFCGTH7PtYDeGdSBk8mPknTpV4U53jV6PW8OG17xlrNViwXI5E3N6hq1MLKVKxZyRQf65s0WFoihl1td6edkNeen8/SlsEkye3rP4cdF0rfzNiiqoclGsWrWKuXPnoigKnTp14r333kOv17N582a2bt1KXFwcP//8c21mFcKlOOvIrPJodDrbzvbKWBUFy+XL5ZRJUcFY0tPJSUzAnJHBJYulzPpao7HUENeV/Sgl5VLyWE5UvLFVuSiWL1/O4sWLue2223j99dd5+umn+eWXXzCZTERHRzN+/PjazCmEKIdGqy3eavCDq3a+l2ZVFBp4QNKJ81cNd10ploJjR7FkZGA1m8usr/XwKGffid+VIbDi4S+th0e1TlwsfS5KCVc+F+VGVeWiSEtLY9CgQQDMnj2bbt268dZbb3H33XfXWjghhHNotFrc/Hxwb6bFvVmzCpezWq0o2dmltlAyMKdfsm2hmNPTyTt1AnNGht2hwrbXcXcvu++k9P6T4ulaTyMajQaPli3lXJTrQJWLQlvqeHBfX1+8vLykJISoZzQlO8y9vcucpFia1WpFyc3BnJ5RZt+JpbhY8v48gzn9F6z5+WVfx83NNrzl1jiE82++Tuamdlw+dhz/O+/GWlhI7onjaD2N6LyMaI3GCs+QF7WvykWRk5NDz5496dixI+Hh4SiKwrlz52jatOL/mYQQ9ZNGo0Fn9EJn9ILGjStdVsnLLdopX7Jlkn6paEulVMFYrVbbuSiXvlnHpfJeU69H6+WFzrO4OIxGdEav4q+lpxnRGr2KvnpemSfnqjiuykWxb98+Dh8+bPuvWbNm9O/fH4PBQLt27WjXrh3z58+vzaxCiOuQ1sMTg8kTg8lU7nzbuSj33EXCpm8JHDUagykEJTcHS3YOSk5O8ffZRV9ziqZZsrIoTE4u+j4nu9yjv0rTuHvYSsO+WIrLxtOI1stoKxit0YjOy6voew+PG/os+yoXha+vL927d6d79+62aQUFBRw7dozff/+dI0eO1EpAIUT9VeZclJZXzkXx6hRR5eexWq1Y8/OLSqSkVGzf59gXTE7RPHNaGpZzZ4uXy638BTQatJ5Gzvh4gbunXdkUFcxVZePpZbeFozEYau3qxHVxQECNTvM0GAyEh4fL/SKEEA5x5FyU8mg0GjQeHmg9PIDKz1Epj1VRUPJyUbJzsOQWF0rxFkxRueSg5GTjphSSk5aBJSeHgsREWwGVtx/Gjk5nPyRWMizmVWp4rJJhtcrOyK+LAwLkegBCCCjKeCQAABuESURBVNXU5rko1aHRam37XCo7p72iy3hbzeZSBZNTpmBKtmZs3+fmUJh6saiYcrKhnHNc7PIZDFe2YjxLDYkZi8rG65ZbOL/sDQp+6knqvv125esMUhRCCFFDGr0evY8v+PhWe12r1Yq1sNCuUEoK5sr3ObaCUXJyMKenoyRcsM0rudxL8rbtNBg4yOlFK0UhhBAq0mg0RVsMBsM1z8Qvj9VqJfvgLyT+9z1Md/YjaesOvDqGq7dF8dxzz1U4T6PRsHDhwhoHEkIIUXW5fxwh6f0VNJ7yBKG9u6Ft29G2j8JZZVGtomjbtm2ZaZcuXeLDDz+kSZMmTgkkhBCi6px1QEBlqlUUDz30kN3juLg4Zs6cyeDBg5kzZ45TAgkhhKi6ujggwKF9FGazmSVLlvD111/z4osvcs899zgtkBBCCNdS7aI4ffo0Tz31FF5eXqxevRpTBWdbCiGEqB+qdU76V199xahRo7jrrrv46KOPpCSEEOIGUK0titmzZ6PVaomJieG9996zTbdarWg0Gn766SenBxRCCKGuahXF1q1bayuHEEIIF1Wtonj77bcrnCfnUQghRP0k51EIIYSolJxHIYQQolJyHoUQQohKyXkUQgghKiXnUQghhKiUnEchhBCiUnIehRBCiEpVqyjOnj1Ljx49yp23dOlSpk+f7pRQQlwv4g8lErvzBGmZ+TT0dWd4dGsiw2VIVtQv1SqKadOm8eGHH9K+fXvbtMzMTKZPn86pU6ekKIRTufqbcPyhRD7ceIQCswJAamY+H248AuBSOV2dq/+eRTWL4oUXXmDSpEl89tlnhISEcOTIER5//HHatm1LbGxsbWUUN6C6fhO2Wq1YFCtmi4LZUvS9xaLYPTZbFCyW4q+KlZVbj9nylSgwK6z87hhGdz06rQaNVoNOo0GrLf5Po0GrpfirBp1tWqmvWg1aDUXra4qXKZ6v0RRdBaG+kLK9PlSrKAYOHEhmZiYPP/wwY8eO5bXXXmPSpEk8+uijtZVP1CJX+SRnURTyCizk5pvJy7eQW2Cu8E34481/cC4lC4vFWvSmrShX3sBtb+5X3swttjf68qeblaJ1LYrVaf+ey7mFvLnqoNOe72pXCqXo+/IKpXQZaYvLymDQoSjWoukarpRS8TolpaWxFRh28zRXLVc6g91ydlkoU4K219FoWPld+b/nL7YdJzTYB71Og16nLf5Pg67kq7ZaB2zWe7X9t6yxWq3V/guJiYlh6dKlxMTE0Lt372q/6Lp163jnnXcwm82MHz+eBx98sNzlduzYwfz589m2bVu1XyM1NQvFiX/8jgoM9CEl5bLaMcq4+pMcgEGvZfyAsCr9D6ZYreQXWMgrsJBXYCa3+A0+L7/ksbnozb/gypv/lXnFXwss5OWby7xRXIvtDUNb9Cai02nQa4u/6rTotBq7Nxa7x6WWK72+7qrnKnfd4nVK3qyWf/0bGdkFZfL5eRl4ckQEitWKohT9Z7FasSpFhVQ0Hbv5ivXKvCvLYTfPetVyReteWab0cpaS+SXPoVjRu+nIyyssWsZ6JZfdc5T6WrKc5arnKb1c0TLV+vU5hUaD3e/UTW//uypTLloNer3W9nvX6bS46Ur9v1BqHbv/P65ap1GAF1mX865ap+T/HS1upcqspAxrW03/lktotRoCArzLnefQmdmPPvooGRkZfPzxx0RFRaGtRrsnJSWxdOlSYmNjMRgMjB49mu7du9OmTRu75S5evMjixYsdiSeqIHbniXI/yX2y+SgJqTnk5Zvt39xLPvEXF0NevoWqvD/odRo8DHo83XVFXw06/LwNmNyNeBh0eBr0eJSa5+le9Pg/6w+TWc6bcICvO68+FuWkn0LNjOrXptw/0FH92tCqsa+KycpXWx9arNZyyq90admKrFTZFE9b+sUv5Zatt6cbf727HYVmxW5I0Fx6ONCiUFhqOLBky/HqdQryzXbDhnbPpVgxFy/vbBooKg99UZlVtHXkVlwyJR9c9Hr7DyTlrWObptXw5Y7j5f4tx+484bStimoVxU033WRrSKvVSm5uLrfcckvRD6WK51HExcXRo0cP/P39AbjnnnvYtGkTTzzxhN1yc+bM4YknnmDJkiXViSiqKDUzv9zpOflmvok/bXsDt31119PQxx0Pd33RtFJv6iVv+J7uRdOLlil683fTOzZEcH8Fb8LDo1s79Hy1oeSP0BWG79Sk0RQNbekc+FVXVLZj7mxLtw7BTkxZuav3UZUUSukSMlsUvH08uJiWjdlcekiz9DpX9m0VWuyHOO3XsS+twnxzmWm2dUoNlVZHRX/jjqhWUaxfv77GL5icnExgYKDtcVBQEAcP2o/n/t///R8dO3akS5cuNX49Ye/YuXTWxZ2ucH5DH3defayn6jtMr5c34chwE5HhJpcdYnR1rvJ71mg0tk/qlQkM9CHF172OUtkrc8BF8RbVwo8OkJ5V/ta3s1SrKJxxKXFFUezehErO6i5x9OhRNm/ezAcffEBiYqLDr1PRWJsaAgN9VH19q9XKwWMX+fy7o/x64iK+XgZ6dWnMvt8TKSi88knO3U3H3waHExTkGsMm9/X14b6+ZS9t76rU/j1XhStmlN9zzTx8Xyfe/vIX8gsttmnubjomDAp3WlaH9lHUhMlkYv/+/bbHKSkpBAUF2R5v2rSJlJQURowYQWFhIcnJyTzwwAN8+umn1Xod2ZldXBAnUlkfd5oTFzLx8zYwul8bors2wd2go0Nz/zKf5MKb+7vcJ+Pr4dO6ZKw5V88HrpkxvLk/4/q3r/HfcmU7sx066qkmkpKSGDNmDKtWrcLT05PRo0fz0ksvERERUWbZc+fOMW7cODnqqZoUq5Wf/khhfdxp/kzOIsDXg4E9mtMrIgQ3vc4lMlaHq+cDyegMrp4PXD9jTfI5/ainmggODmb69OmMGzeOwsJCRo4cSUREBBMnTmTq1Kl07ty5riPVGxZFYd/hZNbHnSYhNYfgBp48NLADPcKDrzn2KoQQFanzLYq6ciNtUZgtCnG/JbIh/gzJ6bk0CfRiUGQLbgsLQqu99k7p+vwpqa5Ixppz9Xzg+hnrzRaFcJ6CQgu7Diawce8Z0jLzCTX58MTwznRt2whtPbrMgxBCXVIU16G8AjM7fr7Apn1/kpldQJumfozvH0anlg1VP6xVCFH/SFFcR3LyCtl64BybfzxLdp6Zji0aMHhIOO2a+UtBCCFqjRTFdeByTgFb9p9l64Fz5OZb6NI6gEE9W9C6iZ/a0YQQNwApCheWnpXPt/v+ZPvP5yksVLilfSD3RrYg1ORaJ/wIIeo3KQoXdDEjl417/2TXLwlYFIUeHYMZGNmCJo281I4mhLgBSVG4kKS0HL754QzxvxVduiSqcwgDezQnqIFR5WRCiBuZFIULOJ+SxTfxZ9h7OAm9Tkvfm5owoHtzGvp6qB1NCCGkKNR0JvEy6+NOc+BoCu5uOu7p1px7bmuGn7c6V6cUQojySFGo4Pi5DNbFnebXk6l4uusZ3LMFd93WDG9PN7WjCSFEGVIUteTqe9gO69OKBt7urIs7zZE/0/H2dGN4n1b0u7kpRg/5NQghXJe8Q9WCq+9hm5qZz4pvDmO1UuZS30II4eqkKGpBefejtlrBy0PPK5Mjy73UtxBCuCq59nQtqOhetdl5ZikJIcR1R4qiFlR0r1pn3sNWCCHqihRFLRge3Zqrr9Fn0GsZHt1anUBCCFEDso+iFjQO8MJqBU93HXn5Fts9bCPDTWpHE0KIapOiqAXr4k7j6a7n1Sk9CW3WwKXviCWEENciQ09OdjY5i5+OpnDXrXJ+hBCifpCicLL1cafxMOi467ZmakcRQginkKJwovMXs9l/JJk7bmmKl4dcjkMIUT9IUTjRN3GnMbjpuFu2JoQQ9YgUhZMkpGaz93AS/W5ugo/RoHYcIYRwGikKJ/km/gxuOi33dGuudhQhhHAqKQonSL6Uww+Hkuh7UxN8vWRrQghRv0hROME38WfQajX07y5bE0KI+keKooYupucS91si0V0b4y93phNC1ENSFDW04YczaDQwQLYmhBD1lBRFDaRl5rHrYAK9IxrT0NdD7ThCCFErpChqYOMPfwIwsEeoykmEEKL2SFE46NLlfHb+coGoziYC/GRrQghRf0lROGjT3j9RFCsDI1uoHUUIIWqVFIUDMrLy2fG/80R2CibI31PtOEIIUaukKBzw7b6zmC0Kg2RrQghxA5CiqKbMnAK2/XyOHh2DCW5oVDuOEELUOimKatry41kKCxUG9WyhdhQhhKgTUhTVkJVbyHcHznFbhyBCArzUjiOEEHVCiqIatvx4lvwCi2xNCCFuKKoUxbp16xg4cCB33303n3zySZn53333HUOGDOG+++7jscceIyMjQ4WU9nLyCvnuwFluaR9I00BvteMIIUSdqfOiSEpKYunSpXz66aesXr2azz//nOPHj9vmZ2VlMW/ePGJiYli7di3t27dn2bJldR2zjO/2nyM338Jg2ZoQQtxg6rwo4uLi6NGjB/7+/hiNRu655x42bdpkm19YWMjcuXMJDg4GoH379iQkJNR1TDu5+Wa27D9L1zaNaB7so2oWIYSoa/q6fsHk5GQCAwNtj4OCgjh48KDtcYMGDbjrrrsAyMvLIyYmhrFjx1b7dQICnDc89OXWo2TnmRk3qCOBgdUvCkfWqWuuntHV84FkdAZXzweun7E28tV5USiKgkajsT22Wq12j0tcvnyZxx9/nLCwMIYNG1bt10lNzUJRrDXKCpBXYCZ2+3EiWgfg76EnJeVytdYPDPSp9jp1zdUzuno+kIzO4Or5wPUz1iSfVqup8AN2nQ89mUwmUlJSbI9TUlIICgqyWyY5OZkHHniA9u3bs2DBgrqOaGf7z+fJyi2UfRNCiBtWnRdFz549iY+PJy0tjdzcXDZv3kyfPn1s8y0WC5MnT2bAgAHMnj273K2NupJfaOHbvX8S3qIBrZv4qZZDCCHUVOdDT8HBwUyfPp1x48ZRWFjIyJEjiYiIYOLEiUydOpXExER+//13LBYL3377LQCdOnVSZcti5/8ukJlTyOColnX+2kII4SrqvCgABg8ezODBg+2mvffeewB07tyZI0eOqBHLTkGhhY0/nCGsuT/tmvmrHUcIIVQjZ2ZXYNfBBDKyC7hPtiaEEDc4KYpyFJoVNvxwhnZN/WjfXLYmhBA3NimKcuz+NYFLl/MZHNVS1Z3pQgjhCqQormK2KGyIP03rxr50bNFA7ThCCKE6VXZmu6L4Q4nE7jxBamY+ALeFBcnWhBBCIFsUQFFJfLjxiK0kALb9dJ74Q4kqphJCCNcgRQHE7jxBgVmxm1ZgVojdeUKlREII4TqkKMBuS6Iq04UQ4kYiRQEE+LpXa7oQQtxIpCiA4dGtMejtfxQGvZbh0a1VSiSEEK5DjnoCIsNNALajngJ83Rke3do2XQghbmRSFMUiw01SDEIIUQ4ZehJCCFEpKQohhBCVkqIQQghRKSkKIYQQlaq3O7O1Wte5TpMrZamIq2d09XwgGZ3B1fOB62d0NF9l62msVqvV0UBCCCHqPxl6EkIIUSkpCiGEEJWSohBCCFEpKQohhBCVkqIQQghRKSkKIYQQlZKiEEIIUSkpCiGEEJWSohBCCFEpKYpa9Pbbb3Pvvfdy77338sorr6gdp0KLFy/m2WefVTtGubZt28bw4cMZMGAA//znP9WOU8aaNWtsv+PFixerHcdOVlYWgwYN4ty5cwDExcUxePBg7r77bpYuXapyurL5Pv/8cwYNGsTgwYN57rnnKCgoUDlh2YwlPv74Y8aOHatSqiuuzvfzzz8zatQo7r33Xp566imn/QylKGpJXFwcu3fv5uuvv2b16tUcOnSILVu2qB2rjPj4eL7++mu1Y5Tr7NmzzJ07l+XLl7N27Vp+//13du7cqXYsm9zcXBYsWMBHH33EmjVr2L9/P3FxcWrHAuCXX35hzJgxnD59GoC8vDxmzZrF8uXL2bBhA7/99puqP8ur8506dYoVK1awcuVK1q5di6IofPrpp6rlKy9jiePHjxMTE6NOqFKuzpeVlcWTTz7J/Pnz+eabbwBYtWqVU15LiqKWBAYG8uyzz2IwGHBzc6N169ZcuHBB7Vh20tPTWbp0KZMnT1Y7Srm2bNnCwIEDMZlMuLm5sXTpUrp06aJ2LBuLxYKiKOTm5mI2mzGbzbi7u6sdC4AvvviCuXPnEhQUBMDBgwcJDQ2lWbNm6PV6Bg8ezKZNm1wmn8FgYO7cuXh7e6PRaGjXrp3qfy9XZwQoKCjghRdeYOrUqSomK3J1vj179tC1a1fCwsIAmDNnDnfddZdTXqveXj1WbW3btrV9f/r0aTZu3Mhnn32mYqKyXnjhBaZPn05CQoLaUcp15swZ3NzcmDx5MgkJCfTt25dp06apHcvG29ubv//97wwYMABPT09uu+02br75ZrVjAbBgwQK7x8nJyQQGBtoeBwUFkZSUVNexbK7O16RJE5o0aQJAWloan3zyCS+//LIa0WyuzgiwZMkSRowYQdOmTVVIZO/qfGfOnMFoNDJ9+nROnjzJzTff7LQhZdmiqGXHjh3joYce4plnnqFFixZqx7H58ssvCQkJITIyUu0oFbJYLMTHx7Nw4UI+//xzDh486FLDZEeOHOGrr75i+/bt7Nq1C61Wy4oVK9SOVS5FUdBorlxG2mq12j12FUlJSYwfP54RI0bQvXt3tePY2bNnDwkJCYwYMULtKOWyWCzs3r2bp556itjYWHJzc502RCZFUYsOHDjAhAkTmDFjBsOGDVM7jp0NGzawZ88ehgwZwltvvcW2bdtYuHCh2rHsNGrUiMjISBo2bIiHhwd33nknBw8eVDuWze7du4mMjCQgIACDwcDw4cPZt2+f2rHKZTKZSElJsT1OSUmxG1JxBSdOnGD06NEMGzaMxx9/XO04Zaxfv55jx44xZMgQ5syZw2+//eZSW7iNGjWiS5cuNGvWDJ1Ox4ABA5z29yJDT7UkISGBxx9/nKVLl7rkp/b333/f9n1sbCz79u1j1qxZKiYq6/bbb2fmzJlkZmbi5eXFrl27uOOOO9SOZRMWFsarr75KTk4Onp6ebNu2jc6dO6sdq1xdunTh1KlTnDlzhqZNm7J+/XqX+mSclZXFww8/zLRp0xg6dKjaccpVeihs7969vP3227zxxhsqJrLXq1cvli1bRkJCAiEhIWzfvp3w8HCnPLcURS1ZsWIF+fn5LFq0yDZt9OjRjBkzRsVU15cuXbrwyCOP8MADD1BYWEhUVJRLvbn16tWL33//neHDh+Pm5kbnzp159NFH1Y5VLnd3dxYtWsSTTz5Jfn4+0dHR9O/fX+1YNqtWreLixYu8//77tg8x/fr14+9//7vKya4fISEhzJ8/n8mTJ5Ofn0+HDh2YOXOmU55b7nAnhBCiUrKPQgghRKWkKIQQQlRKikIIIUSlpCiEEEJUSopCCCFEpaQoxHXn3Llz3HTTTXbTNmzYQPfu3YmPj+fcuXO0b9+eL7/80m6ZFStW2C5psGzZMiIjI+1OQgMYNGgQe/furd1/wDUMGTKEzMzMSpcZO3ZsuddqKu9nI0RNSVGI697KlStZtGgRH3zwge3kRq1Wy+LFizl58mSF62VlZTFz5kxc7QjxNWvW4Ovrq3YMIWzkhDtxXYuJiSE2NpZPP/3U7kJtHh4e/O1vf+Mf//gHK1euxGAwlFn3vvvu45dffuG///0vDz/8cKWv8+yzz+Lt7c0ff/xBYmIi7du3Z/HixXh5eVW4TmxsLFu2bEGr1XLmzBk8PDxYvHgxrVu35vLlyyxYsICjR49SWFhIZGQkzzzzDHq9nvbt2xMfH4+fnx+vvPIK27Ztw8fHh4iICE6cOMFHH30EwNatW1mxYgUXL14kMjLSdr8ORVGYPXs2hw4dQq/XM2fOHLp27UphYSGLFi0iPj4enU5HREQEzz33HN7e3vTr14+IiAj++OMPnnrqKVJSUli5ciVubm64u7szf/582rRp48ivSNQDskUhrluvvPIKS5YsYezYseVezXPKlCkYjcYKb9Lj7u7OkiVLWL58OYcOHbrm6/3222+sWLGCDRs2cP78+SpdpvvHH3/k+eefZ/369XTp0sV2kbaFCxcSHh5ObGwsq1ev5tKlS3aXVYGiCzceOnSI9evXs3LlSs6ePWs3Pzs7m5UrV7Jhwwa+//57fvrpJ6Do3hNRUVGsXr2aadOm8fe//52CggLeeecdkpOTWbNmDWvWrEFRFLsbarVt25aNGzfSr18/Fi5cyH/+8x+++uorRo0axYEDB675bxX1lxSFuC7l5ORw9OhRYmJiWLJkCb///nuZZbRaLa+++iqxsbHs2bOn3Odp374906ZNY8aMGeTk5FT6mr1797bdX6Rdu3ZkZGRcM2d4eDgmkwmAjh072tbZsWMHn3/+OUOGDGH48OEcPHiQo0eP2q27c+dOhgwZgru7OwaDgfvvv99u/sCBA9HpdHh6etKiRQtSU1MB8PX1ZeDAgUDRZUYATp48yffff8/o0aNxc3NDq9UyduxYdu3aZXu+W2+9FQCdTkf//v0ZPXo08+fPx9fXl5EjR17z3yrqLxl6EtclDw8P3nnnHdzc3Jg0aRJPPPEEsbGx+Pv72y0XEhLCiy++yMyZMyu82NzYsWPZvXt3ufcfuPo1S2g0mirt26hoHUVRePPNN2ndujUAmZmZZS77rdfb/3lqtdoK55d+7quXUxQFNze3MpcaVxSFwsJC22Oj0Wj7/rXXXuPo0aPExcURExPDmjVrePPNN6/57xX1k2xRiOuSVqvFzc0NgEcffZQ2bdowY8YMFEUps2z//v3p06cPH374YYXP9/LLL7Nz507OnDlTa5lL69WrFx988AFWq5WCggKmTJnCxx9/bLdMdHQ0a9eupaCgALPZXOV7caSnp7N9+3ag6J7jHh4ehIaG0rt3bz777DMKCwtRFIVPPvmEqKioMuunpaURHR2Nv78/EyZMYNq0afz66681/0eL65YUhbjuaTQaFi9ezIkTJyq87POcOXNo3Lhxhc/RsGFDFi1aZPcJuzbNnj2bnJwcBg8ezODBg2nXrh2PPPKI3TLDhw8nIiKCoUOH2oaMPD09r/ncAQEBbN68mSFDhhATE8OyZcvQ6/VMmTKFRo0aMXToUAYMGIDZbGb27Nll1m/YsCFTpkxhwoQJDB8+nCVLlth2lIsbk1w9VggXtXv3blJTUxkyZAgA//znP3F3d+fpp59WOZm40UhRCFEDDzzwANnZ2eXO++STT/D29nb4uZOSknj22We5ePEiiqIQFhbGvHnz8PHxcfg5hXCEFIUQQohKyT4KIYQQlZKiEEIIUSkpCiGEEJWSohBCCFEpKQohhBCVkqIQQghRqf8Hv8TV7DVfu2sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_study()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a [binary decision tree](https://en.wikipedia.org/wiki/Decision_tree_learning) to sort each input sample into a small \"peer group\" with [DecisionTreeRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html). Note the similarity with KNN, except now we ask a set of questions to identify the \"peer group\", instead of using nearest neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = np.random.RandomState(seed=123)\n",
    "tree_fit = tree.DecisionTreeRegressor(random_state=gen).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 0.745754528682889)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_fit.score(X_train, y_train), tree_fit.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_fit.tree_.max_depth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A decision tree is highly interpretable since we can see exactly what questions it is asking at each step. As a first step, look at the \"feature importance\" of your trained model, which quantifies how much the performance suffers when a single feature is shuffled to make it non-informative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance = pd.DataFrame(\n",
    "    {'feature': X.columns, 'importance': tree_fit.feature_importances_}\n",
    ").sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance.plot('feature', 'importance', 'barh', figsize=(10, 10), legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-train using only the 8 most important features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VIMOS_U</td>\n",
       "      <td>0.467002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ACS_F850LP</td>\n",
       "      <td>0.139219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ACS_F606W</td>\n",
       "      <td>0.112596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>WFC3_F125W</td>\n",
       "      <td>0.058217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>IRAC_CH2</td>\n",
       "      <td>0.054210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACS_F435W</td>\n",
       "      <td>0.026314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ACS_F775W</td>\n",
       "      <td>0.021706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>WFC3_F160W</td>\n",
       "      <td>0.015878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       feature  importance\n",
       "2      VIMOS_U    0.467002\n",
       "12  ACS_F850LP    0.139219\n",
       "6    ACS_F606W    0.112596\n",
       "18  WFC3_F125W    0.058217\n",
       "28    IRAC_CH2    0.054210\n",
       "4    ACS_F435W    0.026314\n",
       "8    ACS_F775W    0.021706\n",
       "20  WFC3_F160W    0.015878"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_features = importance[:8]['feature']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The re-trained tree is much simpler and almost equally accurate on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 0.7381381023225613)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_fit = tree.DecisionTreeRegressor(random_state=gen).fit(X_train[best_features], y_train)\n",
    "tree_fit.score(X_train[best_features], y_train), tree_fit.score(X_test[best_features], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_fit.tree_.max_depth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tree_study(n=(3, 4, 5, 6, 8, 10, 15, 20, 25, 30, 35), seed=123):\n",
    "    gen = np.random.RandomState(seed)\n",
    "    train_score, test_score = [], []\n",
    "    for max_depth in n:\n",
    "        fit = tree.DecisionTreeRegressor(max_depth=max_depth, random_state=gen).fit(X_train[best_features], y_train)\n",
    "        train_score.append(fit.score(X_train[best_features], y_train))\n",
    "        test_score.append(fit.score(X_test[best_features], y_test))\n",
    "    plt.plot(n, train_score, 'rx-', label='TRAIN')\n",
    "    plt.plot(n, test_score, 'bo-', label='TEST')\n",
    "    plt.xlabel('DecisionTree max_depth')\n",
    "    plt.ylabel('DecisionTree $R^2$ score')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_study()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chose a `max_depth` of 5 to minimize overfitting the training data (or choose 10 to balance overfitting with accuracy on the test data):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7262743028011795, 0.699983128890002)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen = np.random.RandomState(seed=123)\n",
    "tree_fit = tree.DecisionTreeRegressor(max_depth=5, random_state=gen).fit(X_train[best_features], y_train)\n",
    "tree_fit.score(X_train[best_features], y_train), tree_fit.score(X_test[best_features], y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that a tree of depth $n$ sorts each sample into one of $2^n$ leaf nodes, each with a fixed prediction. This leads to a visible discretization error for small $n$, which is not necessarily a problem if the uncertainties are even larger:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predict = tree_fit.predict(X_test[best_features])\n",
    "plt.scatter(y_test, y_predict, lw=0)\n",
    "plt.xlabel('Target value')\n",
    "plt.ylabel('Predicted value');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tree Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree.export_graphviz(tree_fit, out_file='tree.dot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "!dot -Tpng tree.dot -o tree.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_branch(path=[], fit=tree_fit, X=X_train[best_features], y=y_train.values):\n",
    "    tree = fit.tree_\n",
    "    n_nodes = tree.node_count\n",
    "    children_left = tree.children_left\n",
    "    children_right = tree.children_right\n",
    "    feature = tree.feature\n",
    "    threshold = tree.threshold\n",
    "    # Traverse the tree using the specified path.\n",
    "    node = 0\n",
    "    sel = np.ones(len(X), bool)\n",
    "    cut = threshold[node] \n",
    "    x = X.iloc[:, feature[node]]\n",
    "    print('nsel', np.count_nonzero(sel), 'cut', cut, 'value', np.mean(y[sel]))\n",
    "    for below_threshold in path:\n",
    "        if below_threshold:\n",
    "            sel = sel & (x <= cut)\n",
    "            node = children_left[node]\n",
    "        else:\n",
    "            sel = sel & (x > cut)\n",
    "            node = children_right[node]\n",
    "        cut = threshold[node] \n",
    "        x = X.iloc[:, feature[node]]\n",
    "        print('nsel', np.count_nonzero(sel), 'cut', cut, 'value', np.mean(y[sel]))\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "    xlim = np.percentile(x[sel], (1, 95))\n",
    "    below = sel & (x <= cut)\n",
    "    above = sel & (x > cut)\n",
    "    ax[0].hist(x[below], range=xlim, bins=50, histtype='stepfilled', color='r', alpha=0.5)\n",
    "    ax[0].hist(x[above], range=xlim, bins=50, histtype='stepfilled', color='b', alpha=0.5)\n",
    "    ax[0].set_xlim(*xlim)\n",
    "    ax[0].set_xlabel(X.columns[feature[node]])\n",
    "    ylim = np.percentile(y, (1, 99))\n",
    "    y_pred = np.empty_like(y)\n",
    "    y_pred[below] = np.mean(y[below])\n",
    "    y_pred[above] = np.mean(y[above])    \n",
    "    mse2 = np.mean((y[sel] - y_pred[sel]) ** 2)\n",
    "    n_below = np.count_nonzero(below)\n",
    "    n_above = np.count_nonzero(above)\n",
    "    mse = (np.var(y[below]) * n_below  + np.var(y[above]) * n_above) / (n_below + n_above)\n",
    "    #print('mse', mse, mse2)\n",
    "    ax[1].hist(y[below], range=ylim, bins=25, histtype='stepfilled', color='r', alpha=0.5)\n",
    "    ax[1].axvline(np.mean(y[below]), c='r', ls='--')\n",
    "    ax[1].hist(y[above], range=ylim, bins=25, histtype='stepfilled', color='b', alpha=0.5)\n",
    "    ax[1].axvline(np.mean(y[above]), c='b', ls='--')\n",
    "    ax[1].set_xlabel('Redshift target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nsel 13024 cut 0.007975295186042786 value 1.9733001\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_branch([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nsel 13024 cut 0.007975295186042786 value 1.9733001\n",
      "nsel 3044 cut 0.008564084768295288 value 3.4734519\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_branch([True,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nsel 13024 cut 0.007975295186042786 value 1.9733001\n",
      "nsel 9980 cut 0.28916649520397186 value 1.5157386\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_branch([False,])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use an ensemble of decision trees that are individually less accurate but collectively more accurate, with [RandomForestRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html). The individual trees are trained on random sub-samples of the data and the resulting \"forest\" of predictions are averaged. The random subsets for each tree are created by:\n",
    " - Using a \"bootstrap\" resampling of the rows, and\n",
    " - Finding the best split at each branch from a random subset of `max_features` features (columns)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = np.random.RandomState(seed=123)\n",
    "forest_fit = ensemble.RandomForestRegressor(\n",
    "    n_estimators=15, max_features=0.5, random_state=gen, n_jobs=8).fit(X_train, y_train.values.reshape(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9763620168915049, 0.8630589304956074)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_fit.score(X_train, y_train), forest_fit.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the first branch for two of the trees in our forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nsel 13024 cut 0.01966005004942417 value 1.9733001\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_branch(fit=forest_fit.estimators_[0], X=X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nsel 13024 cut 0.0073961850721389055 value 1.9733001\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_branch(fit=forest_fit.estimators_[1], X=X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forest_study(n=(1, 2, 3, 5, 10, 15, 20, 25, 30), seed=123):\n",
    "    gen = np.random.RandomState(seed)\n",
    "    train_score, test_score = [], []\n",
    "    for n_estimators in n:\n",
    "        fit = ensemble.RandomForestRegressor(\n",
    "            n_estimators=n_estimators, max_features=0.5, random_state=gen, n_jobs=8).fit(\n",
    "            X_train, y_train.values.reshape(-1))\n",
    "        train_score.append(fit.score(X_train, y_train))\n",
    "        test_score.append(fit.score(X_test, y_test))\n",
    "    plt.plot(n, train_score, 'rx-', label='TRAIN')\n",
    "    plt.plot(n, test_score, 'bo-', label='TEST')\n",
    "    plt.xlabel('RandomForest n_estimators')\n",
    "    plt.ylabel('RandomForest $R^2$ score')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forest_study()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance (again)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance = pd.DataFrame(\n",
    "    {'feature': X.columns, 'importance': forest_fit.feature_importances_}\n",
    ").sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance.plot('feature', 'importance', 'barh', figsize=(10, 10), legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction uncertainty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we now have multiple predictions for each sample, we can use their spread as an estimate of the uncertainty in the mean prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = forest_fit.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 4342)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_each = np.array([tree.predict(X_test) for tree in forest_fit.estimators_])\n",
    "y_pred_each.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(y_pred == np.mean(y_pred_each, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_error = y_test.values.reshape(-1) - y_pred\n",
    "y_pred_spread = np.std(y_pred_each, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the estimated uncertainty increases when the estimated values are farther from the true values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.abs(y_pred_error), y_pred_spread, lw=0)\n",
    "plt.xlabel('$|y_{true} - y_{pred}|$')\n",
    "plt.ylabel('Forest prediction spread')\n",
    "plt.xlim(0, 3)\n",
    "plt.ylim(0, 3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more quantitative check of the estimated uncertainties, plot the distribution of \"pulls\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-2.5, 2.5, 50)\n",
    "plt.hist(y_pred_error / y_pred_spread, bins=bins, density=True)\n",
    "pull = 0.5 * (bins[1:] + bins[:-1])\n",
    "plt.plot(pull, scipy.stats.norm.pdf(pull), 'r-', label='$\\sigma=$ spread')\n",
    "correction = 2.0\n",
    "plt.plot(pull, correction * scipy.stats.norm.pdf(correction * pull), 'r--',\n",
    "         label=('$\\sigma=$ spread / %.1f' % correction))\n",
    "plt.legend()\n",
    "plt.xlabel('pull = dy / $\\sigma$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This test reveals that the spread overestimates the true uncertainty by about a factor of 2, which is not surprising since the individual trees do not use the full training data samples or features. The lesson is that the spread is a useful indicator but needs to be calibrated using a study like this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"Out-of-bag\" Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining the trees in a forest is known as \"bagging\".  Since each tree leaves out some samples, we can use these omitted (aka \"out-of-bag\") samples to test our model. This means we no longer need to set aside a separate test dataset and can use all of our data for training the forest.\n",
    "\n",
    "*Technical note: since RandomForestRegressor does not support a max_samples parameter, the out-of-bag samples are only due to bootstrap sampling with replacement, which generally needs more estimators for reasonable statistics.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = np.random.RandomState(seed=123)\n",
    "forest_fit = ensemble.RandomForestRegressor(\n",
    "    n_estimators=100, max_features=0.5, oob_score=True, random_state=gen, n_jobs=8).fit(X, y.values.reshape(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.982570310465904, 0.8767722201664717)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_fit.score(X_train, y_train), forest_fit.oob_score_"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}