MDDanalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Major Depression Disorder analysis with Gene Expression and Demographic Symptom data\n",
    "\n",
    "In this experiment, we focus on the RNA-seq data provided by Le et al.'s research, \"Identification and replication of RNA-Seq gene network modules associated with depression severity\". The data used in this work is provided in two separate .csv files; the gene expression and demographic symptom data sets. The gene expression data is created by analyzing the DNA, quality control, sequencing with RNA-seq, normalizing the values and filtering with variance analysis. In this experiment, we recreate a similar scenario to understand the importance of different genes, in addition, developing AI models with high accuracies. Further, we examine the demographic symptom dataset to uncover the important features. Finally, we present the results of our experiments.\n",
    "\n",
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.covariance import EmpiricalCovariance\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "from sklearn.feature_selection import chi2, f_classif, mutual_info_classif\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import SelectPercentile\n",
    "from sklearn.feature_selection import SelectFpr\n",
    "from sklearn.feature_selection import SelectFdr\n",
    "from sklearn.feature_selection import SelectFwe\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The original data is presented in two different files. The \"sense.csv\" file contains the gene expression data and the \"Demographic_symptom.csv\" file contains the demographic symptom data. We applied pre-processing methods to the original gene expression data; first we applied quantile normalization, further we applied Log2 transform and lastly applied Coefficient of variation filter (CoV > 0.45). The original gene expression data contains 8923 different genes, after the pre-processing the number of genes decreased to 5587. This pre-processing step is applied with R and the processed data is stored as \"filtered.csv\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demographic Symptom Dataset\n",
    "\n",
    "In this dataset, the diagnosis (target of this dataset) is presented in the \"Diag\" column. This value is extracted from the demographic symptom dataset and binary encoded to represent Healthy Control (HC) with 0 and Major Depressive Disorder (MDD) with 1. The columns \"PrimaryDiagnosis\" and \"batch\" are also removed from the dataset. The spaces take place in the demographic symptom dataset are filled with mean of the corresponding feature. The resulting dataset has 157 rows and 36 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>hs-CRP</th>\n",
       "      <th>shaps_score</th>\n",
       "      <th>psqi_score</th>\n",
       "      <th>ctq_denial_score</th>\n",
       "      <th>ctq_emoabuse_score</th>\n",
       "      <th>ctq_emoneglect_score</th>\n",
       "      <th>ctq_physabuse_score</th>\n",
       "      <th>ctq_physneglect_score</th>\n",
       "      <th>...</th>\n",
       "      <th>qids_psychomotor_score</th>\n",
       "      <th>qids_score</th>\n",
       "      <th>tci_coop_score</th>\n",
       "      <th>tci_harmavoid_score</th>\n",
       "      <th>tci_noveltyseek_score</th>\n",
       "      <th>tci_persistence_score</th>\n",
       "      <th>tci_rewarddep_score</th>\n",
       "      <th>tci_selfdirect_score</th>\n",
       "      <th>tci_selftranscend_score</th>\n",
       "      <th>tci_impulsivity_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>si</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>AA003</th>\n",
       "      <td>45</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>AA115</th>\n",
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       "      <td>6.9000</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>19.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
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       "<p>5 rows × 36 columns</p>\n",
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       "       age  sex  hs-CRP  shaps_score  psqi_score  ctq_denial_score  \\\n",
       "si                                                                   \n",
       "AA003   45    1  0.4000         25.0         5.0              12.0   \n",
       "AA101   26    1  3.0436         15.0         5.0              15.0   \n",
       "AA115   44    1  6.9000         16.0         5.0              10.0   \n",
       "AA296   27    0  0.6000         14.0         5.0              12.0   \n",
       "AA328   51    0  1.6000         24.0        12.0              15.0   \n",
       "\n",
       "       ctq_emoabuse_score  ctq_emoneglect_score  ctq_physabuse_score  \\\n",
       "si                                                                     \n",
       "AA003                 5.0                   6.0                  7.0   \n",
       "AA101                 5.0                   5.0                  9.0   \n",
       "AA115                 5.0                   5.0                  5.0   \n",
       "AA296                 5.0                   6.0                  5.0   \n",
       "AA328                 5.0                   5.0                  5.0   \n",
       "\n",
       "       ctq_physneglect_score  ...  qids_psychomotor_score  qids_score  \\\n",
       "si                            ...                                       \n",
       "AA003                    5.0  ...                     0.0         0.0   \n",
       "AA101                    5.0  ...                     0.0         3.0   \n",
       "AA115                    5.0  ...                     0.0         0.0   \n",
       "AA296                    5.0  ...                     0.0         0.0   \n",
       "AA328                    5.0  ...                     0.0        11.0   \n",
       "\n",
       "       tci_coop_score  tci_harmavoid_score  tci_noveltyseek_score  \\\n",
       "si                                                                  \n",
       "AA003            37.0                 17.0                   22.0   \n",
       "AA101            38.0                  6.0                   10.0   \n",
       "AA115            42.0                 19.0                    7.0   \n",
       "AA296            36.0                  8.0                   14.0   \n",
       "AA328            39.0                 26.0                   14.0   \n",
       "\n",
       "       tci_persistence_score  tci_rewarddep_score  tci_selfdirect_score  \\\n",
       "si                                                                        \n",
       "AA003                    3.0                 17.0                  35.0   \n",
       "AA101                    7.0                 18.0                  36.0   \n",
       "AA115                    5.0                 19.0                  41.0   \n",
       "AA296                    3.0                 13.0                  40.0   \n",
       "AA328                    8.0                 18.0                  27.0   \n",
       "\n",
       "       tci_selftranscend_score  tci_impulsivity_score  \n",
       "si                                                     \n",
       "AA003                      7.0                    6.0  \n",
       "AA101                     12.0                    0.0  \n",
       "AA115                     14.0                    1.0  \n",
       "AA296                      5.0                    3.0  \n",
       "AA328                     10.0                    3.0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demog = pd.read_csv(\"Demographic_symptom.csv\")\n",
    "demog.rename(columns = {'Unnamed: 0':'si'}, inplace = True)\n",
    "Xd = demog\n",
    "yd = pd.get_dummies(demog[\"Diag\"])[\"MDD\"].tolist()\n",
    "Xd.index = Xd[\"si\"]\n",
    "del Xd[\"Diag\"]\n",
    "del Xd[\"batch\"]\n",
    "del Xd[\"PrimaryDiagnosis\"]\n",
    "del Xd[\"si\"]\n",
    "#One hot encoding feature \"sex\" 1 - Female and 0 - Male\n",
    "Xd[\"sex\"]=pd.get_dummies(Xd[\"sex\"])[\"Female\"]\n",
    "Xd = Xd.fillna(Xd.mean())\n",
    "Xd.head()"
   ]
  },
  {
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   "execution_count": 522,
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    {
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       "False"
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     "execution_count": 522,
     "metadata": {},
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   ],
   "source": [
    "Xd.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 1]"
      ]
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     "execution_count": 763,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yd[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gene Expression Dataset\n",
    "\n",
    "The pre-processed dataset contains the diagnosis in the last column. We remove the last column from the dataset and assign as targets. The resulting dataset has 157 rows and 5587 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered = pd.read_csv(\"filtered.csv\")\n",
    "filtered = filtered.transpose()\n",
    "filtered = filtered.rename(columns=filtered.iloc[0]).drop(filtered.index[0])\n",
    "X = X.iloc[: , :5587]\n",
    "y = filtered.iloc[:, -1]\n",
    "y = y.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.index = Xd.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 5587)"
      ]
     },
     "execution_count": 764,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
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       "      <td>4.64711</td>\n",
       "      <td>5.2622</td>\n",
       "      <td>6.29881</td>\n",
       "      <td>...</td>\n",
       "      <td>5.2875</td>\n",
       "      <td>5.65313</td>\n",
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       "      <td>6.70174</td>\n",
       "      <td>5.26176</td>\n",
       "      <td>6.34637</td>\n",
       "      <td>5.05789</td>\n",
       "      <td>4.46604</td>\n",
       "      <td>5.16713</td>\n",
       "      <td>6.40649</td>\n",
       "      <td>...</td>\n",
       "      <td>5.51464</td>\n",
       "      <td>5.49731</td>\n",
       "      <td>4.81168</td>\n",
       "      <td>4.38234</td>\n",
       "      <td>7.45239</td>\n",
       "      <td>4.44773</td>\n",
       "      <td>6.30947</td>\n",
       "      <td>5.16134</td>\n",
       "      <td>8.79826</td>\n",
       "      <td>7.98613</td>\n",
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       "      <th>AA115</th>\n",
       "      <td>5.3293</td>\n",
       "      <td>5.43748</td>\n",
       "      <td>7.26366</td>\n",
       "      <td>6.61001</td>\n",
       "      <td>5.22449</td>\n",
       "      <td>6.06607</td>\n",
       "      <td>4.66614</td>\n",
       "      <td>4.79144</td>\n",
       "      <td>5.22848</td>\n",
       "      <td>6.09649</td>\n",
       "      <td>...</td>\n",
       "      <td>5.13445</td>\n",
       "      <td>5.7745</td>\n",
       "      <td>4.7364</td>\n",
       "      <td>4.58146</td>\n",
       "      <td>7.25967</td>\n",
       "      <td>4.3542</td>\n",
       "      <td>6.3964</td>\n",
       "      <td>5.59576</td>\n",
       "      <td>9.12823</td>\n",
       "      <td>7.79678</td>\n",
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       "    <tr>\n",
       "      <th>AA296</th>\n",
       "      <td>5.30012</td>\n",
       "      <td>5.69415</td>\n",
       "      <td>7.32919</td>\n",
       "      <td>6.89105</td>\n",
       "      <td>5.27701</td>\n",
       "      <td>5.93528</td>\n",
       "      <td>4.66073</td>\n",
       "      <td>4.55113</td>\n",
       "      <td>5.16668</td>\n",
       "      <td>6.42389</td>\n",
       "      <td>...</td>\n",
       "      <td>5.2329</td>\n",
       "      <td>5.43972</td>\n",
       "      <td>4.73382</td>\n",
       "      <td>4.57008</td>\n",
       "      <td>7.28511</td>\n",
       "      <td>4.32801</td>\n",
       "      <td>6.40649</td>\n",
       "      <td>5.45817</td>\n",
       "      <td>8.70631</td>\n",
       "      <td>7.75873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA328</th>\n",
       "      <td>5.2591</td>\n",
       "      <td>5.57367</td>\n",
       "      <td>7.49888</td>\n",
       "      <td>6.61415</td>\n",
       "      <td>5.18453</td>\n",
       "      <td>5.66789</td>\n",
       "      <td>4.39394</td>\n",
       "      <td>4.49811</td>\n",
       "      <td>5.13999</td>\n",
       "      <td>6.3729</td>\n",
       "      <td>...</td>\n",
       "      <td>5.18135</td>\n",
       "      <td>5.71725</td>\n",
       "      <td>4.45784</td>\n",
       "      <td>4.51728</td>\n",
       "      <td>7.08301</td>\n",
       "      <td>4.14281</td>\n",
       "      <td>6.44268</td>\n",
       "      <td>5.79226</td>\n",
       "      <td>8.88766</td>\n",
       "      <td>7.61559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5587 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          AACS     AAK1    AAMDC    AANAT     AARD    AARS2   AARSD1 AASDHPPT  \\\n",
       "AA003  5.64288  5.49633   7.4418  6.75926  5.26923  5.99327   4.7652  4.64711   \n",
       "AA101  5.64591  5.38718  7.77937  6.70174  5.26176  6.34637  5.05789  4.46604   \n",
       "AA115   5.3293  5.43748  7.26366  6.61001  5.22449  6.06607  4.66614  4.79144   \n",
       "AA296  5.30012  5.69415  7.32919  6.89105  5.27701  5.93528  4.66073  4.55113   \n",
       "AA328   5.2591  5.57367  7.49888  6.61415  5.18453  5.66789  4.39394  4.49811   \n",
       "\n",
       "          AASS     AATK  ...  ZSCAN26  ZSCAN30   ZSCAN4   ZSWIM8 ZSWIM8-AS1  \\\n",
       "AA003   5.2622  6.29881  ...   5.2875  5.65313  4.80185  4.63581     7.3341   \n",
       "AA101  5.16713  6.40649  ...  5.51464  5.49731  4.81168  4.38234    7.45239   \n",
       "AA115  5.22848  6.09649  ...  5.13445   5.7745   4.7364  4.58146    7.25967   \n",
       "AA296  5.16668  6.42389  ...   5.2329  5.43972  4.73382  4.57008    7.28511   \n",
       "AA328  5.13999   6.3729  ...  5.18135  5.71725  4.45784  4.51728    7.08301   \n",
       "\n",
       "        ZWILCH   ZYG11A      ZYX    ZZEF1     ZZZ3  \n",
       "AA003  4.36214  6.47859   5.2879  8.81543  7.71738  \n",
       "AA101  4.44773  6.30947  5.16134  8.79826  7.98613  \n",
       "AA115   4.3542   6.3964  5.59576  9.12823  7.79678  \n",
       "AA296  4.32801  6.40649  5.45817  8.70631  7.75873  \n",
       "AA328  4.14281  6.44268  5.79226  8.88766  7.61559  \n",
       "\n",
       "[5 rows x 5587 columns]"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 5587)"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157"
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cf06d1af98>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BbDZLNkkvXopa9Ln0QiKRgGXLlrV87jDOWQCAcDhM/nar4LxYwmjqWcq9Ws6fPw8AALt27WrylVRHkBXDalgMnpjFiJdWRnNzc3WRg7yG7WuaAx3RZxhG3fcX3oveKl51TWMJhFIJALBq1Sro6upy9VIG3YIWRFplk/HbemZ2dpYsgC+88AJMT097Ekab0ecykUi0fO5wb28vqcaIYZ74t5PS7yeMplGhipVKhfktXuANXslkknyWSin3RigKmUwGzp07R64rm80G3lu5GFm+fDnk83kt8C8w1q1b5+n8elTK9xq2r6keP86Sbdu2kb8ty4Lt27fX49IIfLXlT3/603X9Pk1rEhilUqWISKsoN4uNVtlk/Laeob1opVJJb5I1IhqNQnd3N1EsaZwqOPOCk4ogFQSPFG/woiMwVEq5NyJkK5FIwI0bN5jXgu6t9EuQvX2XLl2yhZpr7NQi7aGRXLt2zdP59ejr3cph+wsVP86S559/njn+2c9+VuvLYkgmk8STHo1GA199WeOPwCiVKht8qyg3i41W2WR4RYXPKZBx+PBh5vjll1+u2TUtdnp7e2Hjxo2kOjDiVMH50UcfZY537txZl2trJDKDVz3auFQL74ltZA/WZiDbW6pV4ptV6Mc0TaJ81EOpWEhMTk4yaQ+tjtd2UvXo693KYfuqBMlg5NdZ0uicSlH15SDdZ01tCIxSqbLBN0K5adVWB82kVTYZ3iMm8pCJ4BUe/ljjn6mpKejr67PlnGK5e5GnYGBggAhO69atg9tuu61xF1wj+HAlmcGr1m1cqqXWAkAQhIp6eYCb5T2nlYh6KBULBdM0YWZmBgAgUJFNshYiMpyiQvyQTCaZFmKtZBBTJUiFevw6S3jjoN+0DS/w1ZcbcZ+DsMcsJgIjPasMnEYoN7t27YJcLgff/va3a/7ZQaVVNpn777+fOeZb0cjge3+JeoG1Ev39/S1fpAfJ5/Nw/PhxuH79OvN6LBaDVCoFZ8+eFXoK1qxZA6FQqOW8lKo5lXy4Uqt4890QCQCqxhnZ5wVFeOMJqrDiN2JjsTE8PMzkfAfBW1ksFj0XQHGKCvFDPB6Hz3zmM2AYhlLYvqY6grJ3ADSn+nKQ95iFSGCUSpWBU2/lZmxsDC5fvgwA8zkr2ls5T6tsMjt27GCev2rrGb8ezmYxODhIivTUMsSuHkJ0pVIh3gCa119/HUZHR0l7jkOHDjGegs7OTti4caOyl7JRCkAkEiGGKxmicKVW8ear0NbWxhyvXr26Yd/dSopcUIWVoK1nzeLIkSPEQNTqwjoyOTnp+T2rVq2q+XV47Qeq8Y/fvYNft/v6+mp+bRoNT2CUSpWE+norN3zBCu2tvEkrbDLxeJyU0XZrPUPz0Y9+lDm+6667lN5HK3SN9B7SoZW1DLGrhxBdLBZtlnLcGGkBqdoCSY1SAGKxmGtOkyhcqVW8+SrwnhDTNBv23c1Q5FpJka0FH//4x5ljDDVvFEFp6xUkQw8iMtC5UY/5q/uBNg6/e8fjjz/OHD/xxBM1vzaNhicwSuXFixchm83C/v37Hc+rp3KDXkrk0qVLNf+OoNIqm8yZM2egvb1d2UsJAHDLLbc4HsugFTrae1hvgtQ2RxSqhRsjLSBZlmUrmOSFVlIMROFKtTB4NapS5bJly4h3yzAMW6n4IKEyLmSKbNAqgyK80YP3PNeboKxPQTL0IEuXLvX8nlrMXz4ipllFqBYjfveOgYEBso739fUFsjaBJngEQqk0TZPkZB09etTVW9kKyk0zCIqFuJ5cvXoVwuGwp+f/xhtvMMevv/56rS+rZvChlcVisdmX5AgdKoobHG6MvLC7YsUK8rdXJbFZoYqiOSfzgFRr8JqdnW1IpUq6v6hhGIEQtmVUMy6C2pajmeuZaZrw4osvBqKtV6ukbdSTWrV24CNigtDCaSHhd+9AA5P2UmoaRSCUSt476eatrAUiSxyfm1CPXIVqCIqFuNXAkFmklcOg+NDKK1euNPmK5GQyGbAsiygokUgENmzYQDZGXiGmf0sQ8tlM04TBwUE4ceIEo3jIPCDVGLxM04SLFy8CQH0rVRaLRTh37lzNi3sEjSC35di0aRPpvRsOhxu6ng0PDxMvfbFYbPm9qBXSNlQxTRPeffddT+/B1g6aYPPcc8/BwMAAWJblyXEQDoehq6trQXspmx2lpL32LIFQKo8dO8YcHz16tO7fKbLEffOb32SOn3zyybpfhyp+exlpALZt28Ycb9++vUlX4g4fWuknx6ZRJBIJaG9vJx7JrVu3wg9+8AMi5CxbtoycW02YpWmacObMGThz5kxDx/3Q0JBU8ViyZInNA1LN5vPYY48RJbxcLtdNWJ+dnYV8Pk+USsMwWl4xqAdBbsuRTCYZpbKRCtPIyAgZO9WGtDeCVo5s4oXl4eFhT8aejo4OpWdfbYSTFqrrD8qjQ0NDNiOmG4VCYUE/n2YboLXXniUQSmU98bKgDgwMkL9XrVrVUtYfv72MNAAHDx5kwv0OHDjQdOuXDD60sru7u8lX5E4oFIKuri6bgEOHWVYTpjU8PAz5fB7y+XzDxr1pmky1yEqlQiIohoeHYXp6Grq7u5nfVM3mMz4+TgTKcrlcl0qVtDcUqdd3tRKi3Mlf/OIXzDm17vVXT5oZ1kmHsIuONerwwrLX1jDt7e1Kz77aCKcgCtWrV6+GixcvBsr4XiwWyRhQjZ4oFApQKpWa9nxaVY7S1I9AKJWikvaqi4Gb0uh1QUULcCt5KQGa28vIq6Wy1XI/jxw5wljXR0ZGmm79ksGHVoqEtlazHIdCIejv77cJONFoFLq7u4XCr+pmhDlcCN+apF6IvAZHjx4lEQMAADdu3KjZ9916663McT0qetKGKSQoVTGrQZQ7yT/bVgwHTqVS8NBDDwkLhDUrrJMPx2/l8PygwadpuKESxbJYI5zC4TBMT08Hyvh+6dIlxnGg4q2MxWJEXmgGrSpHaepHIJRKvjRyOBxWXgyclEY/C2p7e3tLxqg3szy6V0tlq+V+Bqm0PO+FiEajtnP8Wo6bYVXs7e0VCr+qmxGdwwVQ/zyucrkM2WxWGtb37LPPko0/CDllNLRhCqm2KqbXMdXoMSjLnfzUpz7FnLdp06aWM9YAAOTzeWGeXbPCOum1M+iVg1sNP/PQTabxE+Fkmiak0+mWLxInoxaKdDPWAlynEJXoiY6ODgiFQpDJZFpu7dIsTAKhVPIhfqohWW6Lh58FtVAokIbtrUQymSS/pZXLo9fbMprL5SCXy3l6j9/S8s0K7aC9ELW8huXLl0M+n2+YtTqTycDU1FRVwi/tZUa85nF5ERBmZ2cBAKTW31deeYUoZuj1dvredDqt9Pyw+jVSj4qetHGFr9TrF6+W6kZbtmW5kzt27GDWhB07drRUmN/Y2Bi8+OKLdb1ffgRneu2MRCItuw8FkXg8zlTTVsFNpvET4TQ8PAzZbDaQXuhUKgWPPfYYlMtlAPCfn94Ka4FK9EQikYBYLAb5fL7p16tZHARCqeQnvWEYSt4kN6XRz4Kq0vy8GcTjcVL4pJXLo9c797OzsxM6Ozs9vcdvDlKzQjtoL0Qtr2FycrIhLSuQWlz73XffbXstHo8Lz5UJyaoCwtjYGPk7l8sJBTzLspjXndapdDoN2WzW9XsB2KJGoVDItzfdKfScNq7wlXqDipvRRZY7GY/HSbjh5s2bW2493bt3L+RyOTh//nzdvsOv4IzjX1cerT3r16/3dL6TTGOaJrS3tzPVud3WFTq8f3p6uiXDZZ3WuHQ6DePj44xSGZSccXoPAFALh85kMi3pBNEsXKpSKg3DeNYwjCuGYZykXusxDONlwzDO/OH/VVcS4RPULctSEnbclEY/YY/YK+473/mO6uUDQH3DJXAR7enpERZEaSXqnfvp15McpNLy9cA0TZKDU0sPst/nkcvl4OTJk3D27FnP77106ZLw9Wqty7t27WKORU3lV65cyeQlqownFcW6t7eX/F2pVKoqaiQLPaeNK3yl3qDiZrhwyp3csWMH3HnnnbBjx466XR+Naq752NgYjI+PA8D8/Mrn8424PCUwHF17KeuDKN3BCSeZBguK4Zh3itJB4wxtFLYsqyXD+93SaxqRn15LUqkUnD59momOwegJN9BTqdE0imo9lf8LALZyr30DAEYsy7odAEb+cFwVtPIHALBu3TolYcdNaVQJe+Q3erRweQ0/q2e4BC6iMzMzwoIojcZJgaata/XIX/TrSW7l0vKNgC48U0sPst/ncf78eahUKvDUU085nsc3eq8nly9fZo5FYdamadbku/g5xOcvffOb3/Ss+KuEnldrXKGvO5VKtXzlv5UrVzLHdO9hpzWhFsXG+M9QbRewd+9e5rie3kov0F4s3Cc1tWVyctLT+aJ5/NZbb8GWLVvg0KFDzOtOUTponOHzrkdGRlqqwudCLDyUTqchHA5DX18f8Va2YvREswhiJd+FTFVKpWVZRwGAf5L/JwD87z/8/b8B4MFqvgOAVf4AAHbu3On5fSKlUSXskbZ60eFvhULBlxel1tCL6PT0dEskz/MKNC08JZNJEm7TyrmfCxEnQZiOBqilBxk9KV6EjrGxMZibmwMAgImJCcd5JgoBohWDekMbuwzDgN7eXsZTKVPO3RQueg5hYQyaM2fOeFb8VULPqzWu0NedTqcb7kXzU4ma5urVq9JzaeG5FsXG6M8wTVO5XQB6KZFWCW8LgherHjSqmjkdTaJCOBwWzuNdu3aBZVmMcqjqWd60aRMji23evLmlKnwODw8TGUi2xjUiP10FP9Frq1atgq6uroZFTwSBIFbyXcjUI6ey17KsSwAAf/h/1Y2qUPnDv5cuXaq0iKsojU6W+WKxyFi9nn76aebf9+zZU8Wvqg38Ru43eb6ajdHNUklb4OPxOCm8VI/cTz/hltWGJrdaVUjZ83AShP14kFV+d6VSsbWpcGP37t3MsZO3UhZdUA94ZbWtrY0Jl4xGo7b5J1POvShcQ0NDwqIMXi3xzWw71Ci8RoTQYcWiY5pEIgHLly+Hr371q/Diiy+6ekOc1kXeo5JKpZTbBaxbt445bpXwtgMHDti8WIsBXFd37txZ131A1MbIK2+99ZZw3alUKkp7cTKZJNdgGEbLGYXpwm2yNW7ZsmUtUe3dT/RaNBptiWi0VqGenmlVua6VPPWtQNMK9RiG8TeGYbxpGMabU1NTrucnk0no6uqCFStWwP79++HEiROwb98+V0XILZzLyTI/OTnJbPQTExPMv/MW42bAh6N4sWTS4D31syk6WSpFFvje3t665X76CbestgVHK1SCoxE9D7fFl34Wqh5kL7/bzZJNGzUuXrzI/Bs/72ji8bjtederjcFXv/pV5rivrw/a29uJgLJlyxb44Ac/yJxDH/ObDxo/nDYkev7weA1TDlLrnEbhpa9iJpOBs2fPwujoKFlznZ6B07rIe42PHTvG/LtTo/tHH32UOV67dq303GrwKizxOcaLYXzR6+q5c+dgdHRU6X0qAit//53GhAhRCDKfF47wFfZl0Ebhnp6ellNuVIyjvb29vqq9q1Kt57oV+ni3wjWoUM/Cj6ryTSt56luBeiiVk4ZhrAIA+MP/hbu0ZVn/07Ksj1iW9ZHly5e7fmg8Hof+/n4AALIBHz9+3DUEyS2cS7a4ZzIZmJmZYSz7PLzFuBnw+aaqmwONaZrw2muvAcB8A/daLiRDQ0M2C/zU1BR0dHS03IbklWYuJm+99RZs3boV3nvvPaXz3RZfulx9o6oH00LTM888AydOnIBnnnmGhEcj/DHPmjVryN/RaNSzkKAqPPNhUtgfMBqNEiPJyZMnmXNOnDhB/vYzXuj5w+PV2+i3dU4jSKVS8NBDD8Hp06cb+r2bN28m40vUV5HeH5YvX04MAW7eEDd4rzHvgXIa8wMDA8SQEovFoKOjw/P3q+B1vNK9m1vRi1UP/EYKqQis/P1XqfbJw+/lIi+lYRiwYoVzQBm9RqI3H393K3lqVIyj0WjUV7V3GqffXG1ovCi3ulAo1O0eixTIVuslLmMxRN8EjXoolf8vAHzxD39/EQD+n1p+OO/FqNbtLVvcE4kEdHd3M5b9e++9lznn4Ycfbro1h89zV2FnAAAgAElEQVQ39RpqCDDvpUShxrKsmobw8A16jxw5UldlrKOjoyZCVittlCJ27doFlUoF3nnnHaXrVFl8o9FozZUNWvDgwXFgmiYcP34cAAB+9atfwUc/+lHmvE9+8pO299LC/rVr1yAWi5GqpV6FBHo8quadAsxHBRQKBSgWiyQkiW8R4tQyBBUDp7ng1ODaq7fRb+ucRpHP54XGu3qSTCaZNZ4e++iFQu+TqKqwX48v3wqHD63+1Kc+5fj+NWvWQCgUqpuX0g/RaJTcy1b0YtWDWkUKqeBnXVZRCnp6elyrytJrJBrR0JjXSp4akXFU5DhwimDDPSCdTkv3Atlv9hKOWSwWIZ1OM+fIcqtxr/AqkxSLRcjn845h07wCGaRiR5s2bWKMgoshOqLVqbalyE8A4JcA8AHDMCYMw9gBAN8BgE8bhnEGAD79h+OawSdZA8xPHNHiWW2uGx8m8fDDD5N/u+WWW+DYsWNNt+bQgmI8Hme8NqqglxI5evRoVddEK2Qir1M9FbZabXCttFHy8HkxdIl00zTh1KlTcOHCBeY9tJW7mj6H9eCZZ55hjvliU/S8Q2hjUD6fh1AoVJOWME4VOPnQqu7ublu4Nd8j1WvPVB4nj5UfA0Crts4ZHByE/v7+hucHxuNx2LJli9AgwTd5v3btmu391bR3obn99tuZAmZuhTg6Ozth48aNdfNS+gWVEz/GzSBSi0ghVWT9d534z//8T+aY90iqeCmDRltbG7M2ihwH8XgcBgYG4Kc//ant/ahkfec73/Es33kJxxT1hRZFdiH5fN5T4bNUKgUXL16ESqVCCt/xmKZpyw+vdy/xWrJt2zbGIbJ9+/aafbZI6de4U231189blrXKsqyoZVl9lmUNWZZlWpa12bKs2//w/5o9EZkiYlmWzfOSSqXg2LFjVeW6icIkcON/4oknmmbNEbU56enp8b05OPVqU4FXEmmF7P7772fO/dSnPtU0hQ1D7AYHBxvyffXKS+DzYs6dO0f+xj5xtPcawG7lFgnCc3NzUKlUauqpxutwet7opUT+67/+i3gn7733XlePB3oLq20JY5omHD58GAAADh8+LMw7pQV/0Xzr6upijpcsWeL6vU4GFn7+0PjxNi701jl+DFYiRZtv8i7bR9ra2uArX/mK5zWFb4Xz5ptvwtKlSwFAvV0A5nO3UkRFKBRivFgLHTpSqBUVNN5I84//+I/M8cDAgOfel060QtE6wzCU0mtEyibtpRsfH/cs36mGY8r6Qv/iF79gzpNFqqjc59HRUZIeUyqVbIZmgJvyAsBN50yQQkoPHjzIeCoPHDhQs88WKf0ad5pWqMcPiURC2HAcwF4UAKsrZjKZqha5bdu2QUdHB7GAGIYBoVAIjh071jRrDh+ucPHiRVi9erXvzWH16tXMcV9fn6f3o8KAhZNoT9OOHTs8WeDrTT6fJ7lwiJsg6nejrFdeAm+txHHIC8L0RhiPx8kmu337dtuGa5omKexQy7xap/BXALtXEnn44YfhzjvvFHopefz2wuShrcKWZdm8lfF4nKw/W7ZsgampKVulYb7omCzHKpPJQLlctimhPKL50t3dXbdCVzTVCIjYBsWrgaqjo4PkzvvBj8FKpGjzuXKyAicdHR3CNcUN2usdDodh8+bNntoFZDIZuHbtmmN4dbV4VdAzmQzk8/mWaXHiRK0MfnSkkEoYaaN5//vfzxwPDAwQz2o0GvXl6c5kMtJn3GpF67xCz3vEi3ynGhEk6wutauBXuc+Tk5PM+3lDM8B8iC3t6Tt8+HBNC7r19/dXtZ67QVf7FTmX/CJT+jXuBEqpBBBXunMqCpDP510nX7FYlG4wBw8ehHw+Tywg2CKhWdacesS7P/7448zxE0884en9xWIRrl69CkeOHIGTJ08yeX7YAgbgpgX++vXr8Pbbb8NvfvObqq/dCxhixwudbmElfjZKr8+pFkKOW584p6q7+/fvJ3/XKq+2v78f2tvbhZsZImrm7aZo1Qs+Z1I0p2+77TZyDxOJBMRiMaaIAu+ZlHkqE4kE3HHHHcLxCHBToI/H46ThNXqBVq5c6busvBdFcXR0FF566SVfY3JoaAiy2aw07EpGIpFoWCSBE3yunCyfcs2aNdJn6AQ9B8PhMCSTSU/tAnDs4d/1wI+C7qeFUC1RWUfHxsbgC1/4gmeDn+yz0dPdal5KABAquevXr4dQKATr16/39Zn02Ks3tfZ8uhlK+HkP4E2+U4kIwu8RfT6fS+2nOBPCh+uL8n35MbtixYqaFnQbHBys63per4rmtNJfKpW0t9IDgVMqRYtktUUBJicnhRtMsVi0xZuHQiEIhUKwadMmCIfDAHDT0uxEtXmEuKE9++yzUg+p3+8YGBggf/f19TFV/FRAxaBQKIBlWVAoFICu6Mtb4M+fPw8AAP/0T//k+VqDgte8BC9eTX4jwA2A3xD5jdBJaPWTV+s23gYHB+GOO+6Ajo4OIpzSQkKxWBRudN/61rc83Q/V3qSy68XXRRZqHtE9jMVi5Pfxnlf62Mv8pAX6VatWQSgUgvb2dujv76/KG+LFQOI3/KdYLBKhqVQqSb3RNKlUqi6hnLlcDk6ePAlnz5719D4+V06ELGpGhXg8TiI4Vq1ataDCkZupVKqsG7t374ZyuezZMCvLt37uuec8h5H63audDHQiRHOvVCpBpVIRthxpNWrt+XQzlIiUOJGyInt+bhFBou+hP5+OUsDILtozPDs7C6dOnVJaU+laC6JjAIDLly/bjlu9oBtNvSqa00p/pVJp6RDgViNwSqXIs1HNoEfBlt9gsCcZH2+O0E2ALcuCUqnkaFGrNo8QN8tXXnlF6iH18x0o5Le3twOAdy8lHSaA8KXVaUH8rbfeIkLHjRs34Nvf/ran73PDKbm6XoKraIPx4sn26tXk82Isy4Lp6WnYtGkTI3SINsJ0Oi0cp37yalWrptLQQgIfngMw76Vcu3ZtXfKVZfPDy7zhxxdfbZgv1EGvTX7XAAxTm5uba1j+HD2vDxw4APv27VN6H44xWrHgBRcR6XS6LqGcWKjC6zrDV9UW4dbqxklxME2TjP1MJuN5jDuFINaKagpVNKPAhco6OjY2xlSQn5ubExblEn22qCongD/Fx+9a4FVhF809NOrSufheaMTYq4ZcLge5XM71PNF+SOfNIyJlxen5qfThlrU+oSNTMLKLjogpl8tQKpWU1lQe0XrFG0LwuJqCbo3scVkvBVil36lGTOCUSlEFvqtXr/r+PFqwpT1KiUQCisWiLd4cw3v46zh+/LitgXWtoDfLSqVSU3c/boilUgkikYhnLyUdJkAjK62+e/du5viXv/yl0veoLlSzs7NS70q9BFfRBuNlUfLq1aQ9ywA3Q12TyST5HFlIeDabFQpA1ebV+skfFc1l9FJ6uR+1yqlUuQe8945/9vxmLwqbpHHzpNG5ibRH1Ctew8j4nJ9f//rXSu9LJBK2kFcvbRZqGco5NjZGwtovXbrkyVsZj8dJPz4nUPESeQ6cBE96TItC1d2gP7dehoZqClXUusCFyvhVWTf27t1re40PexfhVJWzlZmZmWH2TNqoW6lU4Ny5c569pnz4a73z5rzS2dmpXHWb3w/pdB06f96LsqISxu7UF1qUW817lmdmZly9lXynBFHe940bN4TH1RR0a3SPy3pUNFfpdwogr7a/mAmUUpnJZIQhJjKhRSUsjn4v71Hiv4sOO9y9ezfjdr9+/bpyuWevlhx6szQMg/yNgz2fz8PJkyc9lZvm8SuYyzbkSqUiFOL4RUzV8qqyUJmmSazQIku1X+FLtuk6baZ8pVCnBc9rfq5pmrbXRkZGmI3KKSRc9Fuqyauly5IfOnRIeVyjRRaJx+PwoQ99yHY/Dhw40BCrp9s94JP3L1y4AJlMBk6fPg2/+93v4POf/7zS96RSKZiYmIAzZ87A2NgYVCoV2LNnj/BcbGtRKBQgn88LBQmV9QSNR07eahp+DKoqhiphWSJwTNZSQeKL63j1VmLlRBG4VqLipdr0HpHlVLUKfgtV0N6QWkYZ0N5AmYKpso6Oj4/bXsNIDydUq3Kq0sg+yLQCzBt1b9y44ctrSueR1ztvrt7InkNHR0ddC6LJ+kJPTU3ZqtfSDg7EzVvJy6+icP1169Y5HnulGT0u613R3MmoIKu2v5hp6TvBC0uJRMJTqXKVhrF0uBrvUeIt7nTo7cTEBLOBeWna7dWSQ2+W5XIZDMNg3P3j4+NQqVSEG6Yq6OHzugjwTbxpRIIyX7hEZTKqLlT16q8k23SdNtN4PE7Glpul02uoheh34Xtw4/noRz/q+Bk8AwMD5Fmo5tWiYESXJXdKaufDj3t7e5ly4Gi04fPZ3DwDqjmVbgwMDJCNV3QPeO9dKBSCRCJBwpJEyr6IdDoNMzMzjBFofHzcZoQpFoukmm+5XIZKpSJUXryuJzJvNQ2ft6vaf0+UnhCLxRoWCkkL6169xjyinsjI2rVroVKpEMWLr7bsRi3Cq+pZqIce6+VyWTq26D06kUiQOgMA9auKLgs3VWmELhKaQ6GQ63WqpAf4zZlWRXV94aENGH6NuvQ1pNNpaGtra3hbsEa0KzFNk0TQXL9+HdauXVs3ZSUUCglbn/Bjo1gsCuVLN0Pfbbfdxhh5+ErAAACPPvooc7xz504A8B/CGpQel25jiU91E+FUbX8x09JKpUhYEj04kcCDcf9uIWO0YMtbjbq7u5lNStZ8OBKJuBZ1QPxYcuhcuUgkAps2bSLu/rGxMaL8zs3NefZWFotFOHXqFAmtwHtdi7h4kaDMe4NUKtCpLlRulmrZOCgUCjA7O2v7vRMTE/D5z3/et/dFJbcCwJtXE0DsHU4mk5BKpaBcLkMoFIK///u/l75fdh9QoVL1UuLmJypLDmBvK8GHH2/YsAHWr18PhmFALBYj7ThE+Wx8HhN/3ZVKpSaLOoZNie4B713CTR3zkUXce++9ttdEVmcA1giDOd280GeaJpw/f54Ir34tw07Cr2mazL976b8nCmmem5tzFS5qJaDWsgcub+nHNT4Wi8HMzAzMzs4y496LAKUaXtUs6LFeLpelnlR+j6aFXy8e2FrsNyqN0HkhWvU6Vapy4tgbGxuDBx980HNxKDe++93v+nofHTbpx6hLg5ETsqrOqpEQXsF1bnR01PVcr0ZGer3gw9K9RiBUSyqVgtOnTzPrr8hQp0I0GiWycSQSEUb5DQwMENlj3bp1xJD6zDPPwIkTJ+D73/++0nfh/B0ZGQlEj0unPGhaWXRi//79ZB74SWFYqLSsUikSlmTVIkWolr2ORqPS2Hk6pyYSiUjDDUKhkHIlQD+WHLooEFYEQ3c/H87i1Vs5OTnJCAJ4r928H7iIvP76646fz3srP/zhD5ONLRQKweTkpKsgoRoe6lZeur+/X9iuIhaLgWEYtt97/fp1ME2TaZHiBdUWAbRXc/Xq1a7n8wLNunXroKenR6lYRFdXlzRkNxwOQ1dXl+e8WlFZ8lQqBY899hgjgPD3cHBwEJ5++mnYuHEjvP/97yebOybf0zh5KzHPeefOnVULM5FIBN73vvcJ7wF/3zH3ZnZ2lrxGe2oAQNhnUyYk0HMXc7pFFup8Pk/u1fDwMBEanTxKXuDzpNva2pQrW/IhzQCgpPDK5mY18HlVqnlWCC84Y+XvtWvXQiKRsBkGvAhQtIFSJWcrlUrBn//5n8NDDz1kG+P1CKPkI1Duuece2zn8Hl0sFhnjqhcPbC3ysFQaoQ8MDNjkApXrFFXllLF3717I5XLSkHa/1KIFF2/UVQ137O/vh9WrVxNhW1bVuVwuQzabVVL+vIDKrIqS5yWVh98PeYOtl3zwWpBOpyEcDjOKrshQB+AePYLrQldXl6N8ivfq4YcfhkceeQTS6TQcP34cAOZrXqgYenD+rlixgpHB7rnnnoYV7akVfK9S2ZrEV8yvlQJdb298vWlZpVKkfImqRWKvMBl07L/M5Y3933hrMW3p2bp1q7Ta35YtW2Bubg5yuRwMDg4KvwNDY1544QXPlhynUEq6kh2AXRBywjRN22Qvl8swNDQk9H7Q1mR6EXFifHzcFhb0+OOPk3uuUtBBtReRW3lpWZ/K2dlZKJVKNgMGPie+RUo96O3tJW0j3OB/F4asILKQpkQiAf39/cr5L6rhRvxGf+XKFRgdHSXVBZ3aSmA+BK+0iDw3ojwm0zSJVfrcuXNSYQa9pm5e5yVLlsDVq1eFApyoMiAAGwpHewVoAwH9/TIhgfciLFu2TBgBUSgUyO84cuQIo1SqbmxO3jy+NY1fTz2NmwFNNjergW+Z4LWFAi+IYesPFDT5Z+M1hBXXKhzrTuGT6OWnI1Ew58srtarOyO/RV65csTVbV/HA1ioPS7UROi8vqFynqCqniFwuR4xDokgdL1RTfZeG9kZ++MMfZl6/5ZZblD5jcHAQwuEws7eIFDysuvrf//3fNfPU0mkAKmGGqp5K0X7IGw5Vw/6d8Drf5ubm4O2334aDBw8CgLiNngqJRAL6+vpI5XAZWKTx2LFjcPLkSXjyySeZf3fzVtLzN5PJMFFXANDQoj21wK01G8B8ETheF6lVhdhattBpBi2rVIq8UyJhrFKpSIUeXFhQUBG5vDOZDBSLRalHiQ5hvP/++5l/i0aj5N/Q23X58mXhoMDQGL+WXPo63IR9LwWAeMrlMhw5ckToTcU+Xfv372cWESfWrVsHy5cvh3w+T65r/fr1cMcdd5D8DlqQEP021V5EKuWlRRs1rSjQBgzELQxGNZfG6blh2wiVDYT2csTjcZtXTRbS5Cak8JuxW6l8/LxPfOIT5DXDMOCBBx6wGYCuXLniSWGIx+M2rx/9eXQ+Jy1Iyp4TWrrfeecd27/RBZew3D5f5AWvCb2TAPMWZH7toef3P//zP5O/h4aGIJvNwuXLl6XPmPdKoqFBBP5OFY8SD22dF41JPqfVi2DV19cnVISbEQq1cuVKx2M3eMEUxzCOY/o5yqotO4FrLK5TbqG7sViMCMHFYhHy+TzxmnpB1m+R5o033mCORREp/B5tmiajuKtWzaxVHpZqnmpnZyd0dXVJq2/KEFXl5OGrQFbjraym+i4NPz7QM7VkyRJlL3cqlYJDhw4xaxTvxaMNfADeCmPJ9kY+DYAOM5Qpa9VUA+cNh6ph/0549cLjnoLKHJ8bCTBvcFStL+IWtReLxSAajRKZjt9D3Sr00/PXsixiGO/t7YVXX321oUV7agG//4nWEVEV6VZLYWgWLatUirxTotAqWfEKALXFxW3C0SGM/GZSKpVIInehUHBNei8Wi8yiW6lUlAciXRGMFvZ5wRtA7q7ncSqlziv0dJ+uo0ePMkKAEzt37oSLFy9CNpuF/fv3A8B8LPro6KiwlYtIkfHSi8itvLRooxblAfEGDKcwGNU8rlo2csZNRrTpyZ6Jm5DidTPG4k709USjUUgmk7b79+677zqGOIoUcyeFCe+5SrgSVqcFmFcU+M0NCy7R5faz2azQW0lb9kUbMK0E/vSnPwWAeasm5pnOzMxIvdGf/vSnmeNoNGoLA6Z/kx9467xoTPI5rV4FK5HS7GZAS6VScObMGTh16pSS8JFKpeChhx5y9LqLPOheoAULNJbQ0PfIqdpyPcC55/U37du3j4xFpzxluugNgF2wMk0T2tvbmT2afu5o/FPx0nitfi1DNTcd6y3QRmEVRFU5eXhDhCwdxa0NB10wplqBnG+5FolEoKurC9avX69skMCwTBre2MTvK17a+Mj2RlEaAI4PVWWN3lvov0VGVjoqTJaH6AadV+rkhRd5VLGuAMD8/nLw4EEmYg5gfmyvWrVK+N1+ihlhTQmvRZsQfv7euHEDQqEQvPfeew0r2qOyH6jC73+i9UE0rxu5/rcyLatUirxTsr5hjaq8FI/HmZAoWqikvSUy+FwqL5NYprjwBQQAgAgNbsgqt7a1tdkUerpPF8BNRYwPKYvFYiRPY/Xq1fDMM8+QKopHjx6FdDpti0VXESRUexE5lZfet28fGSf0Ai/yHt96663Mew3DUB5jKEh5CRvMZDKQz+eVLce0EieCv1Y61LkWLTro9i2vvvoq8eCh0s8bgD772c86hjiKxjev4PLHmUzGtiaIvGrDw8PkWTgl1D/11FPMschbyStz/PGWLVvI3ygk8XnPsmJaorGdTCaFijh6q3kPEj+3/EDntBqGAVNTUzAxMaEcwiXyrqqEGFYqFWn1YJGwlM/nhb3XkM2bNzM5drxS6Aa9lkciEcfr9yOQYY6mV+i5h6H6qtBeB6c8Zfq3irywQ0NDMD09zbS6ou9Xe3s79PT0KAn+qukNiMgANTg4CH/7t3+r1FsQ1xmnCCXZ+9yUMH6NkvX7dWvDMTw8TMYGCuQbN25Uuk4et36rKmG2vEEcwC5Ei4zU/JrqBz4NYPPmzZ5CpunnRv8tM7L+yZ/8CQCAYySWm1EAK2w7eeFFRly+QjV6K3t7e0nLMKeIJj+G67a2NrAsS9rBwC3Eno8Q6OnpgY6ODrh+/XpDi/a47QeqxONx0rMafw8Pn4usWlNlMdCySiX9YN28UyJBEa2RfhE1tTZNU9rYG4UXp9h1UfiuqvVGFmIpCsWRValVJZ/P23ph8n26ZBQKBfjCF74AnZ2d0N/fb8txe+KJJ4R5sW6CxHPPPQcDAwNVWYPoa8EFPpVKKeUBeSmCgoKUVy9CpVKxKRwyy6OoKii9mfDCIn/t1VoN+QIxhmEwVn++XYif0BA+BI9XmPL5vM3rJ/Kq8UaWl19+Wfh9dGVa0TGAuOoj0t7ebrNy0goA4iXvOR6PQ0dHB/MaXY1VVCRJhmhNk4G/o6OjgwhhXkK48B709PQoRRj8xV/8BZmHIiGRF5ZUcjCTySSjrHgdg7TB7O6774aenh6yDvP7iyhipF7w4WZe1hl+D5JFq9AeG94LS0et4HUkEgnmfs3NzSkL/qrpDQitGNCe0Hw+T8Ja3Vp8VRMiyUOPCX6uyhQPNw+uyHvrN8qFHx/FYhGy2SwZC2fPnmWUK9GeIyouxqesiNbGiYkJX9dM09vbyxgvksmko7LW0dFhew48tCf44MGDsGXLFhKZQo8d2friZBSg31OtFx7XxGg0Cj/5yU+Ux6xpmnDhwgW4cOGC63qP+xG9X9N861vfcnw/PV8rlQrZg2jDcjgc9p1zqBLt4LQfiMazW8oSyhW8cwHhq0hX299zIdGySiXA/IOlBVWnPmM4WXEAqVZ/BWCL+SCiptYiq253dzeYpsksHDiJ+cEsCt9VWWRM04RsNkv6SNITQqRAqvZj44V2GrxWFAadPLA8//7v/w5DQ0Pwq1/9yvZvorA9FUGiFqGj9MZIb9SiPCC+nxeAWhgSLUh59SIA2MOnVH835uwhfGgbLTxWKpWqrYZ8gZh3330X1q5dC7t374bp6WkmZEc1NDCTyTDzhfek41hPpVKeKl7yOX6q7X9EbNu2Tfpvs7Oz0NPTo1Q5ToRTjhsNXY2Vr0jtJMThmnbmzBmYnp52LF6EyqxhGKTqtp/cmN7eXqUIA695dSqFl+LxOGzZsgUMw4CtW7d6Mkjx69Tp06cB4KZCwwsvjezZxxeSqKZCpaz4HMDNnF7eUMFHrRiGYcuhLpVK8Oyzzyo9Uy/pDQiuFWjAC4VC0N/fD319fRCLxWyGHICbwumFCxd8GZxFgqhpmnD16lW4du0aJBIJWLt2LeO1kFXPdPPgiry3fvP7eA89KhHnz5+HYrFIjnFui/YckUEcxx2eL5rjTuNLlWg0SuQR3EuclDXR/CwWi0z0EO0JLpfLYFkW7Nq1C0zThGPHjpHX/RQpo0Nt+XZwXhUrv/dveHgYZmZmYGZmBq5cuQKVSkXojaZlV1qBxe/t6uqCD33oQ8rfa1kWnDt3DizLYjzklmUJx4dKuG41laFlrWjcog4wNF4WaUBXkY7FYq5GjMVESyuVdD4jbVkSgZOVXhArlQrMzs7ChQsXHAUh3kNEfxcqBrR1FkGPAT/YURHlF2c+DEV1kRkeHoZ8Pg/5fB6Gh4ddJ4TqQuTkdeF7LPICvpO7f3x8HL785S8rh4SJBIlqGh3LLFv8Rn3PPfdAOp0mYWj070UvOY2KsOvkRfDSHNsN3KgymYxwbPKhbSqNwb309+LHw9KlS2FycpJZ/HEM0cKQLGyoWCzC9PS0Yyn6TCZDhB6cr7wFXeS14Q0EN27cEK4Hd911F3P8sY99zHYOVuSTcfbsWXKf8/m8Y94yz8svvyy8Ln6uFwoFohjy4ZMy48/ExAT57FKpBENDQ8pl+mmFQTYHZPM1Go1Kw9FpvFr0Va9dNWxe9Pk0ovww2mhZj7YePIVCAdLpNPT29toKKalWmOQNm6L0CSQajUIoFGLyvADsVZgtyxKGnb/yyivKz9Trc8rn8zA6OurJgEcroLFYTGhMdkK074rmAh09IZovKh5ckffWb89E+p7yz+7ChQuucxtAbBDnjXPxeNwWlvnJT37S1zXz8AYOr8oa7k14D3nDDMB8ZMrTTz/NvFbNvC6Xy0yVUN54Ltpv+VzJv/u7vxN+tpMsUSwWSQ0BgHkZFlu9qEQsbdmyhXje3LyUAKwx1LIsyOfzytE4bkZz0VzxIht6aUXjFSyUpBVKlpZWKmlEVnxaeRJtRnNzc1Aul2FmZsZRGahUKszkpq1YlmXB6dOnbdZZgHmrWTQatYXXyTb2aDTKhEmpeOj27dvH9NvCfmBOOAkKNLLvvu+++1x7LMZiMem/LVmyBKanp4UhSKIwMdF1+PVMmqYJX/7yl4WWLbqQA/4/m82SPof07xUtQirCrpMXwU9Ojgx6LIrGJl4LotIYvJqQMFTU6J51WGhmw4YN5Dw6bIgWhCcnJ0nRLVkPVJHQo1JQSVQ2X7QefO1rX3M8VmmK/NRTT5H7fPbsWejt7fUUGklfF29dp0EDEx8urcpjKWkAACAASURBVGrIOXz4MBE83HLS6XssmwMqwoGT0uMlr45+Dvy18/lhTjnWToiMAXwlTzpFwA+47iBOgmImkyGCYXt7O/lO2rCpYs2nhVa3fosA82sCv2aJxpiohzN9ntsz9fOccM0AcA8D5iNI+MrwfqDHYalUIlECtBGrVCrB4cOHmbGv4pUXeW/9hg/S9/R73/se82/8tXqJYBEpDnwYoKhPrxdwTvAGDr53Ny9D4NpJ9zenjQ+yehInTpxgjv3k6OGYsiwLRkdHiaLNG89F+y0thxqGIY2McZIlJicnmT3DsiybNxoRrXPJZBJuvfVW+OAHP6jkpRSlRpVKJSZiLhQK+fI0iuaKqmzotE+cPn0aTpw4IQ1hzmQykMvlSMEl+jNxXGEV6Vq03OK/O8i0tFJJb7KigYvFQeLxuG0zKhaLTFijijKG8IpBuVwWfj9azXih0clTSP+bU6gPWmOOHz/OeB+KxSK88847zMDjFVU3QQGR5V5+/vOft73Gh8q+99578KMf/Uj4fuxVJeIb3/gGcxwOh5l7IMv7UvXyYQEJkRU4Ho+ThXjFihW2DZT+/I985CO2z1axiFbb58prL718Pi/s3whwc6ylUin43ve+J20MjgullxBnfjzQz4uuyOyUe4KC8NDQEBMZ8Oyzz0p7oOIzQysvb0EX3W9R+W8/FRX5psiieU6Hn+Kz9KJU0tfFW9dF56rCK990YQZZ8SK0OvttgwRwc07t378fTpw4IbUwe8mr46MB6GuvVRsGkdDJV/zDMR8KhTwpJ7i284V6nATFRCIBnZ2dAHCzKrBhGGTtVC1aQnsdnfotOiHr1cq/Th+r9qyUwVfwLBQKcO3aNWafNk0Tfve73wn3H37MALAGPBVPby6Xg5MnTxKPNb8e4PfS3rpIJAIrVqxgFH5VrzzvvfVz/3iDh1OuqVPemywMkKezs5N857333utrfNHPgp4TtIHDqXc3wM21c+fOnXD27FmltlPVIBo/uObOzc1BR0eH5+eHBYO84hTRxxsxeHlFJEs7YZqm1JDpZIxU9TbSbWz4z3Cbs077xJIlS8AwDKnRIJFIQDgcJgWX6M+sd9/NRqZS1IOWViox5BNAHNaVy+Vs4XUIn1coUsZo6InBl1OPRCK27w+Hw2Tz4At6OHkLcCNzKxyB1hiRMDg3N8cMPL70u+qiIGtLQCscou9AsBoZj+z39/X1wX333UdCZw3DsBVawaIB/MKv4uXjw0D5BdQ0TfL8L126ZLtO+vPfeust2+erCEZ8nytVT0QikYCuri644447HD+fvhZEZsRAj3U6nYbx8XFmY33hhRfIebhQeikgw3uWaEqlkmueF+05GBkZsTUt5xsp4/eg0INWXjqknC5gQzMwMGC7RyIPAX0ciURs/84bm1SU8EwmI20LIgKva/Xq1XDt2jViXZedy4feyMLSReFr9PXzgm0qlSIFu+jqf6rKAf3Z+/btI3lKWAGaFgZSqRQ899xzynl1/HPAwku1bMMgoq+vjxi9crkcI+zIKvqK8BuFgV6MTCZDFI4VK1bA5OQkuRa6AJlIaKP3ExXjoyhEVJY2wc8xLz0r3QREvoJnLBazVQXF7xTNS37MlEol5repCIsXLlyASqVCPNb8Z+L6SfcNLpVKkMlkGIVf1SvPe2/9FODzUpVYlvcmQ+ShxzW7q6vLt5dSVXDnU3SQXC5HDMvnzp2zhZjOzMw41pOgUY1CoK9ZtMdjNWQnTNNkCj/6LTQpKy4DYFfM+Ogtr3m7w8PDSulW/DhXXQPp38J/hts44ecntsZ75JFHYPny5UwROtF6yXuR+VBcVSeVamrCQqGllUoaUUjn3NwcrF27VlheWUUZAwDhwKBDBQHmLY/89zvlIzqBC0WpVHK0KNHfTWMYhs0bg4Pfi5cSQF5ERDRh7r//ftt3plIppQUFF+YnnngCAADWrl3LvI7Q1XWnp6chl8uRyZjJZOD06dOO1i3ecswvoMPDw0z+iBMia7dKEQnaghqPx5kGxar9LFXA68/lcrZnA2AfC7wAhl5+eqEslUrK3kres0QrLZFIhNyDPXv2wLZt22z5aHxYC/29tHWRvl/YcoImGo0SD05XV5e01Dq/QYg8BLRBQvTvmzZtYryOKqHChULBk6CG3xsOh13bFImel6wKHZ/Pzc9bXrCl+8heu3aN/G63OdDf3w/lchnC4TB0dXVBIpEgCiX+ll27djHCAF3oQyWvjjf64TgUtWHwCx96jb8NPaF8k/vz588Lz3dqO8CHvzqB4a8IKhzRaBSuXbtGPocuQDY6OsoIM3RRDgD3vmooHPNrlmyP4ccUrgMqvSC9egBwH1UV+vn9OhKJEM+XSo7j2NgY+c7x8XES2k6Da140GmXWWl7h91rttlpUhVmnsShSlCqVCqRSKTh9+jT592w2C+VymfTv9ooXwT0ajUJHRwfpB4zQc1O0dkajUWn4K8+tt97qKHOkUinYt28fc82i+6hSTIuWT2iPKhqy9u3bB/l83lXZdHqOvGLGV3n22peTLtjnhN9xTleRpz+jWCy6zlk67xZgfo+TVeZXUXJ5mQU/A9MSnN5Xb+9mKxEYpVK2kXkJZRCFxmGhj0qlQjbggwcPMhvk7Oys7ft37NhBPE98MjoK7DS4MNCL3Le//W3XMAC+R1A0GmWsSehRwH/zspDLiojQEwbhf384HFZqTxCJREgVX7TgYiw6v+Dzi+r58+fhxIkTMDQ0RMIRnCa+6PfQC6goOd8LskWRzuMyTRPy+Tx0dHT4rtZHQzdSFhEOh4Vzg/dYY6EDANYiKQrhwgXaybPK5/zQizddUOHYsWNQKpXgySefZN6v8ixKpRLjTe/u7hZWdUQDwI0bN4TXm0qlbN8l8hDw/bb4f6fzeADmBVs3o0ooFILnnnvO8RyAmwI5fi/tvZURiUTg05/+NCMIyISXqakpxosZiUQcW77QBZDwOjo6OkiBIB4cK4ODg3DHHXcwCjdfKOnSpUvS8HS3vDrTNBmFF2D++Z89e7bq8v00orn761//mtxfXrArFAq2++LWixAVDz4nVGTVXr58OfObZVXF6XHLF87av38/85luygYqk/ycisfjtpBuLHxGEwqFYOXKla69IL30HESwDgKuQXzECw+fm2ZZFliWBel0WqlKLR9Cv2fPHtt34vPJZDLMmoqv45j0U+22Guh6FKICdDQywTeRSNjWulgsBul0GsLhMBkrqGCIcmxVkAnuIjKZDFy7do2RCUzTdFW6vETk4N4uI51Ow/Hjx5lr9luxlZdfcK2ZnZ2FbDYLv/71r5WMUNgXnIZWzLZt28asMfjM6HGomm7EG1plrFq1ytc4xyry/Fyh86llc5berzE/9cUXXwTLsoichkU4eVkWcyrxbwB72Do+HzSgivCztgWdwCiV8XhcGMIlE6JE5/KCAh1uAABEgTly5AizgVuWxXw/NkRFzxMf5iGynqKFm7bqXLp0CU6ePAnHjh2TKg19fX2M1XPLli2MEJlOp8nk8houIfO2ojBPLyq0IIGTXIUlS5bYFnFZD1F+UcX3jYyMQC6XE3oI+N/jVAiJ/r2ihZBeVPgQEqf8EDqPC4undHZ2erL6FYtFssjx8HH9NPzYBJg3RHzuc59jNg4+DwuvTaTc4QJNh5+LoD1L9DjdsmULTE1Nwfnz58lnT05OMt5KOgRMRiQSId4AzE3i4RdpUSW2dDpt+40iyykfCiRToGjcNlQs5e4ErezhdYnWL4D530d745LJJPFO0uE8NLi2oHCPLTZkLV9SqZRtXS2XyxCLxWB0dJQRUukWTqLvdjI6+fEmDg8Pw9jYmO31p556ylOxHydEvUXD4TCsWLHCcd3z+ltwj6HvJ68I0q8D3FRE0dCEY4u35vNtYNLpNOMx5r9Xhqh3LoA9zBorqtJzetWqVUproNd2MngeRgFs3LjRtQLj888/zxyXy2Wyp6hUqeXzacfHx20eSRTmE4mEUAagx6TfqsR+OHz4MFnH6NBcEU6GGD4yAyOOEDrSaGZmRrp2isIBUYmRCe4iRG3jVMZOW1ubcvirSuQOnduL16yS689XfxXVY6DXIrxfforphcNhopgdOHCAWWNw7bh69So5XzWqije0yqg2j7Wnp4eZK/w9f+GFFyCdTttkVrzv4XAYnn/+eUYOQKMFVoi9fPkyk8vL7+38/qJSL8PP2qYL9dQZ2oPIl1sGkBdC4c8VLfKiBzwyMmKzuEYiEcYCxm+ofK4DHfIIwOb68IyPj0M+n3cUPDHMY8OGDbZNiF+4jx49Kv0cHtmGFolEmKI2PEuXLoVsNgv5fN5Vubxx44Ywd1G0MMqU3EqlQjZ1kRUOSSaTzELAe+topSEcDtueE73w8RZdWX4I/WwPHTrEVNT0UhUMLW/vvPMO87roGdDeu3K5zLSWCIfD0N7eDj/5yU+IkQSh87AQkXIns6jxwgDvWcLeTslkEhKJhM1D9eSTTxIlhA4BMwxDaLAwDIMssLQxhjZK8H0ZZUaHUChEQt4AxGGc9CaUSCTg97//PbNGPPbYY56KGalSKpXI9y5ZsgQA5L1mRZ4G9PzLQgGxBUs0GoUf/ehHRJjF0CLaO43n8xtqOBwm45zugeoWNiTyLCMq3kS6t+DExIS0+u7ExASMjY0JQ6W8Mjw8bAvpCofDpN+bDMztVIEuJIf3U9YPlDZ+lstlWL58Ofmcjo4O6O7uthUt4dvA7Nq1y3YNfC9bHt7jRoNFhnDtaG9vh9dff50R2lQFST8eZlRicQ1y8hYA2Ftp0KhUqeXDyvGYVprR81ksFoVRGPSY9FuV2A+WZZF9QFQrgMbJEEOvCbfccgts3LgRAG7m3fJGCpnRQhQOiPsc3S6HFtxlhmgevhI/gD0se25uzibjyVDxatKpJXjNuI4jogrkPPR6RVd15gtM5XI5R9mCV2jb2trIHrNt2zbGc0bX7ZiZmfFcyTQejyuFoPO9Ur1QKBRg9erVzFxZtmwZGY/hcJjIpDxolC6VSjZnEcD82oo1RGZmZphxibICjk0+bF0lEs3P2qYL9dQIWRjo3NwcEY43bNhgE3ZkDzYajTLKn2EYcPnyZabcvCjErFKp2BQXy7Jg//79xGJLL1KZTAb27dvHnI8TE61vdK6P6PchsoRe7KH4R3/0R9DT08Pk6vBC9VNPPaXsYpcl/qMVl19gLMuCUChE8ofOnDnjKmSXSiVbyxakUqlANpsl1+skBOK1oBIl+z1YFCUSidjCQvmKcRs3bmTGk2majGcP4Rc0GvrZlkolpqKmqlBFK6aiMDoe0eaN1fnK5TKYpkmMCyMjI+Te0XlYSDKZtAnQ5XJZaHBxyw3ABuQ9PT2QyWRsgtXk5CRRQugQMPSeAQCsXLmSUezw2mpRtS8Wi8Ftt93mmuMVDodtxTUAgCl2hLiF8KpsuKFQCHp6eiASicDMzIyjB+mdd95hlG76WdCFR2TrKS3MYosjbE8iu27DMJgxwvdAdUJkBMI5p+JNpHsL4nfL+P3vf0/C9KoJKxTtC/fddx9s3rzZ0bvupcovbTTA+ynrByrLtRocHIQf//jHkEqlbEVL+MqLIiOF23PEe5nP55k1CUPHAG6uk6VSyZbD9MADDyjlwvPGRNGYoMPxisUiycOmPeVO+atOeV/0s5YZIx599FHmeOfOneR8BPezK1euCMdJI0JdZaAw61QLQhQGLzoHYL41CYZ2Y24qXyFflI7iFA6Yz+ehvb2dadWD8p3IEC1SNEX3nVcqvRgGVdJl6NQSvGbemMbLaQD2liLxeJxcK67Pokgip70wk8nY1m7c/9977z3YuXMn4zl76qmnmPe7hS2L9pWPf/zjtvP4yIFae+R7e3vJnC6Xy8w4kSFbA/ioRFkubzweJ0ZdPmJQRq2iZ4JEyyiVIot3pVJhrLmf+cxnlAsb9Pf3M+div0q63LxMIT1+/DhzXC6XbcUmUqkUZDIZyOfz8Mtf/pI5HzdwDCFQzeXDsvv79++3xbTT3ky3XB0vYViy8vD5fN62cGFhCS8V5SKRCLEqI/xmsH//fnjkkUeYEvlO8LlBNMlkErq6uuD2228nn0UvhLTwJfKiiO6dUzU1+tling7Ch+7ICnfQFdT40td0GX2Et7ofOXLEsaqxbKMwTROefvpp2+JYLpdtFjXTNEk+wqFDh2yKLx++Kwubpa+T9pxWKhUIhULkWiKRiM0zhPcTq5J6AQ0bfP/VVCoFg4ODZHz09fXB0qVLyXMsFovkecgagMsqrgKw3lYZGG6OlSudrJnlcpnZTEdGRuD8+fOQzWaZwkYqhQdojwov4NHGLtGc5AVG1RwcAGAUp2QyKX0v31uQbyFBEwqFSMXgD3zgA1UJMbJ9gbZUi3Aq1kCDuWA0hw8fZtYL2qoty7WimZqago6ODiZqQAVZXj0NH7aF3hOcs6FQiCgk9PqHEQuhUAheeuklqbHs/e9/P3MsWiPpUHwMBc7n83Ds2DFmnPvpGUpfs0zxGxgYIN6nJUuWkDBSUe7k9PS0Tbnx01KCR1atXQW8TicPHYbBi+Yjru+WZUEkEoEXX3yRUS5E3lmRAdgtHDAajZJ1lhfc+UrEIkWTj44BECuRokJcfolGozZlQ+SsUAHHL65BsjSRmZkZYdRKIpGAvr4+cg9Xr17N5PTyYaO8DDQzM0MiQ0RrsmhfEUWd0VEM4XAYdu/e7ZiL7xX+GkT7AhaR8opTLi/WB8F9y8173uiiXK1AyyiVImjBplKpwO7du22TlX/4dMGIrVu3CickClB+PR+o4OEmxwsI/LFT/hgKpMVikSm7v3z5ckgkEkRx9ZIv6SUh2Cl81a0puspnRyIRW3XPRCLBbBZHjx4l/QqxyI2TcMDnBtHE43Ho7++35Z3iIsQrFbz1SiTQOwlo9LM1DIO5n21tbcyCKTMGHDp0iLkOtxAJkWeRDonjQSEUPeETExPEg37y5Elh2BgKH4VCAVKpFAwPDxOFkVa0EGzR4mTpDIVCjLIp8pziecuWLbP1CuVDiAqFgs07JMut4g0bSDqdJhEMCL3x0u1XRMKmYRiOIVLlclmp3QSGPQI4CyD070XLJ/ba8trjlC7GQwt4vHdRND7cwt75fCEaFHi6u7uZ3HQePvSLz2GjwfWiXC7D2NgYswZ7Leku2hdee+01JhKiGkS/1bIs4bMFsHuXRIXg+Ht42223MR5tGZVKRXp/isUiGZO4p9ANxQHmQ/HQYENHghiGQcJBC4WCY+9Q3vPxwx/+UHq9dCiwZVmOPZFp3LzIS5culUYwYIXP2dlZAJgvnIL3SuTRsCyLnAswP85pI6cTvJGLpprqkTgG/vVf/1V6DobBi+YjXRgFC/TgmlmpVODMmTPCz/yHf/gH5rfw4YCicFXa8EsbYuk+lQDiOgR8mDKOQ/61zZs3M+kXTqgoPbSy4bbeO/WFxmiwqakp0jpIJAsZhgHHjx+XrmmrVq2Crq4uxhDBfx9drI0GI0NUwjDpqCiAm/MMDbgA8+NDFuHktyI+v+fyOc8A83u71z0RwDmXl5YfZWlctBOj0UW5WoGWVir5ASEKIeBz1+hBKpuQKEDJXNFui4xlWXDq1CnlMIpyuSwVFHER5C1GeIxemXK5LFzcRE2JvRTAEF2XzGvm1Qrc09NDNhCs0IjwiwJ6aLDIjVMyuiguXlVo5MOoeLyGJ9BjLBKJMAqmatsYPv+CtibTvdmckOXgIbQSef36dchms8QLJLpGVApKpRKk02mboksLA3T4LhZoEI2r9evXS6+P9oAvX74c8vm8rSALv1nHYjGboikaN065YSJ4jyQK8aJ8Tf65tLW1+a7+55VQKAT33HMPMx+cBJpMJsMIeLLm1OfOnWPed+nSJdvc51ss0Z4knF+xWMxmMFi1ahUJF0Nr/IULF+DUqVOwb98+qQAKML9miNagcDjMtG0olUqklyCA95LuojUAx4STt/Lee+9V+nz685Du7m5GYaat2ryiIxrLvMUfq/26RX8YhgFDQ0O2/GsAsdGBrxaNYw/3asxh6ujogKGhIaY4nczYyXsZSqWSzQCH6xHfFw/3EaySjdeG9wL3BrcculAoBGvXrhV6VLDCJ60MYT9ClQikbDar7I0RGbnGxsbgwQcfFCpgquBzEgnfiCgMHsBeEyKXyzERNPl8Xnofzp8/D6Ojo+SYN7BjCg6tINKCO44DkVdIZMjkw5Rlihs9p2RyHEZZqSg99DWL2gvRqPSFRjmWVkpo0AssW9PwepxC9kOhkNAYTVdHdUO0HtCeOVTq+XBnnK9+vJSi99DrAM75YrFo++0qMizmxao4ckTGU96b28iiXK1ASyuVKjjFl8smJApQsofMtwgRUSqVyEDmBQQUbHGjv3jxorSaI1pEeO8Af4y5LTyiBHCnhGBeARNNMqem6F6gcwwBgInfF1mQcHHiDQW8lZn3nHgRGmmjg0gRE40Jp4WPHmNbt24ln93W1mbLmVDl97//vWMOkgg35XX58uVMP8q5uTnGG8fz+uuvQywWI+NDVNQGEeV4ihR2WZEl+v5mMhm4dOkSZLNZ2/PB+VYoFMhYQS+d03fQuWFY8EVUfRDvOf9b8ZjfPEKhkG3tmZubsylSXqsyO3lV6A18y5YttnFy/vx5YYn0SqUC09PTjIAna0XBUywW4QMf+AA5duuHS1twec/Bk08+SQqP4X3CMfmrX/2K2Yx5AbSnp4cobvh6KBQSRoJgL0E/Jd1Fa8ClS5dIuLjsPj300EOun43QheSw9Qa9VjpZtd99913b/OIt/olEArq6uqCnpwdWrFghjbb4xCc+QUJgR0ZGGMVeZHSQpXJgiPLU1BQAzI95PrRWZuzk92esf0ArC7iWHjlyxNaLGGDe8ENHKeC9wL3BbR2emZmB06dPw4kTJ4TXyHsuXn75ZWUPy9zcnGMkiRt79+6FXC7nWi3bCVxT3KrkivLJ9u/fb1McUBZR+V30c+Rz+C9evAjT09PEE4rrKW8A471CIkMmwHyYsqxXL7Ju3Tolj5FhGK5KCG/MofuZinDrC43GVVGaiEiGdFvTnIxgK1euJPNVhJtyDGA3/GFIPD4rvv80PbewnkaxWHRsrScKe3aC7kXJG+BWrlwJAM6OI9rg2dXVRTzlon2VlvFoZZbmueeeg4GBgUXhpQRocaWSF65klmBesKQRTUhsVSDrH/eJT3xC6fpwUeArzWKpbXqj7+3thQ0bNtg+Q5QDgNDWOZmnhbYmI04CIq+AuXlw6M/xmsfGP7+JiQnGWymD7i0kqmpJT1qR0Kgap++kiNHvd8uVwjF248YNshAXCgVlTzavCE1MTDjmw/G5FKKqyDTd3d1M+BKAs/cc/52GV/Dccjx5wdMwDLh27Rqxcss2kWXLlkmfCy8YiqoPyzYLy7KY38sbIeicZbq0On3MC3Xbt2+3zbPu7m6mWBSAWrEHmnA4LK1qjWMqEolAMpm0eR7Qm4MFQ5C5uTmbVZ9u3l4ul2H79u3C74xGo6SoD4C9qrITnZ2d5O9QKESqIyO8cEjPbb5CMJ3riPchFArBX//1XwsFpz179vgq6S4rYPbss8/CyZMnpe/72c9+5vrZAPNzh163N2/eDJOTk2ScGIbBPAvRNfPraC6Xg5MnTzKvY9XEqakpx4gU+v7Qij0tnBuGAb29vY6pHNhOCUC8vsiMnfw6iUqLaP3kvx//TqVS8OMf/5jZL+i94dVXX7VFhNB0d3c7elT5dcWrgsdX9RaBQiv9u8fGxsgcd5IV3LjnnnvgkUcecfU+ifLJXnvtNen5bhEyAPPzGr2t7777rq1i7v79+5lKxxMTE64V8Z0qzf7pn/6p4/VgkSU3eaZYLDL58qI9izfm8P1MeXjPnsxbKUoTERXEcVvTnEL2+RoQPFg0ULZXY4EwEaVSyRaaTs9/vGc43rDlkeh7+LBnETimaOP59PQ0M2/RsBwKhWDp0qVSWWHVqlWwYcMGWLZsGfT395OICYy84FN8UOmllVkalfoGC4mWVir5/m1dXV02i6thGPDZz36WHPObK05I3jLc3t7OWO1pfvCDHyhfYy6Xg6mpKWaAXr16lWwQmPAcjUaFn4u5F3yxj7a2Nqb6pQyRAC5LCKaLreDG6WSJ46vBYVEGVURx6XRYmhOlUgkMw4D+/n6bMkHfK5HQ6BSn76Zw4gJNv99tI8Yx9tvf/pZZpFWbLPNjmj4WXevjjz/OHN9xxx2On79y5UrHIicyMF9KFD7KK6g05XLZJnTRSsjVq1fht7/9LVloaaXdyTqKxhocD6KiUaJNkg6bn5mZYdo2iDyq/Jyic7gQnBv8b8fQKqf2BiLotipbtmxxzRXD7+Wt8pFIBA4ePAgAN6sZ0+0raKs+baW2LIuUVufDlNesWUMqbnZ0dDh6KRHsb1gsFkmT+FgsBseOHWPGNC8c0hs2XyEY4GbLJPq+ywSn8fFxXyXdAcTFubAq7Kuvvip8D9+iQxaWz69NO3bsYOZAKBSCvXv3EgFLVEyH94ZcuHABKpUKWV9pg2QikSDPAOCmgvTAAw/Y+vXRa/bTTz/N7Gvt7e1MWybR73dCZuwUKWiVSoVZPzHMjP9+nJMiwZffG5zWY/r+iwR1fp3xquCp7AVoDKLPdVNSVDAMA2KxmKNBBOHzycbGxoRraj6fh1wup5TeATA/lnK5HOzZs8d2L/g2aE5twxDekDkyMkLm2r/9279J3xePx117ddLk83myXo2OjsJ//Md/wF/91V/ZxlqxWIR0Ou0YXmyaJhw8eJDZh2V7ssjb2d7ebpOFVNa0ZDIp9FCrGASGh4cZpQjDVrFHLr2u0Nc0NzcnzOHE+c87BjD8OZ1OM+umaisZuh82nYdP7xOWZcG7774LHR0dsHbtWrjzzjuFRfYuX74Mg4ODsHz5ctItwjRNsg7zPVhjsZhNmfXamoXGqYp1EGgJpRKTkmnovpAA5VXPewAAIABJREFU89bcixcv2oSoaDTKKD785kqfh5sRVuiSec28bhh8s+OZmRlIpVKQzWbhvffec1VKCoWCrZEw/k7a0p/P522LmSisSRY6NTw8TCafqNgKD98UHYV4eiFxCisSCR/j4+OOnmWEFjT453Hjxg1G4HITGlGRxPfQIao8mLfCC/X8WBEJjPwGy28YMiGTXzT5TZcPwfiXf/kX5t/feOMNaYXatrY2UlGPvqfYDFkG5mNhTomob6ET/LhcsWIFE+5D5+3R80MmfIXDYbIx0kUweHgPXyqVst1vuiCOqNCXDHrc49w4ceIEc861a9eIMcQLPT090NbWRoo9uBmTMN+ZzyHq7OxkBODh4WGb8CBrI3Ho0CH46le/ant9yZIlcOnSJbAsC9rb25W8lKiEXrlyhcwDLNR07do1MuZ54ZAvKrV9+3ZSIZj3tgPMrz+pVArK5bJtLVq3bh1T5MZLSXdRZUVaQRF5M/kWHbKwfOxHyb+GlMtlGB8fJ4KcUxsIADbkDsN+MRcfW3oUCgUy3/v7++HOO++EHTt2OHoq6MI72OIgHo9Lqx273Vs0dvIKoCzSolQqMQ3fsS8lrZTjPBkdHYWXXnqJ7FHFYtG2Nzgpdrygz+8jorHlJR8sFAo5hinSBZBKpRIZD05Kiip33XUXvPrqq8qRM/Qaunv3buE5lUpFKeoIQcOeyu9RMX7ye1elUiGV8532B1FlZ5FihNAVtbHlj0jpnZychGw2y8hrNPF4HIaGhqBSqTDX7sXjnU6nSVVVL20q4vE4rF+/3nbPli5dKlznaH7+85/b5NdsNssUPuTnRjgcFo412tnBjx16H6bXTSzqSLcCFClsKP/MzMxIxw/d9xRgfq8XPS+8t/hMh4eHybNDhoaGGIWXV2araX/m1NkhCLSEUlkoFGwDl7YydnV1Eev4tWvXmEF83333ESFHtLny0APba66TCNmigNY3Udy8iM7OTrJZ9/X1EQGaroRaqVRgdHSUUUx4q9uGDRukuaJ0/zXLslwT//kFGD2VtELh1ABWFF6ybt06eO+994Tn8z2/EFExFlrgoqscihZY9Fym02nI5XJkgRItTnTxEBreSCESGHlFih4bqVQKvvSlLwlzdnghnRbasP8aHYLBF7ApFotSBbFYLBLhx8m7yINGDRRM+cIstKDLe6/5xugA9ntTqVTgxo0bymXycZxnMhnHa+fHo1PYCVrl6WR71TwpvJei4mFYKZfut+YEtjApFotMZWI3RkZGYGBggBzHYjGbAebw4cM2YwcqcvxvLRaLMDo6aivUUywWhflLpmnCqVOn4MKFC9DR0QHlchlSqRTTXmB6epqMZ7zHKrnGOL+++93vwsDAAESjUam3PZ1Ow8WLF239gtva2mD79u1kbjiVdKcVHdM0HT3mTtUB8d465XLS/Sjxt/JGIbrCq+ia6XQO3pu1Z88esm/k83kIh8MQi8XId166dAkefvhh+PKXv2wLX7vlllsY6zxW48Z59dZbb0nTAbZt2+aYKoDGTj4czCkslTeAmKZpK2pXLBaJAIhyxJUrV2yVuWWEw2HH/OJMJmPz9OzcudNT1cpSqSQNIcTq2vT6jEKpW36gChMTE55aWtBrqKjtFuIUUl0NKp8hCgUFsHs9eURGeBUv8r59+8gcLpfL8Jd/+Zfk32gjkcyB0N3dTTxd9Nx3akclMlqEQiG4/fbbpW0qZJFY/JoDML9XfuUrX5F+PyJaC+nv4I1Csv151apV0voEeP+KxSLTuqxYLEKxWGSqR4tSvubm5qCrqwva2tocCxPRY5uvHIxks1mmyvSLL74o9Izn83loa2sj9QrovUm2P/A45ZIGlZZQKnn4suVtbW1kMGJMtAjR5kpjGAbTy8trXoRssXMTdOlFS/QZpVIJMpkMrF27FgzDgBs3bhBhNZFIMIrl5OQko8zwZfa/9rWvSYVSXuDG/oAyzpw5wwx4zEtDS2AoFHJtlsuzc+dOocWWVgij0SgT2sgLPqggTk9Pw7Zt2xhFWZYXhszOzkI2m4Xvf//7wgU9k8nA9PS0bdGjLawigZH3rCP42uzsLPmbFzL53zc3Nydc7OgQDB6Z9xfDqHt7e4XPWuZxpAuhJBIJJuQRFUYcG7Lm5W698s6dO+fqLe/u7oauri7o6OiA06dPuyqhqv35kKVLlzKFtWQ88sgjjPfaSRAZGRmB2dlZxrPlxPXr15nzVdm8eTMzP9euXWsr5iAy+qDyp4rM04mRD7Q3O51O2zZ9FLREiiQWTqBJp9Nw8OBBsCwLzp07B6OjoySFQLb2ZzIZePnll8l3dHZ2wpkzZ+D5558n1mmn4je0ooNhiDKwKqQIXIv40MudO3cKC+AAuIeNxuNx23Ol9xHe+zM+Pg7d3d1QKpWYMYXXnM1m4Tvf+Y6wwuON/5+9dw9yo7rzR7+n1XrbnpGEx7Zsj+2R7Ux27WR3CZCER4jHYALJ3t1QFZLo3kpVXJVlvLlsKGohYEhCeBl2XQmuCrOPn7P/7OxWUlup2ocBE9vhEQIkZLlghxhseTz2jGzPIGnGtqTRq8/9Y3Ka06dPP9UttWb0qaLwSK3u093nfM/3+flevqzwztM9YTOZDDzyyCPcMSKE5PRpLWgZ9HprlihzNPsru9eeP39epcTl83m47bbb5HvWc+wGg0HFvGAV9WQyCf39/QryKSsplAQvv/wyl1k2k8mo6tDJ/bCZCHb6cE5MTJgufRgaGlJkjlk1Eu2Mj70GHTnUIj+xmiFFwJPxhFxG71x073KaFT+bzcKpU6cUOgiLRCIBH3zwgUq+k5YeuVwO7rrrLkWWiFYdYaVSgenpac02FcSBThuXtIFE35ff7zfVr5MX9KFhJl0ZQBmNZOUwkdEXLlxQtC47f/68opdyJpPhGmzlchlSqRQMDAxovsvBwUHw+/0q4h8WCCEYHR1VZDTxylxIqjpPV9PiRGCxEOstXTMqEUJ3I4R+hxA6hhD6d4SQdo4BA9ZrpzdpX3rpJVlQ8zZXvVYTdEqsGbAKOKlVMPJYkkWRy+W4QkcURUgmkzAzMwOBQABmZmZU90wzo7Leb3pTfvjhhzXHwYbkaeVPy9ilwfbRI2lGVtDb28vdEPr7++HrX/86hMNhxXVrtRpEIhHF+GZnZ2XF59lnn1VEKg8cOKDpraOjR6+99hp3o63VarB//36VYPL7/bJSyKvj5BlHdESGnjtszQ7LwKaVPsGy8JkBMWR59RM+nw/++I//mEu7Ta4vSRK888478MYbb8iKLUlFJ8Jw586divezc+dOqFQqUKvVdOtwJUnSbb6OEIJEIgHlclk2vNwCvY55qcTHjh1TrF3Sv5OHrVu3Kjz8RqnChAnQqkJ26623KpxvhUJBtVnz1kFvb68lOndepJN2/LFRf/p4QgZB31ulUoFSqQTvv/8+1+v82GOPKVKJTpw4AcuXL4eBgQGV/CTzm9TjrF27VsGUfejQISiVSprN54eHh+GrX/2qQgGjnykPkiSpIvcExMHCpl7S6aysgTg0NKSKMLNOIvr5xWIxWLt2rfw3G81avXq1nM0wNzcHjUYDisWiQnaQvZKkaxOQ5/v888/Lxm6lUpEZmbVqfUOhkOK58Qi0fvKTn3B/y94fDeIAodlf2TlA93il7+PZZ5+Vyf3Y1lt0dKVarSoyU7ScD2vXrgVBEGSiFx6uueYaze8wxgqZMTIyIs87loCIKKUbN26U9xAe07sZ6EWCWdx+++0KTgIjEjgasVgMQqGQZTnGpmCSek2ADzMW5ubm5GixnnMVQN+BsHz5crjnnntUijxZG2wqLPlbyxGUTCYNdaC+vj5u7Wk0GoVKpQI//OEP4fjx4wqODy2jmdSq67WpIHOKZkDmrQ+SIs6Ct1+RaCWvxZnZulragUg70mkSNtZgpP+WJAn27Nmj+379fj+34wMAwDvvvAMAH+71NIsxjU9+8pOK58LTE8n52Q4H9G+s9Eam0emRS1eMSoTQagC4CwA+gTHeAgA+APiy2d+zXjviJSA5zCxVPlHQ2c11yZIlclSPRzYiCIJu+gENkkJEC0x68RkpjgDatUw0cxWJgNTrdThx4oTCEKJB3ze9yM6dO6dI+6XD60NDQwoD7IorrpCP420EgiDIaZd0yqXZ2gwWPp+Pa7CRcZNIMn1+YtzQQpZOS2BTeg8fPqxJ1MPrM8iLVrO09QAg92sk37N1nDzhTN8HK6See+45+W/eWAuFgorCWo+FTwuk9osn+EmEnEdrXigUFM/g6aeflhsqs0QtiURCjk4Srz+b4qi18erVi91www1yz0uyqenNPTuU+7wIL0+h5D0jLQ/j0aNHFc/OjCOA7rNlBqIowoEDB1QRNZa5lvfcedFBKyCeetrwGxsbkw082iin638IgsEgIISgWq1yDXg2pbher8PU1BT4/X5FWiiAmtRqZmZGTicmY5ubm4NIJMJ9r7OzswrFgq47Z69DxtrT08Nl8qZbrdBGAlvLw9bxpNNpVbYC21ZgzZo18NGPfhS2bt2qen9sNIuWoeT+taDFAFmr1RSfGymObKSdp8TqeeS11gippyKgyx2MQEeA2cgivQc1Gg1Flgmb7UKclJFIBLZs2aI4F5sWq5cuCqBMz8xkMvLcS6fTimdAZxgQBwL93vXqAFlYIWhjo83setPDFVdcoWj3ZAYkUscCYwylUgkOHDgAGGOYnJyU25ew9W1WQDK99uzZw/2engeBQECeJzxHEIFWuziC9957j/s56a9LR0EJEEJQq9U0DRNCDsiTaXTPYAA+0RfAvG716U9/WvU5L+uOPBctJ6UZbNiwQf437UiPx+MwPT0NlUpFdw8nzjkeaOcHMbiNQBjRWUxOTmqy2hLoOU4SiQT4fD5LvZFpdHrk0s30VxEAwgghEQAiAKCrLdEhaa0GuaSmhldUPzIyoto4SFuHgwcPwvLlyxUTgbBNmfWqkY2M3izpekkzRiWbl00wNzcHtVpNkUYBMC8cxsbGuN4Uct/ZbFalNNDRSjq8nk6nFYoOvQHSgoS3sJ1goWs0GnDkyBHu/RAjWYt5k+dNlyQJ+vr65HfI1sKQ1BnCwMtGAxqNBjf1SpIklaCMx+OykL/22mvlZ+Tz+SAUCnGba9MpkrzaJT1PViwWU7SG0IpyE2gpWno1rwAf9nCkwSrBACATFJCaPzYaTJ67VuE/LyIFoE6JI4p7b28vfPnLX5bfvxklwqyDiEZPT48cOSP3Y4YVTw+lUkkho8w6YUiqMoB+ehfA/PonLK8AH6YpGyn/wWBQ3tCt1ITR2LZtm8rxNzc3J4+Z7VtG/5+uX5UkyXT9KImGsvOAln1EmeI9g3w+z11rpGaZPAueQ4n0oCVyplQqcb3JtNyk26GwtTz0uhFFEeLxuOGcy2azMD09DXv37lXJrI0bNyqiUWzNdStAou160Euh08poIDKURBVuu+021f6kpdRee+21shH32muvKb6jnTHs+mQNK15KIcB8+j57T7waaxpasoBOcRZFUfGOZ2ZmQBRFRRmMFUPxpptuMt0vmU3FplPOjeTryZMnVeMSBIH7foi+tH37di4PgiAIcPbsWfl5NRoNOHHiBJRKJcN0cS2yHIAPM720jBN6L4xEIrKsYvdv+j6NDBCWnAdgfv/Xi2Ll83k4d+6cbcOEYGRkRLNFklbJDg+0fksTBwKoo45a0GJE7+vrk/uQGkV9tebxd7/7XXl9EoObh2w2C6VSCd577z3NNTQxMWGoz+utv1gsZsgwv5DhilGJMZ4EgL8HgDMAcA4AZjHGujlFdPoZ2+OKbMgkUshO3GuvvVamIibfLVmyRMXySJqykvQHuj5QD2QxlEolbvrVsWPHTBV7a9VdknRH3uTTGh8xoHgKlJaCkkgk5DSgG2+8UXEvPFIVepN3goUOQPsZ1Ot1OHTokEphisVikM1mNQ3rqakpRc862kghqTNE4eaBp8hgjFVzrK+vD86dOwdHjx5VeGMxxppF2fV6XRba9HtCCAHGWLFhsP2UYrEYPP/88wAwv8Hce++93GvQY+aBGBC8jV3LK9hoNLjK/uTkpMyiy3pD//7v/x4A1EX9pDZWawNNJBLyRpFIJOT3cdVVV8learOoVqsqT6pRaw6AebkSiUTkOWJmo9VKl+LBbGSFNmzp32r9Xi8ypDWOarVqOl1JCzt37uT2C6xUKjAxMaFw3kiSpPD4+/1+Ra2fWaImIiPZsgCSxmXm91rKGWlDVS6X4corr1R9v2XLFrj11lvlqHalUuEqFdVqVc5GoduhELZxgHmiG/q9EeXF6P3ptUnK5XKm5rkbMONMJSB7iNk+wjRIVOHZZ5+FHTt2KL5btWoVd41cunRJftbs+9JjINcisGPfAa8sx6iVEGvwkAhuPp+XlXPyWSaTkWu5yX0QHcisUUn2RLOKLeuUzWQycrsjo3NoMX7y2lkAfEjAyDoWBUEAjLFCDjcaDajX6zILtR5o8jL6nGZAk6vRdbSsXvKb3/xGdizRLeW0Uo3ZMRvpnRhjmJmZ4RJ9WUEmk9EkRkQIqVoKaYEEbFjiQADls9FioQdQOizodUR+b7TvIoQUaf8Eq1atgoMHD8LMzIyu4wohBMlkEiKRiO4cisVius9bjwwokUjIjguA+fk0NjZmSt6RIEi5XIZoNPoRwx94FG6lv8YA4P8CgA0AkASAKELo/2aO+QZC6E2E0JussJqcnFRMVHZBh0IhTUWL9pbTKYqFQkH+7ujRowoPKDUm7jkxxnLKFgujJvJmkc/ndRckC0mSIJ1Om/ZAEoRCIe7GR98DHUkggtMJFjojxONxlWAh9UA8IIRg5cqVXCKOWq2m6BvES30F0BZktNLBppD+8pe/VLwrLSZdURS5NZXLli1TbBhsqlk8HlewiWGMNcevByJEAbRrY7LZrCqtW+96pHaNjjidPHlSNg7q9bolqvl8Pg8DAwOAEIJisSg/95deesmQnZgFqTehocfiCQAy+ysNswoIoXg3gpU1yhp8Zg1SI0ULIQSDg4OAEJLXulXFHmD+Xn7yk59Ao9FQ3HsgEIBgMKhZXyJJEpRKJZUyZZYlT+9Yuv64UqloPm+tCAdpQ3Xq1Ck4ceIE95if/exnpgx3ulclr+6JbdFA5h5vHiWTSRgeHoYvf/nLcPToUc01yXIQtBJW0jDJnsszkHk9ZwHmFTyaTfjgwYNcMjZeFO2NN95QyFAaeu+SjirTdY/ZbBYmJibgK1/5CjzwwAPc3xq1D2PvsVqtyvwAfr8fotEoSJIkkykRZwevlY4ZDA0NQTweN52iSM9V0uwdAHSbxeuBGIIsGo2GnPVCR9KIsq71fvR0AQJSO2cHdHYBwHx6cyqVUkWgCYs8wPw7J+PVMxbJGHt6eixFrxqNBuzatctWxKtWq2k6UHjOcx4CgYDsGNByCJH2Y3rz36j1idGc7u3thUgkojLovvvd78pBIhqsw+v666+X/60VBCL1nXqO1/7+fl2iNvq3pJWRmawgEgQ5c+YMCIJgvhDaY3Ar/XU7AIxhjKcxxjUA+BkAKJK3Mcb/hDH+BMb4E7wURPql0aF2Qg5D5/q/9NJLcuuEer0O0WgUtm/fLn9P0vn0IiZ/GBP381KpxI1SWoGRAmd2gRMYKRFaVMV+vx9SqZSK9Yu+Nzpl7YUXXoB8Pq+q23EDvHoUIw9pKBSCFStWyP39CM6ePatwHFy6dEkltILBoKbHKRaLyXOwr69PYShJkgQYYxBFUZP1DSGkUHRogcb2ZWQ93o1Gw7ZnkgaJ4qRSKdmgoEGyA0g9FP29kdeQrpl86KGHFN89+OCDsmJkBDp6NDc3p8u4ZhUjIyOmMgjI9UjLGbMKerFYNBXVtEIwxM5REukzAp2OzYMgCJaZcXlACMELL7ygaDtCoqmVSoUbvSHvgEQg7CIWi3GV41wuB8ViUZ5vWsycPKWmVCrJ77DRaHDZrI8dO2bawUH3qkwkEvDAAw/AY489JsswrR7ILPEPeZ+zs7Nw8eJFWYYBfNhoncgIPdISu/jkJz9peIwgCI4Ys/TeyMqolStXKlLnSbSSlttTU1NcR6nduUa3I6PrHolimMvl4M033+T+Vi+zAGA+FZWAZmUlzkWyt5BxsERXxPg2Q6AjiqJc42uUokmOp52yZF6R/chpxwV57yMjI/IzsyvzjZwbZsfOHkccOyzInCA8H4QRXe86GGOIRqOW5yV5/u+//76cXWdEAkP2M6PI7o033mh4fTrSTGcoEQOT8DIIgqBrrGmxP9PrX2+PKhaL8O6776qO0ZL3vFp+AP25sGTJErmvNzt+gkgkwi3zIdk6bHsiM0ywdEcBJ1odthNuGZVnAOCTCKEImn8bQwDweysn4HkC6D5nNOh8ezJh6AlMalqayW0mKYt2EA6HTXkqZmdnLaUTaRH/AMw3g9Yr+GUJUrSMK+JF5aWUGIFd/FbujSCXy2mm+pCG3NPT03KrGOJZZpW3er2u2LDIJsBT+Ml8Ie+bFJHTmJ2dVSgALNh7HR4eluc0K3hZY9rn89miZudhamoKhoeHYXh4WCEY2cgk/X/23wQ8ZWlkZESVwvjBBx+YFoyHDx/mbnx0A3C7MFvwXq/XoVwuQ7FYNIxssmjW8CUgnl7eeXkKM/suGo0GZLNZTQOUlpmEBMZOTWWlUpHrfImsJXPdyNNs9VnRa4h4kHnKNJ02DQAKpYB8rqXUmImqa5HZaIE28oj3mURTtdLjWPIr8j6XL18uP7d6vQ5vvfWWoik3gD7ZVTMgz5AeMyvXrDhMiCOYjXYkk0m5ppJtDE/6kxLwyiTy+bxhhNAqnnjiCTn9k7x7uh+hFvr6+lSlDGRv9fv9kE6nZcZhltlSqwSGfJ/NZuXvzZSjbNq0STYSaWIUmqxPC+fOnVMo33bq1Y1A5A/9jDHGtoxXo/dvZT8lZVK084aNXJHxkiiyJEmqFj48pFIp0y04WBA50Gg04OjRo7B//345eMC2XiGt0/Qibj6fTyV3eJidnYX3338fvvrVryrS7EnGAX0NNqONPPd4PK4oqaHneU9Pj1zrqLfnV6tVS3oBO79ffPFFue+lFohjki3/YSPYrBODJv2kHThsPT0LIgvbmW3iNNyqqXwDAP4DAP4XAI7+4Tr/ZPd8RDmk0/TohUny7Wll/ac//ak8qXbs2AHT09OWittZ2H3hVgyEnp4e7nW0hP/Pf/5zTWXHqH8kq+DpLbTnn3/eFiPV1VdfrXvNZkAvVjqdKpPJcL1lJBWVKEXr16/XjKTRyrYeG5leujLZYOiegGbnEMu82gxyuRy89dZbis9IhKFUKsHvfvc709eiFQuS/qo1L8ye8xOf+AR347PD5Mpe0056p9nIppMgrYno8W/evFn+t5koA2mjYdRvjVzDLkkPkTe0l5w2Jp1yhgAo5xtxIPE82fF4XPXuCVMxiU6ySg2BGxs5edYnT56U64JzuRxcvnwZdu/erTjW5/NBPp9X9aIkxDRs0/KHH35YXi+k1korAtAMXn/9ddmApxVJkt1BnCBWjG0iL/XqQ+n3Ua/X4cyZM6p33tfXpzBmjRhuAYwdmuweOz4+Dk8//bTciiWVSpmq5yMs5vTYbrnlFpnsKR6Py4zDLOlfoVDQ1VGKxaK8hxqNg81MIIRsNCESew56HbMyuVqt2kp/NfMbnu5h5VpmUoGtrHNSRpHNZqFYLML+/fu5kSsSoTN7bitMumZw+PBhOH78OLzyyivwwAMPyM4rOuNHD2afcSKRgCVLlqjmLIByjw0Gg6qMtnXr1kE0GlUZVnT2wcjIiFzraIa4jW6/Rf5PShhoZzbrwCMcHHprjLx7lkWdrDutd02X5JmpsSUgspAlvutkuMb+ijH+LsZ4EGO8BWP8/2CMbcd0ecohT6GnXwpRdkmhul0lykkYKVxawkBv8WsRNBQKBV1D8Te/+Y3uWGjUajVVLZAZmG0AaweBQEAzVYJXmE4MPFILprfYc7mcrMz19PRAMplUeWp9Pp+hx5GkxWQyGfjBD36geRz7fmOxmGqjtLOhEzz44IMwMjKiICEgkXcrhj49n9iWIXahFSmyo+wThkSCZglpmoGV9xUOh+UeeDzwmHNZOUGcBHopx0SOOmH40Wyf5XJZJq9yykgTRVEx34hSwtsL6JpKEuEjaf5f//rXuUoNAWtsaLEXWwFJZX3yyScV7+n06dNw5ZVXqiLSJOJIt3gCmJft7BwulUoKB8Ho6CgkEglFuYdT8Pv9EAqFVO1xCLGVXvkAD0btNngol8sqcg5eGyQjJdpIzvH2ktdffx0A5uX4HXfcYVqesGuf1NbW63VZgU6lUqpnZ1S3ODc3Z4lwht3/ScuuUCjEbfVjBDv7eTgc5mY5+f1+eS1ryTez47OTAWUESZLkWsTDhw9zn2W5XFbNK733R3RQs/WtZsZ46tQpKBaLcPr0aZk/wgqeeeYZw2Pi8bhizrLpsGQfqFQqqnc9NTUF/f39ivX1gx/8QDFOWgcwywZOl2jl83lZD6blAKvzE+ee0d588uRJw7XO1gnTzuhkMgmhUEh2GPICPLlcDr7yla/IdfIs8V0nw82WIpZAtxQhfxNo1aHwQG+ARIianahOAiEEkUhEESY3SiHRSuGQJMmWIsibzO+//z68//77ls9lRJXOg1ET8WZAe6bZdCqegkEz/Ropvtddd52cmpTL5eDs2bOKul6SHmN0HoyxLGzoxsa842j09fXJPbkIaAZNq6jVajILLsC8QWiFYIPA7/dzGUrZxuJWoDWvEEKW1y3NIA1gv6aqWcRiMUsKWKlU0k27NasoGN2vW0Y2Sf2ykgqph1AopNlUmuccnJ2dlee2z+dTtH9IJBIQDocV6X80WOfSQw89ZIkwjQXpVZnL5VQpikQJ7O/vV3z+/PPPw1133aXyjgPwjR06Hfa///u/IZ/PK9LYli1bZrlVFguyptk0dlJ/TWrU69lDAAAgAElEQVRYrUaA2KwJ+jveuSqVisJpAKCfVWMXehkKGGO48847TZ+LnreCIMgtDiYnJxWZHbx3qyeXrToB6f2f7dNNtwrhgddmasWKFbYMOF6Lj40bN8prWUsuma0Bd5v5mI2AAXyYpst+rqXjkTKdbDarSOFvxlkMAHLfdjp92OwcCQaDpnpfk/2H7PvkvZF6UgIy72mjs1gsqvZ4ti/no48+KjtE2bVuBvv375fXb6PRkGUMzwEZDAYN92ajfbRWq6nmLGH6J8+jWq3K76FQKKhI1kZHRyGXy8kBkHQ63fRc8Ao8YVSSTYJVGMhD5tVx6ClINNlHu4peg8EgVKtVBRFEM5uhFi23HgqFArz77ruKCe3z+WTFyyqselLsRC3sbFp66VRa0DLgef1I2VSioaEhQ0IGAiJotfo0skgkEvIGRIMmO7GDM2fOKN6HkQHAu044HFa03iBoxmmj9Qy3bdtmKu3TzrndRiwWszx2IiPK5bJtkiajuUETFdhJDTaCE0Y8IfxhYaRs8Ag2li9fDvfccw8sX75cUz7wFFezUU0eCNvm6Ogod/49/PDD3Hrv48ePc+u2zOxfJFpJ+hiuXr3atOzVemcPPvggNyIIoDTArMr4Rx55RPWZUQTdbg2aVehlXth1yGjdE6+/dKFQUOxLzWaCFAoFuOuuuyCfz8t1q/S4SMubRCIBCCEolUpw1113qdjIAebnKJ3WZxbValWl91x55ZWKdacVueO1j1uyZIljkT4rYJ0OJD2dhZaMiEajsp7i9/vl45xIibWbNmmWfJI42FkWYtJbkgU75y9dugQnTpyQ9zV2LU1MTMgOOK21rjfvDh8+rLgPkpnFQ61WgzVr1jSls2g5wvWyt+jexoSUB+DDFP9EImHYU7xT4Amj0u/3c1kPeSleZlKsSqWSKhrXygakPp9PXujESzcxMWHoQdLagIj3y054nM4/p2EnzcuqAmFV2JHaMpqYxOfzuWIc6DU/Zplx2VRiwvJlVomu1WqmyTSIYOF5Ru2SGJDz0c3bjcbOuw4hJyLno8dIRyutkDpojeP222+HwcFB0+dh57gewZPbmJiY0HUisKQ8BES23X///bayCQDMOx28UA7AgxYpTi6X0zSEA4EARKNRiEajin1kcnISjh49asmAfuKJJzSNeiP5TfruAcyT9fDug9dewaoxzspDUltZq9VkB5hVYhIWAwMDch0ei2Zq43lZR8lkEkRRdLQe1yoCgQC3B14zIBktLLLZLMzMzBjua1dccYWKpMvqXnj8+HE5vZp1spO0XEJKhzGG48ePcxX7SCSiaO1iFvV6XZX2zCr0Wg64SqWiMtLq9TqsWrWqrXNFD1oRb/pzwvLr8/lgdna2Kf3GTBqzXqs8M6B7hbN6ASsLSCor+37K5bI8D3lOATs9UAnYMdVqNfjFL36hyvYi/b9rtZrMP8CLohuBV17F6jzsc6H5J1hSnnK5rLu/dRo8uTLZVA0imMnEM7MYiDFFfms2UuQENm/erPBamEmVBNC+r3Xr1kGpVLKtJOfzecjn8zAyMiJHTu1EBIlwMivQrRrB8XhcJQD1ntvZs2chm83CxYsX4Z133pHTHoyKowG0iQckSYJrr71W8V0sFlPMH6u1QefPn7dEppHJZLgRlGYiQfQctHseQoiRzWZBFEWo1+tw4sQJyGazsHTpUvk4J8huDhw4wKVy1wI7T7Qa3RO4GcUkLHV619bLPDh9+rThGtOau3rrRa8notchiqJmdgGvh+7ExISsHM/OzsLZs2e53mvWuB4fH7cdKSZ99wD0GVmbNejp9kQAH9ZWkj2H3T+NwB5Lzy0n6qZZjI2NqWp/narRtot6ve5Iyx0axMD/i7/4C9izZ4+sNPKUcx7OnTunYju2U7Zw8OBBOUWUVn5/+tOfwsaNG1UKPW9/K5VKlsqQaLBGKuu0tQLynuxkbrUCWnO4Wq3KKfmE7bbRaJhmltYyfvS4JQiazSDp6+vTnLOs8/vRRx+V6+tZHDhwAPL5fEvK0WZnZ1U6P2EQv3Dhgkw6VK1WHanJtaLzsKQ8lUoFfvzjH7etXMdpeFK7YFM1EEKWIw+EfZNMerdJO2iP4nvvvadaVGauz5tUpPFsMxE7jDGMjo7Kwqxer1vubSaKImzbtg0GBwdN1xxZjapdunQJisWiitlPb7Elk0m5Hu373/8+AJivsdB6nqQvHMGyZcsU9O+1Ws2UAkTOXygUFNTyeiCF904bPXoNe60AYwzJZFKRrgmg7bQxe98stBrV02AVC4wxZLNZGBkZMZzfpGeYG1T5APope1ZqcbVgZ350MmU5eU+sN5eeX6TW7+jRoyoD9OLFi7YYrO1i69at3M/N9Bck4K2bdevWKdoTAczvdfR6adZoxRjDqVOn5L7PTuPSpUuurTu7YB2HTqFYLEKpVIIXX3xRpQMYrUdJkmBsbEzxmZ3WKZIkySUwhFym0WhAJpOBTCZjuo2SU+ztoVBIYchqPfdgMKiaf16bNyy0ykrq9bqckq8n22+66Sauc52n0xBiJ7qdBYtAINA0AczMzIzi3dPlVKxuppeRR7Jw2DntBnikRbQ+Ro4JBAKOpCAnEgnF36yhSr8DnsPxF7/4RdNj8Ao8aVQCKCdrLpeDH//4x7bOQ5R/t/Pw6UVNk8LYRTAYBIQQ+Hw+eRHbCdUTPPfcc4q/P/3pT2sey1NYCQHFxo0bXTPQ6WJrMygUCoqmzMViES5fvmzK6O3p6dE0sthCcra2aGpqCiqVCpfMgAb9m/3798t/63nGyDNwsrWFz+dzrE2JKIqq9NlYLMadE4IgQDgcthWB4DWq552fRrVahXK5DJlMxlS6cSqVUpGmOJECZ8bg02NpBbCWOm7Gax8OhzvWqCQtLAA+dDjStc+k3pLOZGGVbyfbGRGw65j0tAMATbbn7373uwrSLwDt+cLOAYQQ3H///ZBIJODWW29VjINdL806kJ544gnNmiknwCORa2fT71gsZtiD0ironnp0FoFWJIcHJ+YtIREBmF8bGzZskFsW1Wq1lrdRKhQKMDc3J68DLX2CNx9IxM9tch670HpfZI0bZTnt3LnTdEsQ4jySJEnzHa5bt67pCPyFCxcU2V9swIeGEdfE2NiYK7KYB1YGrlq1StWiqVqtwpo1a2Q5x+sJbQZ9fX1QKpW4GQHk71qtBvfccw/cdtttqt8vlCglQIcYlQDzPRntgBh3zZJ+GIHQTxNgjKFWq8kKn1WjtlqtQk9PD5TLZZiZmWnaY8x6Y9577z3NY3kTfMeOHYAxtsTo2gqiFLZo+vTp06Y2bCs91tjNrVAoWGZQPXLkCDQaDfD5fDKTK88YcEO4LF261JEaOoQQBAIBFZPZyZMnuc88GAzKG6RVJfedd96Br371q7rHsIZZvV6Xn5+ZdONsNiu3RgCY34BjsVjTCrmZtkBGKTdG3mX6eZtRxkkrHfLbTCbjWcWMRb1elzM2ANT19vl8Hubm5mzJyGbqWBqNhuI9FYtFOe2a51QMBAIwMDAAX/rSlxSfm40kJBIJGBgYAADl/MYYK/62mv7Kw/j4uK2omFkQhyCBk+1o7CCfz7uu2EmSBO+++27LFUhW9tCkV3bavDQLjDFMTk7CHXfcAQDauhHPGCIRv05Twok+aEQ6FY/HYdu2bYa6E/293jol5F3NQJIkReozKaeamJjgtvbRW8etbJvBjiOXy3FZW2u1mtxP3o7BS95FJBKRAz/0M1+6dCmUSiWYnJyEY8eOwbPPPqty1q1cuVLxN8bYGSr1NsAzRiXbUsQptMoropV+QJR5K2lPAPOLkywAJwQom8dudjMJh8MQjUYhnU6rCoyN0KpnT0OSJFNsgZcuXbKtxIiiqGJmJJ9pgTwLhBBUq1VHotlmIAgCXL582ZG1RRiNeSRCPJD7tOoJRwhBsVi0pdSS37DpKDyQ1hOE1fb+++8HAHfSRNlnZLQ2rKR5mXWi0CgWiyrngBW0g1k3mUxyCR3M0OhrjbfZ1CfWkCWkOTyQ2k+23tesc4olniCg2wmUy2VYvny5I++HpEW69a7JcyiVSqbrDN1Cq3rakrKcVoKVPXREtlXMuixIHTCAtsNfj7yl0+rCEUJcki4Wu3btgkOHDhk6HTHGphyDdutgWbAlSaOjo7bmDm9fc7qWWQvbt2/nZrGdP39edmpdffXVls+LMYZz584pyAtJyZogCPJnpKzq4MGDKnsgl8sp/kYIeTvPWweeWJmEVYwohG4wtbpN1LNhwwbVZ7Qy3aqFowVW0TEbOa3VajIBBVtgbAQ3mhI7BTqqZRXhcBgajYZKOTDjhavX67ICjDEGhBBEo1HTz8qqgkealTcbqRRFETZs2ADBYNC0wUP30zPzrEmKTSgUgmQyaYuMweo7LZfLcPnyZdixY4ccBfICnPbE53I51XxtJt3Qyvic8E4TBma7hrCW8ahFPW8XtLLMgijzbL0vj02QBzpldP/+/fK/McZw//33y06cEydOODJ/KpUKFItF1+rYSB2o2Zo+N9FK4hcr76ZZgx4hpFJgly5dCrVaraU1xizYOmCtY3hwIhLfaoRCIVOOi5MnT8Lc3JwpR1O1WjU0rs+cOcOd22Zbomnh0KFDtjJD5ubmVPuB1U4EdvVKrewlko4NAPC73/3O1vVmZmbkfs3ZbBaSySSEQiEIBAIqp7okSSqZv2LFCsX7qNVqSiuzg+AJo5KlQCe0xE7hrbfecrxegobe4kwkEnKD2mbRjBJYKBQU0Sqz6cD1el1ub7Bt2zZLHsJ2RCpbgdnZWZWgIBu1VWCMIZVKmd4k29V7sV6vy44ZOwyEZgQz2WyIgDerbNMgz+fkyZOmjgOYF/IvvPCCbdZPp+Hz+VraAsltOEX2kslkbHveRVHUjNY7uaaIssxT5Ig3mq33NWt0FwoF2LVrF3zuc5+DQ4cOKb47ffq0/JydJtdxq9ZxaGgITp482fKaPh6MapzbhWadA1u2bFFFs2ZnZ2FyclKTEK4VUUCfzwehUAjy+bxlh79WxN7LsJp141SEka6npREKhWDLli22z2unpyJCiNsq0EwEl4bdNVEoFAyjq2zE0Apobo9jx47B3NwcNxutXq+r7BF2b6pUKh3bX8QTRiULpzeZ73//+67m4IdCIU1D+NSpU1Cv1+H3v/9909dpRlmIxWKKaJWVyClZGOl02nMewnZHgAnK5bLtsWj1jdTqZWgVpIZOD1/4whcMqb4vXrwI4XDYVi2e0dz1+XyKdO9arWbLKYExhrNnz8J9991neNzExIScdlcsFuHee+9tKiXUKfBYD7uwvy/Qjcd5cNKoFEURhoaGYO/evarvCCNhOp1WXDMQCJjOHDl58iR3XbTL2dTM9dPpNDz55JMujKYLgt27d6v4HgA+THvl7Tut2OMxxlAoFGD//v2WnXk8kqfFCjN7JM85W6vVuDLKLOi6XLMgc43Xw9EK7M7PPXv22HKYWNVDCBGX1jhFUVRlf5CsNervjlUAPGlUOr1Buu2FrNVqmpGF8fFxV69tFs0Y1XY9l62g/25XRId9npVKhav0hkIhwyidlkOCJ5TsvItarWa4BtLptCIFg7cG6/U6LF++HG655RbLY7CKqakp24bVzMyMYZSTbkNBcObMmbZF18l7Ja2DOq1myMswmvtOPmtJkiCdTsPGjRu53+/fvx8SiQTEYjEA+LDG2onrehW8SGwikYB4PO6Z/XGhgk6T9howxqZaR7FwM+vMSTihx3p1H7juuuscOU8rnWHj4+MtcdYa9QMXBEFVLsfWWiOEWsdo5DA8NWPL5bKiRUSnoF6vm+7d6CSsLEjSw8/OOcj7sLpBebmm0mlgjFWRyiVLlsDmzZsNDfpmGpWbgZEgRQhBPp+H48ePyx4zrVYCU1NTqkiLE2CNuWbSUDDGhq0Q/H4/10NulXzAqU1fkiTw+XwygVOnyUAWXjOMSeNxFlb7H7NgDaalS5fqRvwPHz4M+XweVqxYITNA+/1+bjSpk6An4+r1ukpekOMJedFihxliMTuw2o+6VaCZqBcqnMiOs1IWQ8hhzKBer3ui3MPNDEI2qLFmzZqWss9qYceOHYZZbdFo9I9aNBzH4Zldn4SLn3nmGcMX70VjxekIhxml3YrSVi6XFelfRMGysqhffPFF08eSay4msBGHhx56CACc9TYKgmB4PivXI8fed9998lxACHGJpwDmlfCdO3c6mnYsCILjwt6o36RTc1OSJMccSsuWLYNIJOK5WmRBECzX0fb29npGYezt7dVMf9Ui1TELdt6Wy2XI5XKwa9cu7vGSJMH+/fvB7/dDOBwGhBDUarWOa5FgFez95fN5OHXqlGGa+kKAGdnmllPaK2uQRTOOHKuMzV5ybrkF4tS2snc0E8V+5ZVXbP+2FfD5fCqZs3r1alfngpbOTju4RVGEdDptyMaPEPJGXZcNeGK10S//5ZdfdsWoJC/cLYPUDqmIHswoGVYYvNiCYavF7q3o4+U1NCOAfD4f9Pf3w+9+9zvHUy4kSdJ976RXkhlgjAFjrJi/kiTB8ePHNX9TKpVkA7rZFGdRFEGSJMcNqVa1CACYJy2gn4MdIiMA76Z12WkL00oYyUCSasrit7/9LfzP//yP7fMCqOuBqtUqjIyM6BJFHTlyBFKpFASDQSgWi4u2fvaJJ56AjRs3KhqrL0SYqZf1AgNuO9CMLmfleC9EqNyG1Swbu73fAbwZ2KHRaDRUpVG//e1v4eabb3btmlr6MSFtEwQBgsEgxONxR/qGexWeMCpZhcWof5+dOjriLfCq584O6vW6aQWWTa+xWuz+9NNPw2c/+1lLv2kXzJJeaIEYk82kZgUCAXjggQccN5aM8vUBrBW+a20OZh0IzRobbjkqWmlUFgoFxWbl8/lsR3Jb5bgx4wygGUytyM1YLNbS50/uRWsuj42NcT3Djz32GPd4URQhGo1qGqMECCHu+3r55Zd1fydJEgwPDyvO4zW0ggBtfHwcHn744Zb06/U63MpOcEOeOJ2lQtavUckCgdVU8Uaj4eiYvbheWw2vsiXrAWOs2VbECWjNC7r1S6VSgZGREZUNs5DmlCeMSqsC1aqgXLdunewNXmjRNrMbcqVSUbB2Wo2KvPbaa3D77bdb+k27cMUVVzT1+1AoBKIoNhV9LpfLcPr06abGoQe9KKoVp0u7Uy2JU8TpddkKkiiCQqEA6XRa8U7sOiRalTJuZo7Y8e4TQ6uVcpbciyRJMDg4yN2gWXn329/+VpO2v16vQ7FYhHg8rrvO7N4jxhj+93//V65p8uKedOONN8I111zj+nVee+21tssgt8FpbK46xo3Ij1uK6tKlS5s+BxlbX1+fvH6r1aqpNGA79+Wk46IV67XZPpILGVYysWhs27YNEomEa1FWrXlBO94JE//k5KSp33YiPGFUuolgMAi7du3q2BQjowXAUhFroVgsNt0u4d///d89n/YA0BzJCwDITcTL5XJTKbB2BIXTtbRGWEjCjI6s2TXOzHrLaUSjUUgkEo40UG/V+zAzz+w4VeLxuOU0rGZBHAgYY6hUKtz1wRouWlFKgA9l7tjYmO1UZiM8+uijrpzXKVy+fBm+9a1vGbYZaha8vnULDfT9CYLAvV83DGu7z9VINjiRpk/Gdu7cOfnfGGNT/RntZJt12hxrdt0tZIPUTkszgHl5ftddd+muNTeeG2F2pduMtHqPbCU8YVTqvchmX/LNN9/s+aJiPTi12RCFqxl0ynNsdtPjCYFWwUyazkJK4XbKgxyJRBQGoV0lwo7TpF6vw8jIiGzIlstl3Zo6L8CoLtcu/H5/y+cnHZUeGxszdV96yiuRuY1Gw7VaUqeam7uF119/HRKJBPzVX/2Vq9dZDCQqNDrBuDFK+3byHlhZYUZ2LGSDiaCvr6+p52z1t4thHZ48eVKXKwKgNevTrlHcKfDETNKLDqxZs6apcx88eFCXjGEhoFUbldmoaKdBK1WyHey1ZpVYK++cbBhe3DicMEBEUQSMcUvr+GiEw2E4fvy4fC+dYPS71fKjHY3J2foerayUVCol/9ssOUwnZbi4kcGwb98+x87Jg9393YuyzAzc2KtXrlwJgiDAsmXLHCGkWbFihQOjcg+dYJg3A5/PB9PT0wDQunneCXvWQsFC783rCcmst3Ca3dS9zFrYaVizZo1t4eNlY7QTa3qsbKwkKuVFRaxZBUEURQgEAo6tcztexEKhIBtTXp7nNDDGHTnvebBzH7t373ZhJO2Fk4phX18fALhPyHHhwgVbv+sqwR8il8vBRz7yEVXLBLuyaHp6elGwpWrBiTKGZiBJEiSTSU/u1wCds8e1E3rPyA7RaCfBE7NWS7EURRHOnTvX4tF0oYUHH3zQ9mbulnfRiQ1goSjXeggGg02z4noRN910EyCE2voOMcZyyrVb89zMRt7d7PVBpz5deeWVnlXavICZmRnIZDKGx1ntGchCrySjO5/NAWMMY2NjMD09rWChdqseeKGj3XIBY+xpw+O6665r9xA8D0EQmpVfHes188Su2mytXxetwYMPPmjrd5FIxBWCH1EUYdOmTY6f1ww6QeGhn3mlUllwRqUoinD33XcviqiFUZ3TYoZZ2TI1NaX4u7+/HwA+XMudsKZbhWq1qktmROBmbejWrVttEWctNtTrdahWq5BMJiGdTkM0GgUA+8ZRuVxuSUsZr8IL7TLGxsbawulgBt01aQyEUFMOt1qt1hzbZBvhCaOS591HCMGyZcs6eqNvdxqFk1i5cqXtGsNqtepKJKler8PExITj5zWDdnuBA4GA4dqgNySMcdPsv16DJEmQz+c97dV1CitWrDBMSVvotUZaMCtbWAKvZcuWwcc+9jF5HbXy+a1atapl17ILM7LV7QyBxbC2rWLp0qUgiqIsD0RRlJ1ONAu1XUKQSqXiuqMfIdQRTPLtQrVaBVEUPan/HjlypN1D8Dya2UsEQYBKpaJurNwh8IRRyRMuhBTm29/+tuo7Ly40HhZSBPajH/1ou4fARbu8iu2s1U0kEjA4OGiZpW8hzUeAeaPyzjvv7CgyFbsYHBz0pNe6k2An+uLWXvOd73yn6/E3QK1WW3DZFXbg8/kUZGuiKMKyZcsUn5Ea2FwuBzMzM3K/WDtoNBquy5qFVNPtBgh5YDOGt1uyq7sPGaPRaMDs7Kyt3/6h7VDHKjWeMCq1hF8+n4ePf/zjpo/3GjplnGbw9ttveyIthEW7DKV2bYi0AuEW2h2FtYJ2Mb62GsPDw10Fu0loRb305LQbMjwQCEAqlVpwTh6ncfz48bYwcHsJCCFFL89wOAxr166FNWvWwM033wwIIdixYwdMT09DNpuF0dFRqNfrLW1HsdDghf1PEAQIBoNNOZ46JfiyUGE3/bzTneSeMCq1PB8YYxgdHdVs+eB1LCTh3EzvRzcNsHYX1bcagiCA3++HWq3mmlLa6UJtIeKv//qvXW9Ev9ChFdmPRCItHQdZX6tXr27pdTsNGONFb1RijOX/WKTTadiyZQuk02lIJpOwfPlyeOGFF9owyoUFL+x/c3NzUC6XFbqxFSMxEol0I4ptRjPPXxCEjq2d84RGrmd8/fznP+8Yw2EhpzN5tRHvYhOckiTBu+++C5lMxrXIsRc2VRaLmeIeAODEiRNNOXbsoFM83WbHSUf4T548CceOHWuL0YIQgpGRERgYGGj5tbvobNCOxEQiAXv37pWdTRcuXFh0+6HT8IquSUh66PdtRY/q1iK3H83ozOFweIODQ2kpvLGCdIAxdqV+zeki8VgstiBqBLwQFe5ujNqQJElm+3MLXjQmli5d2u4htB35fL6l1/OKgmUEs5s3XVP5/e9/HyRJgtOnT7clrf+VV16B1157zfA4L67FLloH9v03Gg1Ng2FmZsaTDkE9EGdhO+Y5L81VkiRP6XGsLmRGP/P5fAsqS24xohupdBGVSqUjjIy5ubmOE+gsBEGQafadPm8XrYETET0vbkh2i94XElr9XrykXDmBTCYDmUwGdu3aBefPnweA9nj0G40GlEolWLZsmeGxGOOu/FzEYNc8xljVGoegt7e34zI6iM7Ujj2nE9lnzTiTMcau66Jd9t4utLBodyunPWMLofZDEATHoiH08+0Ep8BCQac7NrTQbJP1LrooFouwZ88eOHnyZLuHAtVqFVasWGHq2K787IIGr41SqVSCQqHQjWybBEJIs+VbpxtLvb29rs+DeDyuGzXtNOdGF85hURqVgiAsWOW7GdTrddtsmlredJ/P193ouljwWMzNwjsJ4+Pj7R4CAMzLWr/f3/EKbBetBy9aefbsWcAYK3gduvuuNoi+wntGTumGZp+/0w7TFStWuB75lSQJarWapt63ZMmSrmG5SLEojcqu51cbdp8N+zsi1CRJ6qZvddE0vJ7+2goGUUEQumtpgaGreLUf7TC+mr0mTdp18uRJmdDl8uXLcg9LL5YxeAWhUAiSyaSrz8jsuS9evOioc2l6etqxc2khkUjIZEI8iKLYDdwsUnQ1lC5chRebHEejUdu/7Sr17YFWOwgvgG5O7iaGhoa6DrEFBDfbAnXhbTTbC5GWh08++aTiOz1lv1m0e/9zyviam5vzDEOq0zpSMpl0vafxmTNnFHOMdo75fL5F0z+6CzVckxAIoV6E0H8ghI4jhH6PEPqU1rFWvLUIobYLti68Db35gRBqyoPWVeq14WYK6ODgoGvnbhbBYBBKpZLr17n66qtdv0Y7sFhTQE+cONHuIXQB7SGJEQShqWgl3R6HTem+fPmy7fMaQZKkBZFW22g04NSpU+0ehi4EQYA1a9ZY/l02m3VdR2YJg2idSpKkBd1erwt9uDnzngaA5zHGgwDwcQD4vdaBVpR8jHFXse9CEz09PZr93xBC8PnPfx5WrlzZ4lEtDriZ7jIxMeHauZtFpVJpScTp6aefdv0a7UAwGGwqe6BT0U0PW6Ds6i8AACAASURBVLyoVCqOGWerV6925Dxm0c60Wicjel7PEli2bBlcddVVln9XKpXaem9ezE5rFxajw9QVoxIhtAwAbgCA/QAAGOMqxrgbD+/CdVy8eJHbJB4hBFu2bIHLly/DmTNnuL9tNiVpscNNZcMrBCs8tMpz72YEop1YCJGPLpToZhPpIxgMNuUcJy1xAMBWNKsL76NUKsHLL79s+XfVarXtBvPc3Fxbr+8VLEbj2i3JPwAA0wDwLwihtxBC/wchpHBFI4S+gRB6EyH0pktj6GIRAmMMuVxO9Xlvby/s3bsX3n77bU3jpysIu7CDbsTJHrrEQwsTsVism01kgGafD+04ffNNaypUl6m6M1CtViGXy1l2uhFm6S66aAfc2tFFAPgzABjBGP8pABQB4Nv0ARjjf8IYfwJj/AmXxtBFFzIIe2i3gLwLL0Gv19dChyRJgDGGcDjcdeh04Rg6IfJtpom9Huh7tHq/XiGo6cIdIIS677iLtsEto3ICACYwxm/84e//gHkjs4sFDC8ryKQXlNusaF7AYszj71Qs9pRrjDEUi8V2D6MLB8ErP2gltJraewnNpsXRdZQ33nij4rtuOuzCgtWyEoxxN3umw4Ex7livgCtGJcb4PACcRQh95A8fDQHAu25cqwvvwMupbItJce0ET30X87h48WK7h9B2lMvlRVl70oU7aAUTc7tBl3js3LlT3nsFQYDdu3e3a1i24WXdoYsuWo1Go9GxioGbK/n/BYBRhNA7APAnAPC4i9fqwgPwcgpbtVqFu+66y9NjdApdL2UXXTiDeDze7iF00YUK27dvl/+dSCRg27ZtADDfyzaVSnUcm3K7GGW7WT1dsPCCg0MURe825jaAa08PY/z//aFm8mMY47/AGLc3J6YLV4EQstRvtB04fvz4gvdid0LqVyvh5ZRsM+hGnduLFStWtHsIXTiMTjck/H4/pNNpxWc7d+6ErVu3ws6dOwFgcbJO2gExxrvoAmB+v123bp0r++7atWstDcXxAbQI7TfJu+hI+P1+2Lhxo/w3xrhjGMcEQfC8AWwX5XK53UPwFJolxGg3ukZlF104i05fU7fccosqgp5IJGDv3r3y516ItliBUaTSDaZohBAMDQ05es4uOhupVAqmpqZkDg4WzTiplyxZYuXwjhVSnSV5uvAMwuEwDA4Oyn8jhDpGgSd07p228bLo9Chcq9GJEYqF6vzoFND9ALtYGHCiPKCd65KNUvLQ7j6FbsDpNjUIIRgZGXH0nF10Ns6cOQPFYhHK5TJXP+zv77d97vfff9/K4e3JB3cAna1Vd9E2VKtVeP755+W/McYdl3LT6b3ULHq+FjUQQh03PwE6P9La6Wg3k2kX7qDZaGU7jUqjOt9cLteRsk4Lbj1rSZJgfHzclXN30Zkg+221WlUxswuCAPl83va5La7JbqSyi8WFubk5hVHTaRGVdpHZOJl61elGcSthhwii0yPZXXTBohOj9W6gWWKYdhK+8RTbkZEROeo2Ojra6iG5ik7JyOnuFwsHGGOVfiVJUlNG5WJBR62CTq+FaBZeu3+EkDymrkA1BydZ7mZmZhw7V6twww03tHsIpuGG0d7T02OJTImnUHlNDjSLruxoHSKRiGef90Kb126BZzRmMhnIZDIwMjKiyCBaCCiVSi13oAqC0DTzM60fddF5YLOEBEHoGAdHO+HN3UUHbm2IiURC87t169Y5fj07wqZdtNtamJ2dhVhsnvl4x44drl7Lq4pQF61DMBhs9xCaBqnVMDufWdkjCALcf//9bgytbQiHw91U7hYBYwxbtmzxZMsJjDH09va2exiex+HDhzW/y2QysGzZshaOZmHi6quvttRDGCGkMnx9Pp/ndLbFCLuGPdFtCSRJ6pajmEBHaeoYY1daJgwODup6IL797W8r/nbCyFwIwqanpwdWrFgB0WgU0um0q145r6Z6dj2R1vCrX/3KtrfPCYHe7vdVrVbB7/ebMioDgQCcPXtW8ZkgCHDjjTe6NLr2AGO8IOShHrySdnr58mW4fPlyu4ehCbaOqQs1rr32Wt3vF2MbHKfX18TEhCUnppb86rSyoIUIu3v+1VdfrZhX3cCGOXTUU0IIuaJ8/M3f/A2cO3eO+10kEoFUKqWYUAstUsCDXuSWxvT0NITDYYjH421X2NuBha4MNwOeEG7G2+fEs3brfVmZ++Vy2VREJhKJeMYYcRNzc3NQLBbbPQwunFIKg8GgZ1KnTp8+3e4haOLChQuadP5OYaHvU61o7WWx557rcJqYaGJioum9QpIkzzrDFxr0DD677+DP//zPFfOq+y7NoaOMSoyxK83r9+zZw91oQqEQRCIRAPhwQkWjURgYGFjwGxMb+udhZmYGli9fDslkEgC6npxOQavmriRJqjnhRqaBWTRrIAiCoPnsrMz9SqUCyWTScDz9/f2qjSwQCEAulzN9rU6AlzdrpxR0jLFnUqe8/LwxxpbSDq0iHA67kkbfyojUq6++2rJr8SCKIjz11FML3pHc7HrtGpXm0azz1I3nvGfPHsfPuRjQtQIAYHx8XOWVCofDXAW4Uql4ngHKiQ2OTbvjAWMMU1NT8t/tYlTtwhpaGV1lhX07I1LNerNDoRBs3bqV+51RShoNotSaMVhYlslKpQI//OEPTV+ri+bgBMun13r4+v1+xd6GEIJoNOqZWjw35VOlUnGFuXXZsmUtyyqg+0MDzDO/ZjIZyGaz8mduRsUDgQAkEgn40Y9+ZPo3XqzhJdDS9br6zOKAlkOY127m+uuvd3s4HY9FY1QaGVqsx+3GG2+EtWvXqqJw9XodRkdHPZ326ETtaa1WM3Uc3cdtIXstOwVmIsyJRGJR1no4kc6UzWa5pDJWoh+kgbKZNcaOWZIkeOONN0xfyw7cmBt2lFwvZD44Jee91Dewr69PIbcxxlCv1+HSpUuOnN8L700LbkQ0BEGANWvWQCgUagmZ2Ntvv634O5PJqJx1zTRpN4tEIiHvN0b3ffPNN7s+Hruo1WqenrOLAe2qpeYRLBHwuFM+//nPuz2kjseiWUlGXidWeWCFNP39wYMHnRuYC8AYw6ZNm5o6h1nPIm3EeNnQXgwQRdFU6lhfX59n6rs6BaFQCOr1OszMzHDriV599VVTdciBQACy2SysXr0aent7DdNpWQOvFfVSGOOm6fQJwuEwRKNR3RpS3jMQBKHtqWNOGQhek4uTk5MqI7dSqTg2zlQq5QkH4+rVq12NkJF7lCQJarUalMtlx6NbvOfYaDTg5Zdflv+mI5TZbBay2ayr2VR01H3FihUQDocNI/F33HGHa+NpFt2IZPvRrgwmLZmHEFIRdAJAN0vIBBaNUWkE1lP18ssvKyIJ9OQzG8VrFyRJgnfffbepc5jxWiOEoK+vr6nrLDS0k1jlX//1X01FRMbGxjyVjtcJmJubg2AwCMFgkGvYDQ0Nmar3DofDUCwWQRRFSCaTKqWRGJEkKspueq16b06lCJbLZahUKoo0eRYYY1X018igbMU6WwwkSU5DEAQIh8OwcuXKdg8F9u7dC6lUyrXzx+NxuT3QhQsXQJIkxyPSGGNuFE2r3iuZTEIymXS1h3G9XpeNVpJKbeSQ+MEPfuDaeJrFYsjaMXIiL4ZnYAWBQIArO7QIPbv4EF2j8g/gKTHnz5/nHus1zzMPrfC++f3+lkROvACznvd2RgDj8bipzWFubq4j5rCXwKbCsOnlGzZsgHK5rHuOj370o3Ik+eDBg1Cr1VRyh163yWSyLfOp0WjAtm3bHFM0zMiiubk51RrTS0lzWnnnRSW7a8Q6SNSu3bwDPp/PVLTdbtqjIAjQ19cHGGOQJEmRTtwKGK0pO/uylWcxOjoq/9uMAet2yn4zWAzr3Ci9tF6vw6ZNm+Cmm25q0Yi8DS/IsE7FgjYqzbbF0IJbG4WdjUzrN+30ptNRk4XGSMnCTK0iwPx7akd9AJkff/u3f2vqeC/VeHUC6DZCtVoNKpWK4nszpBXnzp2TFRhJkmBsbEzzWGJgtSOi3NvbC+l0uuXpp6xTpJXKHvs+AcDQSeAWmt232o2pqSnu82wliJOATg3lYWhoyNb5w+Ew+P1+8Pl8rrU6IzC7Dun+o1blRiAQsLTeDx8+DADzz5c1YDstwk8izJ/+9KfbPRRXEIvFTGWeFYtF+Mu//MsWjMj7EAQBRkdH5e4PXZjHgjYqV69ebeo4n89naDQ4WSNiR1nT+g3GuK3RQpIKTHsuOxF671cQBNMNpefm5lxhFzQCMSo/+9nPdlNZHAZCCAYGBqBSqUClUpFT3WiYMdJpj369XodqtarpLMIYQ61Wa4vxjxCCRCIh10G2Yj7V63WVE28xRBB4sNuqwStkI4VCAZYuXerY+Xw+H6xdu9b0PNRr/UNDFEXYuXOn5vdmzhEMBtsyT2l9JZlMgiAICmPOaobDVVddZel42hhn61Y3b95s6Vxm4GaNLsYYNm/eDN/73vfkz8yuJa+sOT2sXLnS1HzIZrPw7LPPtmBE3ke9XofDhw8v2j2oGXh/RTQBs7WPGGPDGhBaeLQ6JQ0hpOn9QwjBjh07WjoeGnNzc5BKpWTPZSciFAoZCo8LFy6YOhdCiMsapnWsU6CJUEi00qxTRQ92x/hnf/ZnTV/bayD1lE7UK4miaOjImpiYaPo6dlAoFCCXy0G5XIZwONwyeRcIBBwxor1AEtMM8vm8LWWm3cRGBH6/H5588knHzrds2TKIRqOGjj8CSZJMRUr15pogCNDT06P5faVSgWw227aMD5qQjaTRY4xlop6BgQFL57MaXUyn0wDwYc00STUWRdEVJ7ebyj3PWcFj+GYRi8VawvbbDAKBAPj9ftOZAy+88ILLI+oMiKIIQ0NDqlZhnb63tAIdb1TqeYr0yCF4YPt09ff3y8W6tFBrdXqjKIqaQnXbtm2ygG8HLly4AMPDw44T9pj1NjeLRCJh+D4lSTKdX1+v1+HOO+80dSyZu4IgNNUjDiEkt74BmI9WvvDCC46k79l9B9/4xjcgGo1ajnKZUW7MpiI7BToFxin6eVKTpQenWjxYRSKRgNHRUSiXyxCJRFqSgisIAmzYsMGRZ9su77JTUYtW1+ex0FrzZmUBMTLMOtd4oNd4oVCA48eP62aLsI4PnmHDjh9jDKOjo9z2W2vXroX+/n7NdxoIBCCZTLYlKwVAeS/E4VmtVmWiHquGHc0mawSfzwePPfYY5PN5OXuDvBu31p6b2RK8MRs5ZMm8MApctDuSuW7dOkilUqaNX684pgjaZcRJksTVq1s4no4NkXa8UQmgJs0gMLs5C4IAY2NjKsH11FNPwfDwMJw8eVKx2My0bTALLSWarqu55ZZbNI0OURThpz/9qWPjsYprr70W7rnnHtORPLMIBoNw2223aX7vlLCWJMlRpjyfzwd/93d/Z3icKIqyMRsOh5uKKmrNIav3xTNu7W4yTz75pK02BYFAwFCBuOKKKzS/04su2AX9DOr1uiMkWDt27AC/3+/J9JpYLCa3TbIbNTMCmx55/fXXg9/v95wn2Ox49Bx/VuFk6qgdaN2Hlfvbv38/3HfffQBgr8aOd63JyUmu3A+FQirjrlKpwMjIiKLOkOeMOnz4MPec2WxW12CoVqum2J7dwmc/+1kAmOcyIHpOvV5vCTN9o9GAo0ePwv79+2VG7MHBQQCYl5VOM+4Gg8Gm9ns9A5t1KtNsvjzQEfOVK1cqjDWtudnOcpQzZ87A8PAwDAwMGMoyn8/nqfYqkUgEYrGYaRlM9n5BEJrO0pIkCeLxOPzyl79Ufd4KVCqV8ZZcyAV0vFEpSZKmUWnWW0fqm1asWMGdwI899pip89hRaHmTNBqNKqIYepHIyclJyGQylq+rBStKHTn22LFjpmsOzWJubg7S6TREo1Fu1NIpBU7PQLGjDDUaDVNRzXq9LitClUoF/H6/bi8/PYiiyN3IrUYKnBSY4+PjckqWFVSrVcN1m8/nNecp8dbzYPf50u8JIaSpJJiZL+QY4jDxIhHABx98ICsXGGPHDb3BwUHVsyL9O72k1ABY20OcSoVz0mnpJL7whS+YvsfDhw9DPB6Hj33sY7Yye2ZnZ1WfYYy5+8yGDRtUn0mSBJlMRjGfVqxYoSKD0iLqwRjD1NSUpkys1+swPm5e73OavOb2228HgHkuA1rGkuysVhiXhw4dkq89PDwM0WgUIpEIfOlLX3L0OgMDA3DzzTfb/v369eu56azhcBj6+vrkVGYaPIcsmfuE8XdqakqR/szOlZ6eHkAItZXzgjhXpqenDTN8nGT8pmF37pfLZVixYgVs2bLF1D5J94392te+ZuuaLNrlEBAEwb0Guy6j441KAG0DwwoDXSwWgwsXLii8TYR8ZnJy0tQ57KSdLFmyRF4MZPPt7+9XnacVisamTZsseXh6e3vhpZdekms5nFQ+McaQSCQglUrJxglJcXIywjM+Ps41Nnw+n6teKYSQvCE1Gg3IZrOwatUqW+dZsmQJDA8Pq757/PHHLZ/PqXlGNl5JkiwZTo1GA6LRKFdRJMjlcprvv1wua87DPXv2wDXXXKP4zMycxRjLfdZCoRDXI03SN83iwIEDkEqlNB1iRnAzrWpmZkbxfJ0w9IhS95nPfAb27dunmmevvvoqJJNJz5FMmUn9JXKJluXNwIvRa4B5R4jZWj1JkmD//v22r6U1D3h94vRkFm0Ev/fee3DffffJawchBOl0mqsnEOIovXXWzn7VBw4cAACAI0eOKD4nUUunM4d4wBhz18czzzzj6HX8fr/sYLYjL8PhMOzevVv1eSQSkRl8P/jgA3jrrbfkSCVPj6tWq4qIOEn/5cHn80EymYRKpQK1Wq1tcg0hBJlMRjbQtObz6tWr4etf/7rjDkRRFG0724hBvnfvXlPjouXAv/zLv9i6Jgs600ELbrAd+/1+7WiHx7EgjEqtNFcrk5n0mqI9T8899xyMjIzo/o6eUGfOnFExofFAPFgAoEgRJAKLrQUdHR3lRp3ohdaskhkKheBHP/qR6ZQeQRBkwwHAeUWIvjcikPv7+8Hn8zm6mVerVa4xt2TJEleVO/rckUjElkOC/i0PiUSibUQC5P4EQYDPfOYzln6bTCbh8ccft70RazmBDhw4oEotNPuOX3/9dWg0GlCpVLj3Y1bZIfLl4MGDcMcdd2i+OwDQdTJgjGHr1q1tr9kxC4QQRKNR2fnBOnI+8YlPAED7a5BYGK0fhJApAhknlbVmnpHP51MxhZrFgQMHLMmoX/ziFwBgfm0QrFmzxpKMN+v0lSQJnnnmGTnqFY/HNXtZ+nw+y202tCCKouOEPocOHQKA+egSDRKNslPSwZtXW7duBYD558GTxzyHE5sy2AwEQYBMJgMIIUilUrbqu7PZLFx55ZWq8RO9kRiGjzzyiJzOy6aL9vf3q5yjGGPN/WPJkiXg9/th2bJlgDFuWwYG+0615vNHPvIRSCQSuvuRHdjJVqJ/m8lk5MwvIxlK3xvP+aSFcDismhskU9BMtpdLHA/eqgOxAG/t4A7DbKRyyZIlsHbtWojFYorJ1dPTA5lMBq6//nrF8QghiMViEI1GYfPmzXJ6ZjgcNizcDwQCir5WvM0ml8vB8ePH5b8PHjwI9957r0pA0Btis0rLihUrIJfLmd6MBEGAixcvysKyXq87aoTxzlUoFFypmSPeShq89CungRACQRBsR6vMpJWR2huzcEOpv/vuu1URQi3Q68/pjfjQoUPwq1/9SvW5medYKBRkJ0qlUlFtQsVi0VI6nCRJMDo6qttH7zvf+Y7md8FgEPbu3at7DT3W1lbXLl66dAn6+/shHo9zHXUnT54EgPb05dTD2rVrdb9na6ZYuUU7V5xAsxkUJHtAa27ojfO5556zVGpB7t2qojowMKCpzK1Zs6YpMq/x8XG4/vrrQRRF+TkGg0EQRVGxJjDGslxulrjIDYOCKL10aQxCSP7c6BnxOBo+9alPqT7L5/MQjUYhGAxq3ke5XFaUfDipB0iSBMViUc4aa8Y4v/HGGxV/BwIBxbgvX74sn9/v98v1fF/4whdkJ5jZ69P6Qzt7QpM5XqlU4NSpU6o5S7IrSDDEKrmlmes3QxhI3r0dfgY9kPtOJBKwadMmlY5PMjJIXbgWenp6ZPkWCoVMsQYvdCxoo9IsHnroIQAARXoALaB37dqlON7v96saVGOM4dFHH5WNTC3s2LFDEabXEji0clWtVuFnP/uZyitJ1102K7impqZgdHRUNXYthbter8O6detkhUoURdvGEQ/kvLVaTd7M8vm8o6Q6NNrR+DgcDkM4HDZUurSeqxll5Wtf+5olA8Lp6Cxhzv3Wt74FW7duNWS5JZEQN/qe9vX1qdYQgHWF/5e//CVs2rRJ9VytRFdIHyytdy8IgiZrH0JITrXVizppGWi0UysWixnev1MGKDFIMpmMKsUsm83CBx98oDjeC03Uz507pxudo995LpdTfCcIAkQiERAEwTHG8GblPHnv1WqV+3z1Unh7enqgWCyavha91qyssTfffJOrzAmCALt37zZ1Lr0WYc888wz80R/9kcJhEAwGVe+IOKW16orNyspmjVIeiPKfSCTkvTIej8tz1UgW/cmf/Ini70gkAt/85jdVx01OThpGCImDjMCJVlYsDh482HSGEtuTdMOGDaq2TXRQgNTzpdNpqNVqluY+wPw7aDeTM+l3HAwGoVarqeYsMdYOHjwI+Xxes8a4VeCtieeffx4CgYBtxxzvnJVKBT7ykY/IOvT999+v+P7Xv/415PN52Lhxo260kuzT0WgUlixZ0vJ2g16EJ4zKZr24zfx+3bp18Kd/+qcAMK/Q0qkxREAnEgmFJ2PHjh2Qz+ehWCwqPDtPPPEEAPA3G4QQbN26FdLptC3CkCNHjqiEIi3kmlX8tm/fDkeOHFEZKlpKTCKRgMcff1zRFsNMlMEK8cWpU6cUtSEYY83fNzMHMplMyyMkPp9Pfmekt5jWpqnl6SNeXD1PIEvkQEDmPAs3PG2jo6OQSCRg7969hgrH3NwcZLNZS31Pe3t7Tb3/qakpLumV1XcvSRKcO3euKQOc9MHSAonI89YfLZuIMmwlVZiWFYVCAa677jrd450yiOjnzDOm2ahto9GAjRs3OnJtOwgEAoZOn3q9LqdPsfOBTv3yAlU/LXNITTed7SKKIqxfv15zXltR6gVBUOxXVqKVgUAA9u3bp1LQhoaGIJVKGZ6rp6dHXh882aiVUUBnNrH7GclGoteOWQVSj01Xb93q7enbt29Xnb+vrw9qtZqpaDKbscEahgREoQ4Gg7pOngMHDkClUoFGo8GtX2wWkiTB+fPnHT2n1nwOh8NQq9XgzJkzsHv3bojH45bSKQHmGZwvXLjgifrosbExqFQq0Nvbq5hvdIkPef+33nprS8aktV/zHOi1Wg1KpZIsQwVBUEUW9cDTtzHGsGnTJoU+Sa83uiZcL1q5du1aGB4eluXSmjVrbBMCLhR4wqhs9cKLxWIQCoUgHA6rPBSkIJztIbdr1y4YHByEwcFBuPXWW2UPVC6Xkyf7+Pg4lMtl7v3E43HYu3cvxONxTZZZPUiSBAghRV7/mTNn5H83o/gJggDpdFrF/iWKoqbgJeO5+eabASEEO3bs4N4TKzzuvfde0+N64oknVJ4+LQOgGaWtWCzCG2+8Yfv3sVgMBgcHLdUb0V580lvMTFE4DXLP9DxgwRI5EGgJymKx6HhqJPGCAswzf+opYxhjSCaTlvqe/sM//IMpGbJ9+3ZVhgGAvfS0ZiPmZM0B8Dc9kqp1yy23KD6Px+OKZ1OpVKBcLsvGp5noHrtWxsbGNNPKE4mEa/LZjJHidI2PFQwNDcG//du/wT//8z/Dxo0bNdfF8uXLdTNOMMaO9IxtFj6fD8LhMCCE5DlP90IlLSm05hCRT1rPQRRFeW0PDQ1BPB6HVCoFqVTKEgHYzMwMHD9+XBGlRwjB7bffDrlcjpsyvmbNGvnfZI1ns1nuvsBGHohzTq91EEIIBgcHFfujU45Ireepte4IcQ0BIQ/z+/1w4cIFUxE1SZLk9yyKIlx//fXcxve0fqQXJScG8urVq2Hjxo2muCV40HoWhDjJCshcJDJkdHRUcf6zZ8+qfiMIAiSTSZicnIRisQgjIyOWyoIIKpWKa1lVVjE3NwfBYFBF1EPPcZI58+yzz7ZkTKTelMZnPvMZuSRID5IkqbIHeVi1ahVs3bqVu47I/RKHPoB6vZGacKuOTTuEiwsJnjAqm4XZKNXSpUthcHAQVq5cCZs3b4b//M//VLHZJRIJCIfDMD09rfp83759sG/fPjhw4ICm8Dt9+jT3c1oRHBwchPXr11tOSxwdHVUpwKdOnYJsNttUpI4oAOl02hSxBEIIZmZmYHR0FNLptJwiwtYs3HDDDarfvvPOO6ajYePj4ypjWRRFS2lxraDzXrlyJezbtw927NgBAOYIonbu3CnXGxDl2qo3lIDUS/DAowlPJBIQj8cVihhBM4X1WqC94MPDw7oOEDJWvdqOQCAgG2KxWAzi8bipYvl0Oi3X7rFgx6QVydWDldSXHTt2yFEidhMSBEEmtEmn04q1+Mwzz8D09LRCsSYOnnA4bOvdTU5Oav6ur6+vbQ3eAaw1ZXcSS5cuhZdeekleV5FIRCYtYaEXkbBaax4MBl3zdBOlTY/Zl22TQIMYXT6fj0twgxCC/v5+iEajcpRyeHgY5ubmbLFK0wavIAhw4MABbuYFSYsl64QQ9yxfvpx7XlYh5RmHPp9Psc+Q50RkvFFmAF2npgc7PRhvueUW1fOXJAkmJiZMGzL0PCDXZw3wdevWyfoRaafEnoMAYwyCIMhGmBXmfRrr16/XHbMVkPdKZOWRI0cUc4c3xvXr10OtVpPn68svv2xI1ggw7+yjHf7VatUW0ZyRbmPnnOT90pl4AABXX321onxpaGhI0wntNG64Vf4eXQAAIABJREFU4Qb4x3/8R8U7HR4ehkqlIu9nNFhjEyFkGK0sFAqwd+9eLj8GuV/i0GdLFwD0g110lhkAQKlUgmPHjkGhUID33ntPd1wLHZ4wKvVeniiKIAiC5kYbjUYVZCR6gqdcLsO+ffsMDQ0y0bRw5MgRzcgYz/suiqLimsPDw/D444/Dli1bdMfB4vDhw6oN8PLlyzJ1tV0QBSCRSMiRx3g8rrnhEdazgwcPAkJIjsDu3LlTkQ576623qp7TwYMH4a677jI9NtbDX6vVTNcVabV/cAvpdNoUI+e6desgHo9DtVoFSZLg/fff59Y7WAFJveaNiX4HgiDIDg4t5kOnQbyCAPORfT0Fk450aK3ldevWwapVq0AQBLl2iu5fp7XxxuNxePLJJ1Wf9/f3K5RrQRAMI+o8A9JK5IKONLA9Sun3lUgk5GsRZwAtn8hakCRJpsO3ClEUue+ElVvNgn1mVlKYWgmfzweVSgVKpZK8rohHm03fFgTBsYhEKBSC/v5+1zzdvD2ClTlG0aBoNAqiKMI111wj14QT4ymZTEIkEoFUKqWQLa+99lrTY280GnD48GFuicbQ0JCCiZNE77XaatDspLlcjruf8D4j6fPRaFTh2OIBY6xZE01AyIKs1MkODg6qUvjZtF0zaDQacjrvjh074NVXX1U9VzpKSVhRadDka3Q6fzP18HfeeaemYWW1VpPN7GIdrISgiUAURfj4xz+uMEIwxoaOLYQQPP7446rnZyeSbbT27chjSZKgVCpBNptVzJ0TJ04o9DWSrdYqErdEIiGXXvT09Mg6J+v4AlDLqdHRUdi1a5cqnZcG0Ql484nOFCLnY3HttdcCAKgc0Qgh+ZxkHz5z5gxIkgRnz551qtSh/XnTNuEJo1JrEgeDQVkRWbVqlUqIx2IxSKVSiv46eoqQVaWdDo3TsNoklqeAmq0xIzCqwbLbOiIcDisUABJ5JCyXeguErcNIJBJyWufQ0BDXw9doNOA3v/mNrbFaRSgUsm1sWxGspNaDvFOjYvf7779focw0Gg04f/68LrmEEbTqhBKJhGLdhMNhxfpoRcsReu4a9a4rlUpw9uxZSKfT3M2A1Ln5/X7Fvfj9fojH44AQgs997nOq99ff3w8A/Of01FNPKWSDJEm6TK6CIHBTZs0yE0ciEZVBzzIvkr6YAPNMdLQzgAb9jCqViipbwAy0lJ9Go6EismgGqVRK8beZFKZ2gO6/Nz4+DqdOnZINebZeLJVKORJZDAQC8F//9V/ynCY1c072t3v11VdVBgy7TgjjJQ+rVq2CVCoFmzdvhrvvvhs2bdoE69evl5/VuXPnVE5VO6mDWrjqqqtURFuiKMLOnTtVzqIzZ85oXvfw4cNy7aFeFIp9VrlcTu6dHIlE4FOf+pSmEUfkklZfT4QQBAIBzSwAXjbPTTfdBPv27VPJDjJHZmZmdFP52TZd27Ztk7OMWJ0mkUioxs6ee+nSpfI5aSWdZ/ibxQ9/+EPYvHkz9zuWyMsIJN2bKP5sJtbatWsV7++b3/wmDA8PW84Y8vl8cODAAV0jMpFImCpRyuVyrvVGTSaTCqfRuXPn4JOf/KTsWCDZaq3oqfnSSy9BPp+HXbt2QTQaVQRxWD0dIaSqWT948CDMzMwo9Bde1gUAn+mYzhQCAF0OB1a2IIQUAZcnnnjCcU4OjLH9KFGb4WmjkhTLS5IE09PTig2AjlLQ3vxbbrlFTlFhQZQynrG4evVqmJycVFBMa0UsaeFkhEQiAdVqVbN1gF5RO30N1rPiFL73ve8p/iaG0dq1ayEYDOpGs+gIFMHOnTth69at8MUvfpGrmBOPMw8+n0/lqbNbmwHQXFsQK4Q1hUJBMW/Y90Qr/qFQCAYGBlTGVaFQsLxp0tDbCOgoHguzzcybAWkyDgDw4osvGh4vCAIkEgmuw4V20ASDQcWapdn62FSZp556CgDU9VSCIHDn+COPPKI5vqGhIa4hYbZujrcB3X333Yq5TrcUYg1oGqxT4Itf/KLqGNapxZO37LkJc6lVryt7bjL3Y7GYfA0if2kCNC/VobD96OgsgI0bN8rzcs2aNSqnnFWEQiE5tSufz8s1iESO0imK0WiUW1JAgzxjniKFEFLNPVZuxGIxTecrLeMI6NRfSZK4PZadQiaTUaSDI4Rg06ZNEI/HVXsNISbhYWhoSK49fOWVVzSvxzO4CZLJJNx9992ykctmHd13331Qq9U0y2Hi8bhumwTeWqfTgWnQhqnePsDqEzt37pSzjNg0e9pBlkqlIBwOq4zsV199VZYttJJOG6g0OZQZaBl0Pp8PrrjCWj94tg6TzsRav349bN26VZEOSgiOrAYfiB6kpxOGQiFTZQTbt2/nMpQDzDuE7Rrr5J4ee+wxxecnTpyQ90yA+WekpT9bhZ7uRgISxEmjR6zl9/shEAgo3kutVoM9e/bI9cPRaBS2b9+ukA3EMcfqP+FwWL5fshfxHLavvvoqAKgd0eye+Otf/1rzPrVAk37xnhNCqGNpZD1hVGotxu3bt8uMVclkUmE8Dg0NKQTvwMAARKNRSKfTkE6nuREYIrB4xqLP54N8Pm9qE6SFkxH6+vogGAxqptPSSgoPpH8W61mhv9cyWM14xoxqx/RIhXjRU2KU6tWdaimqkiTJ7V0IvvOd79hOYW0mnfTy5cuyoosQMoxG0EYiTfMeCoVkj244HJZbQfCMq2ZSDev1OlfpI+clz7BcLsOxY8fkWtzp6WnXeyvRzgkz7ySfz0Mmk+HOa0IWUiqVIJ/PK9as3++XlSRaAaKbnLMERaSGh1WYeGQXRLHfuXMn11A3663UUgxopYN1cGmhVCrJYwsGg1yihf7+ftXcYtcma0gR49tKlIllxwQA2Lx5M2zduhUSiYTcyJqWv7t27YKtW7c2tc5psP0G7YAlzGKVit27d0MkEoEHH3wQstksnD171vY1fT4fxGIxmJubg9HRURgeHobh4WFZjhI5IkkSpFIpudbWzHnZfeXKK69UyF6EkCqyrefY8vl8cu3QW2+9BZlMRhEdq9frCiKdXC7HJX+xi4mJCUgkErJxt379ehgcHAQAtbNIrw3ArbfeKs9rvVpidv/k7VvE+GedIm+//fb/3975B0d1XXn++1rdAiEwRo3BFjbCUVNWHIktG8cGx0CQEGBBJTPJTBKbXW+Nu2oqIlVrU6nEpLBdqdjGLjKudbIxVLLLJrtBzmy2POPdjL0mARJSScq7sSvjHzuZCihr2RgsTVrIxgJBS3r7R+s8n3ffvff96JbUEudTpUJq+sft9+6P8/ugv7/faGhasmSJcc6sXr1a2x/1pZdeCjzGQ/N0uWgc/n02bNjgkyfUxveUlwqU0nWuuuoq1NTU+JTWjo4On9xFcKNeTU0NWlpaYtVB0CmWNTU11p6+OtLpdCDXniKxHn/8cXR3d/v2eSoopwr5Ya3SSA6yGeD49bSxY8cOb05xAxIZnpK2WVLzjYkzZ854ZyYfQ9x7puPhhx9GQ0OD9n24Q4I7eXTXUFcE0nVd377suq7Py8qLWalh8LW1td73pbNIV8OBPlO3l4yMjGB8fBwnTpyI3WqGxuu6LmpqarRyQ7FYDCZ5zhCqQqnUTSTybjQ2NvoUROoJo7bXyGQyXi5HNpuNFfZIhx/lCVKIgSn8FSgtvBtuuCH0vaMoCiSk8MWXTqexYMECr6y+yUs5Pj5uzKkkBcZEFKUzk8kYwznHx8eN41KT4qPguq6vLxC1ezFZ7jiV6GnHBQMqOZ1KpZDL5XDNNddYLW9UKYy/HoAXYuO6LubNm+fNB921MVV/jRJe5ziO1SBCa4yanz/66KPehhrW4D0JfG598MEHXsETnv9swnVdPPHEE8ZDure31ztQ1DVLcAWFH5h8fjmOg/nz52uT9HXU1dV5ewxX1ImoRgGTN0EtaU738/Tp015OjEpNTY0vl1JXaGHevHnewUz5Kur8e++993weuHPnzkXaQykUqK6uDgsXLvRaLxCk6P/pT3/C8PBwwENPylNzc3NgnSdZ03HLzetQFQRVqMjlcnjuuefwkY98BI2NjWXlQ58/f97zpvAqyUDpXOICx9tvv41sNmu9LrQOampqfFEwqVQqIFBmMpnAPbZFd5w/f97LHfrGN76B4eFhZDIZX7EPrpz09PSU7enm0H1YunQp6uvrPcUACBqLvva1r2kLkaXTaTz//POhBoxz584FnvPee+8FDD00f8mrQRw5csRqkOH9l1WoNUgU1IgK2oPUUFf1u6hez0Kh4At1V4u/nTlzBqOjo777uWPHDp/cRXCj+5YtWzxPUxSuueYa7XXbsmVLpLm0fv1677NSqRQWLVrkGbNobFyJevXVV73X0p6rGhipCJEJiiKrRCoJya9PPvlk4JoNDw8n7lMbpzsAFaUsxyi/YcMG3HTTTXj66ae9/SqVSgUKAwGwFssBSgpo2Fl06dIlz8uqOmHU+aTb43QyLskqukr5NBfLreqtO4sB4NKlS/+iefqMoGqVSu5d4BSLRVy4cCHUgm6yVOqs//zwo35Ivb29uOqqq4wexmw2qz20OFE3UhJSeHGQVCrltSe5dOmSdy1MITm6DU2tYKtiE05Onz7tLeR8Pm9swm4K/YqiCJp44IEHMG/ePK9QgMkazKGkak7c3n3qnHr55Zfx6U9/Gq2trd7hqXp0CF0eAFCyaJHQODg46AkLOuXKZBG98cYbQ8fvuq41L0A9FE+dOuVtiJlMpuK5lWqoD4UQ8mJONvr6+owH6PDwsE/oKhaLGBkZCeTsEWrpeDokaBxheZ4APE8zfUaxWEzsWaYerAC8cEd6nD+H7icVQNHtRaTQ0f1Tc6OWL1+O5uZmzwhmEhSy2azPA6ca89TPpM+gSJLvf//7WL58eaCfXbFY9OXVHT16FG+//bZWQc7n815ebDqdTiRArV27NrI3z4R6jdS2Uypq/7e4n6X2iiPUSqc0X6MYZhobG5HL5TyDVEdHRyA/9tKlSwFlKEyQJCWXrPO8Kqia95skt851XdTV1Wl7z9J90Cky3Fg0Z84cL6xfF44dZVybNm3SKnYHDx70rVmCr7t0Oo0lS5ZY9zlb3t4777yjbXehkzfU9zl//nyg9Ukmk8H4+LhvT37ppZd8spBOmaI9m69fkpOoaJgJXhWeejBHkYfuvfde7ePbtm2LZGS6++67PYV22bJlePPNNzE8PGw0uPJrQHsu7wEKlHJZbfIMKTCVTCUpFAo4fvy493e5FdlpLqoGN1s4fTm54ry4HN2Pjo6OQGEgju4ekTEsbL3S//N5Z/searV8dSy8z24ulwtEc1WqQv74+Lj2u82ZM6d68kFiMmlKpeM4NY7j/M5xnH+I8zoqxKH2YiMhhKorPf7449rNnTD14evp6Ql4IPkhMzo6iqGhIQwPD1vbGgBB75TKpUuX8P7772N4eNjaS5DYuHEjtm/fDsdxsGbNmkCxCMBvcXIcx7tOOgHQVFKdsFl/Ghsbkclk0NvbC8dx8IMf/CDwGTYlT83R4NjCSQYHB32eAOBD66Ktaqw6tij9jjibNm1CR0dHILyHwtEINTyX4EptoVDw5hNv1+C6Lk6cOIHBwUHk8/lAI22T1eutt96KFF5n8iib+rXxENN0Oo1Vq1ZNWigseRZ5MSfO/PnzrZXcbLiui3fffdd3n9TS8Vx4yOVyWLVqlbeWwtYxAK/qKn1Gf38/Ll686I1z2bJlVu/C/PnzfeGhjz76KAD45pfqXTDdT77vUQ/Eixcv4uLFi77Qs9raWuzbt88Lp7QZwRoaGgLrDtAfyKOjo6itrUV9fT3mz58fENq40jkwMOBT2mke6hTkbDaLp59+Gq2trdi4cWMiRa23txfZbNbnfdf1JrXx7rvvensbb6tgQu3/lhQ1R131OpNxymaYUR+nCIt8Ph8wtFaqymNjY6PPK0XELWhHrFixAgcOHPC99tprrw29Dw888ABSqZRXlAtAIPxx8+bNAQWQ1gVdDwqd012fn//854EzAfCHfKZSKQwMDFgFYVvI3OnTp7VRKw8++KDxNRze3gooGXZSqVRAady/f7/3uy7CgfZs1ehWU1MT2keYewTJQLV8+fLQOXfo0CFtO4nnn38+kjHl2Wef9RSLFStWePdAjQIg1F6oHR0dngcW+HAu5PN5oxGRFBJeLM4Wfm2CK9269nHlQK3L1OJotmJpSfqpE1SIB/hQ0cvn8z4PNhklSB7XzcHGxsZIYc+0nlVPNH0PFbVaPk9ZAj5sswegrBY5SUmn0+E90qqUyfRU3gfg93FfdOnSJaxcuTJQobKxsREnT570bm5fXx+2bNlitEpzyyXnZz/7WSCn0pRYzj1LUVEXISmTUd3ktABVSwotAh5C4LqudbKfOXPGKHzU1NSEeqfGxsa8kLVsNht4L1vYIM99Ua1hanEgIiyEU7Wqc3iZeKB0baiymW2Dp55r+Xw+UB1OF9q7evVqrbeSX0v+HVRlbnR01EtQ50qDLTn+1KlToZYxWyGnxsZG7Tyg0GmOLXSQH3qZTMZ64KihwvweqKHrQGnd8/czzWvTZ6pCDr9e1OPVRFhYVSaT8b1/oVDwBPyRkRGfB9M05oceegjNzc3euE6dOuVb44VCIVCB1nQ/uVBLhcyA0hxUQ8/44WpTnnWeEUBvOBobG/PaX1C+yuHDh7Fs2TLU1dX5rK+Dg4OBz7W1rSCh4N57702kqJE3bt++fWhpaUFLS4s1f80EhauTd8xmwKT+bxQKnBTVkKAaX7j133RtrrjiCtTX13tj5V69L37xi77nplKpihRIGhgY8HmlSAiMU9COQ0WcqIhKOp32KVSmtJRcLofW1laf0XLp0qUBBUFVAPfs2YO2tjav0MfWrVuNZ6vN08/XHTdQ8uIhUTDlZuuUarViON3Prq4u7zE1bJXgrTJ0hj5SttX1OzY2hrfeeitSzjdn3rx52LZtGwBzHY2+vj5tO4kjR45ozw2Vo0ePensItb8BglEABDc+ua6Lbdu2+cIoqSeoqYDNzTffHFBgWltbsXv37th7AT8jk6QPmSBP9YkTJ3z9HdevX2/1Nre0tCTeH/j15oqezpNI8rhuDkZNTdEVJiN0EXu6IpI8bJrPNV1v3EpRibStamNSlErHca4FsA3Af4r7WpuHQi3ta+rNR+i8lTrLKT9kePy867pWb6VuQ1ffnzZzW4N6Di1ANVSJLwKucFN1OV048NDQkDFsgzxjpoOhWCx6h9vRo0cxODjo6x3Iq2uZoNyXnTt3BvIkdcpLWAinrTGvzhtBlc12794dqHzHf9+/f793cJAgo4Zfc6FS563kCm9YA2Gymubzea/XW9zKvrW1tT4lj1vWdETNbbrnnnsCBz4ZMpqamrxN8JOf/KRnNNA9X71GPIQwm80G1nltbW2grLhurZo297CoAtu80r0nN7ps3boVALzcHPWQ2bRpE15++eXAe9Ba2b59O2666aaAYYm8lUAw9CfK/eIe8dHRUW9MuoMb0PfeI0yCrCkfamxsDP39/b60gXQ6jXXr1vnG7rpuor5dXEjnhh4171ydR7TPUF4Q9SWOq1S+++67Pq+tzjsFfKjg0DXnXrK4qIYh9f7xHHld6xjy0pkK+qhGubGxMW0LAzUvNoxNmzb5vFK0jvl+GkfAppwn6vt76NAhn0IV1keak8lktAoCVwCbm5s9QwatG5MRyhYGydcdlynS6TRyuVxkr62uyqvpftx3332+v++//34A0BbssqE7f2h/161fik6g1ixRFUy6p2Q8UaMzmpqatNdpyZIlgfZYOvi+rEag6c4APm7yiNI41T1UZyThje6p8B3liPO2UGFQegVRTvqQCl2T0dFRHDx40CuOFtbSqbu7O7SgmwnT9dZ5Egl1DpKMaYq4o6JsYVE4FHXH0T2fvpta0TtKuHxS5VCUyug8BeCrAIzShOM4f+04zsuO4/iksdHRUV97DypnDQStC7ZeckDJcqmiCytRBRg+gWxWdXUjHRoa8qxZukMgTAnm2KrZ8U1+fHwcH3zwgXGzVcMsgZKFiqpXmQ5PnqsxPj6OgwcP+qprpdPpUEWIW8nVPEna2LglLKwXpy2cSpejcubMGezZsydQBCSdTnuCcmNjY6DyGTWl5nChcvXq1QGFmoe/3n777cbvAPjLaa9cudIrix+nT2WxWMSaNWsAwPO02tDNR142nrxtuuIa4+PjqK2tRWtrq+/602Glazp88803e9dYF7qmrsMPPvjApxiOjo4GNtwlS5YY7z+/Hzrrpm5e2TyM1N4mlUph27ZtGBkZ8QQpbjSgw1M3N9PptK9ZuVoohRuOdIaIsErU6v+TYmg6uG1eIzVMEChdH1NYLxUtUYW27u7ugIKqfu7ChQsjCaK6PFB+jVOpVCAk0JT/GNfSHDV8jRQcuuZ1dXWRc+lVTBW+dejWe9h31M2xTZs2BfLRx8fHY10v2zmwY8cO1NfX47rrrkN9fX2sPGSbABoHk4KgPsY/T3etVA+Gbbxcpti6dSv2798fuVWD7trrZBkgaCigiJ0ww6Zu7Cqvv/46APO+cfjwYZw5c8aas0jQXssjEerr63HFFVf4vMaqAZigs4HOsSuvvFJ7pvHXqmHOujPg/fff937nRm3d3Mtms4GzkYcxq8YOU7ScjgULFvgqfleyfRyXZ48ePQrHcSKvK/XMOn/+vNGZ0NDQEHq9bahGA4osUKu3EqOjo2htbQ0NxQaCKVd07nNnAd07NUIhShj/2NgYampqYiuJW7ZsCe3AMNOouFLpOM52AAOu675ie57rut9zXfcW13Vv4Y93dnb62nt0d3d7NztO2XDL+LSP0yGj5vLohC2Ce+6A0gFN1izdwRCmBHN01ewIddK/+eab2g2itrbWF2a5cOFC1NfX46677vLGZ8o1UD2fx44d04aFRMWUJ/nQQw/5Cj2ECShxw6nosMvn896GtWbNGk9QPnPmjO/7Z7NZ3HjjjWVVRA2LvzdZ8R5++OFI70/zq7e3F21tbZ6n1YQufNZxHG9dUfGrzZs3+1oAqAfE5z73Oe+6HT9+HFdffTXq6+t9BzPwoQBAOYu6sC3dWuYhY+l0Gps3b/Yp2mfPntWuX14+HNArY7p5RTmSuvfcsmULhoaGMD4+jmeeecZTAA8fPoxPfOITgWuj6425detWX7NyXa4QoTNEHDlyBMCH90dV7lTBMcyaagtlohYtRHNzM9atW4eamppICgYXItTnt7e3+wRHx3EiCaLZbBa5XM47qNWQxC1btuDmm2/2RUHo5lqS6qxhxXls6NpVRUFXuIK/lhtdstlsYA01NDQEPEc8VLS9vd33fmrVyjDvZGdnZ+CxuXPnWveebDaLuro6DA0Nobm5GU1NTaGfE6aU26qym8agUxBsgrXOUxQWDaKiKq62OgNhmOoQqOcI7RmV9HSZchnHxsY8r7JJjiB4PjoArz9hU1MTurq6fF5j3jsS8EdFXXfddVi1ahV+/OMfo62tLWDQ5N9bDXPWnQEUhQJEU4TUzwvL/SdjumneOI6DxYsXI51O+9pj6dZ3JbA5EnTozmmTYeWxxx4Lvd5hULQfT99Q5Qs+lieffDKSoUo1Yv/2t78FEIxASafTAQdNFLkznU7j0KFDxr1ed/bSNYqb81/tTIan8hMAPuU4zpsA/hZAu+M4h2wvoI02nU6jq6vL196Db1Q2RcuW88Kx5USouTypVAq33nqr8b24545y8LLZLLq6urSWjTibhNpag4Ql3eEyPj6u3dCLxaIXZtnW1obGxkY0Nzd7IR702iibDN0jU2idjiiHfy6Xw7Zt27Q5YCo8nEpFFZgIbnm85ZaS/eKPf/yj933ibrLEb37zG9/f3GKs/p+K6fDK5XKeEqUruNHS0oIVK1Z41tJ33nkHX/rSlxJZ8tevX+9txuSFU9t40O+0+fX09Hjj0jU65xQKBW8jVysNAvq1rBMCVq5c6T1ndHTUKwqyfv16L/9ZNXDEtdLfcccdvr8XLlyIrq4uT2D65S9/6SlspNip4+Seic7OTu0aUT8nrJE9HTYUVaBeb1URJWOcCd3/UR7ghQsXfOuADtuo15Kugy5vkzf0vuOOOyILooA99IiupxoFoWKrtKnjmmuuSVzJsbm5OaA4RKliq/YNBErfnb+2UCgEzkOejrBkyRL09/f7FHbuPdmxY4dvj+zo6IDrul5uXZjinc/nvbQFKk4UJRyOj4Hy6hzHwfXXX6813tn6Nqvvp8LPnKgygQ5VAVy5cmWknD6OqriePXs2cI1TqVRoH0QAxtoOqpeG/i5HgVX5zGc+o318bGzMWLk4Djrlu6WlJZALq4PPH9WTbMsv558d1agNwNemBzAX7iPImP6xj31M+/9XX301Pv7xj2NoaCgg8/L1bRtDXGypICq6c9qU0/3CCy+EXu8wMpkMVq5c6ZsPXPFXxwJEW+dXXnmlt/fV1NRo5S/aN9S9JZvNBs5qNZx/48aNvvmmGsbUtIh0Oo1t27ahoaEhVF6caVRcqXRd92uu617ruu4KAF8AcMx13X9te83ChQvhOA7uvPNOPP/88748Hb5RmRQtwJzzohJmWeITY9u2bdi1a5f1uRTSwnPwTJaNsBh2FZ2wZIov1+VUUoESOtxok9aF73F04YNkrYwTkhQ19yWOomq6tvl8PmBRpWpuQOk7kfB06tQpb46ZvIZh2EJrTB4pnRVPVbwffvhhXz9LYmhoSNs7KkpItTo3UqkUuru7USwWceLECc8L19fX5xPgqVopHRBqjgqFhquWvfb29oACqgocurWsEwJ4rqLruujv70drayt27tyJQ4cOaeeNzutnaxuirsvvfve7ePbZZwPFfujfX/3qV9rDk1e5062RnTt3+pQAvl/pDpYzZ86gUCh4IUhnz561KmFjY2NWwU7Nie7s7MQNN9zghavqlLyoHg+6Dt/85jcD/7d//36VOqIOAAAYsUlEQVTPsDVnzpxYgqgt9IiqV+qq1hK8HUJUkuSAEt3d3cZwrbDXqei+O5/H3CDHFRdAfy/VnPF8Pu8Ld7cpIU1NTWhoaPDSFr71rW+htbU1kJsUJceON6Dft29fQEAzeSaiwM+cqDKBCb5vP/LII2WH4ao1IYDSmly1alXoa00GPPVx+psXyjOhek90ubVAMD9TN09s5yhPZ+KQMqDKFJQPres5qJ6XmUzG1zZHvUdhsgW/TlEUodWrV3sy5Pz58yOHLr7yij5wb2BgwOs1DPj3RL6+iblz52LDhg2RPlMHl4miwM/pmpoab49dvHhx4LlHjx6NJcvpWLZsGQYGBrBnzx7fuarOOS7/h61zmi9cqTRFLplyv9XoM57iwY0Z9P3VFBDyjBK8EGTSKtnVSlX0qWxoaPAmoi65mlsiwqzSHJ1yFGZZAuIrOfX19T6LIReOuRKrVigNQycsqf3/AFh7Yuk2ejW/UN1kdAJ4XCttHOIoqrqWFCRUqd+D532qwhP3jquvO3/+PN544w1rYSVbaI1aRGPdunVecQL1kNTlYTz33HOBUDMK/4mbVwz4k+u5hay/vx8XLlzw1hu/Lo7joL293bcOVEV64cKFuHDhAhYvXuy7Fvl8PlKRBN1aVteeaolfunSpL28p6rxRPW5c+M1ms54HhjxGv/jFL4zvtWTJktCcLB3ZbNbzrqmV90zKG5+3qVTKp4TpFFGb949HVmQyGeTzeeRyOU/J0ymlUT0edB10XsW+vj5tk/goBh1b6FGUuZ/Ee9Lf3x+pqJqJuELC4sWLjV4U9btT0TT+nNbWVtx6660YGRkJRGCoQjgVSqGQeb5OaR7oxk6Gl7A5rnpKOTolIm5V8amip6fHtxcm9cJxdPM1n8/j1VdfDX1toVDQzklb8TxdKwWOamxR9yA6F9W9k+aJqZm9Ck9n4oQpA7ZKoRwah05GCZuvBw4c8PIi1UgSE3v27NEWo7PR3t6u3Ucdx7GelZRXToyMjGBwcDCxF9p13dgKH7Xq4XKozsjR0dGRKA+an8XU15jLoGongajyP9HY2Ihrr722LC+qetZygzOvy0HfX0U1VM6ZM0frhJoNyuWkKpWu6/7Cdd3tYc9Lp9PeRNR5gPjmY7NKq/CDgYgSghpXyVm+fHmgxDZtiLy3YpRwLxuFQiFQFRYoVYszhYjxjZ4shlTWG9BvMroQtnKttJMJ3ePvfOc7vseXLl3qjVsVnvjBqH7/t956C+Pj477qnCq20Bo1XOjuu+8OVBcMQ9crC0iWV8yLCdD35R4c7oWj30np4OtAVaSpsufZs2c9gYSsxVGKJOjWsrr2TJZ4Gzply3Vd39rjhXeADwtH0T5jCwUcGBhIXERE/RxCp7y1t7dbBQ5dyLfN+8dzorn3mRgbGwsoeeqBrlPwuKcsLOyfC1dRrOa2kPcocz9uKDShrv1CoYAvf/nLRm8LR10nYYKC6UzQfXcqmsaf8+STT2LXrl2+arw0V1QhXJ236jrt7OzU3uMoBlHeaoefdSQ0fv7zn9cqEWp/4LAcoyhGv3Lhoce6dZEEXT4eL24Shu48Us8S/ndLS4v1/dSCK7yon+M4npKmzhHKiw1rZg+U5oQpnSmMsD2W5gHVQUgqo1BoMe/baWP16tV48cUXA15Km5d+x44d2jPl9ttvx9y5c41npa6y6cGDBwORTHEKYMW9TrpWPTqSeifpLD548KC3X6vGM95JIKr8rxtfOV5UEzojWFhNDp7jyWXJsCKPM4Gq8FRyoiRXR0XX6+epp54qK9dCB1VA0x32r732WmivpKjoGuKuWbPGdyjrCjEQZDHk4SxcIOSPcZKWPY5bUCEqqtIwPj6O3t7eQKUynktlOhhVhfDkyZNe6ILaS1DFtEm98MILvsOZclh1h6TpGpmKItnyinW88sorvjWwaNEiNDQ0+MJTCdt1oTHR5rdhwwYvN+7s2bP47Gc/i7a2Nl8YSCXWsVqZMoo1Wef1414+HlJKgo56b0yVeKO00rFhEpTUvqXk8bUp52qOHBDu/VPnrHqIqdcb8B/ora2tgZ5gvD3QV77ylcDr+Rzt6uryGTG4gcs25oaGhsB8jWKtNuVah6Ea73p6evDGG29E8rbwdcIFbxM2A4Zu3ZgU5SiGHN37q1EGOiU+ilLFjbj8rLN5L9UxUG6ojbfffhvj4+Pa0P9KnTlJrmUYal4k/a2rSK8Lqzx16pQxL5r/S3R3dxv7Duv2ZL4Hbdq0yWhM3L17t7WZPYdHWpQr/6iQ8TdOAUSVzs5Ob3x9fX1lGSps81xXeIfy2QcHBwP1Cziqkf/YsWOB0GVTmpGanxyWxx8V9X3r6+sTKfX8LD5y5IhvrnB5mncSSEqYkeL06dPGIosmeSCdTmvlgX379vnWjHq/VEMBncuTaSibKqpOqYySXB0VnXD50ksvlZ1rweEeH9W6AkTrlRSVo0eP+gQQx3Fw//33+zb9mpoaT+jjOQbcYsiVytHRUfzud7/zfY4avpm0klycfmJx0I3niSeesL7GdDCqG/hjjz3m+zvMW6nbpLgxI6z3pu0a6ZRWW16xDvX79Pf3Y3BwUFsARb0uOu8MjYm+G73uhRdeCOTFVGodx0Xn9eNrL4qgY7KoR2mlkxReoZjWrk05554sWzg3p9w2DadPnw54VnhOqlo9W52jJoOLjWw2i6efftqnHOra1OjQKd4qYaFkSbwtPL+W1oGpz55N6YxTGTCJIUe3TkmJD1OqVOPssWPHAp5Sfj6arh0fQ0NDg9XrcvLkSU/w0ykBlTpzKmncJlTBlP7WedwXL16M7duDQV5qaootTLdQKHhtQVR4FA+Hcp9tBW+or6epmT2nkvIPhxt/L126FOgBHJW4vc9NRJnnauGd2267DcePH/f+Np2VuhoNugge3T734IMP+uZx3LoeJtRiQVEr16vws1g1rpEyfeDAAfT29k6Kg4JD/Sx13mbTnm/aG3iaVkdHR6AlkOrUMvWnn4lUnVIJVM5NPVnCH4dv8qp1BaisxVO14C5fvlzbzJn3HyT44lWrET7yyCO+v/P5fMB6XU3o7qvOWsmVT9vByLH1ElQxhcRV6p6bFIA4ecU6K3hPT49vjHRoqddF552JkxtXiXVsq7JrQlekgt+HKIKOes/Igxu3lU4cstksDhw44BPqwpRzqpRI9zKuABzl+vJQYcpPue222wAAa9euDYyJvJVz5swJzNE4BhdONpv1lAXHcQL9KW2vM4XPrlmzBo7jaIV3ThJvC1+7PO9x7dq1gcqANqOdLrTK1BpGN1fiFM6heUNKfJhSpRpndfseV3pshaRoDGFeykopAWFMhlHMFMqvRp8ApXUYxUttC9Pt6ekxFqkyFZMynTmmvTxKa5ZKenxpPqsyS1JvZZIaBTrCCtMBHxbeAUohqAsWLPDVeVi0aJH2nqtG/o0bN2pzaXWtXxYtWhRITUmCuo/kcjnPW3nttdcm7rVoq+7Nlcy6ujqtXFtpisWi1ttskgdsewM30KiFmtTCPfx9ZzpVqVRWqulxNpsNWNXr6+vLek8VXWgCp5IWT1t+GW36XV1d3oFx/PhxbxOwLV5V8VCtLNWWT6kLJWlqagpY6lRlOIqSY6rcqsMUEjcZVm5OnLxiXbVjqtLGk8NbWlp84wzzzkQRFiqxjpMKJUuXLkVdXZ3n9eD3Icp7qjmtJs92pdFdM9u8tVVKjELYteB53IcPH/ZC9hYsWABAP782btyIn/70p/jJT34SmKNJ72ehUPAs1bziYxRM9+y+++4zXle+l5TrbeF5j7t27cJTTz3l/V+Y0U4nnNoK2ahzJSz0lI9P7eMYV6nS7XtRcxN5gQubElwpJSAKlc7BMhXVyeVyvnVE6yKbzYaeR6b1RPu3ibj9npPu5ZU+C2k+q22C1GqbUalE73Mg+h7BC2X9+te/9l4zNjaGkZERo/dYNfLzomsUPaNr/XLw4EGt9zkuun1kz549mDdvXmQDnw5bUTOSQbu7u/HMM8/gwIEDiT8nCsVi0bsfqszD5QEAofJAoVDA3r17vSq2UdJMAL2BaaZRlUplJVFzfJK66U2EbfqVtHjaKr3Rpm9qyWJbvDpFuxIbURQreVLUxbd7926fIKhW1wSiHYxqX1LyyKjYlK4493wyrxEQDFOhHk18jFu3bsW3v/1t3zjDvDOTrTiX+znU70qnbEV5TzWn1eTZngqizNukAnDYteB53MViEQMDAygWi157Hm64qsTnmejp6UncEy+bzQY8YHV1db7rqmuLw3+vpLcll8t5e3eY0U6XP2nL6eXfyVQ4Jypx55Ru34sqTBFhSrCay6XmJlWyiE+ljNuEWn2ZX1e+T/N1oeaQq94o03ri+7cOnVdrMqik/JOkPVAYcWsUmIi6R/A5Fec1qpFfV3NBbf0ClPaPcuexaR+JY9w2wedvJpPxtfGpVIRc1Boq3HuvnjG8feDcuXND5QGTwyEMnt40U5n1SuWqVau8SVtfX5/YTW9CF5qgUslwXtOhRJgsZnzxqrS1tQUeq8SBGsVKnpRcLofrr78eAHDFFVegubnZ14Mtae4AeWBMfxNRlK4o93wyrxFQqlTHW4rwHk22MYZZXqcqZzLp59BBovuOUd9zsqrFTQZJ12vYteB53FRB98SJE4mLbyS9n+V6C7/+9a/7/h4dHfUpWWvXrvV+VwWayTCgRDXaqYWGeH+zMEyFc6KSZE6Vs2aiKMGqAKv+TRVwJyssthx01ZeJ1atXY/v27YH/27lzpzUVxbSebJFJU53SUql9lBuWVJIWoIlbo8BEkj0izmt0+4V6XXUGqHJ67hLl7iM2VOM2KdaVjJCLWkOFGyx0Z8yOHaX2gSR3mtA5HNS0Els17QlDx5jxCVXOrFcqufBfaS8lEC3/sJLhvGFhbibrF1+8qsfytddeK2tcOsq1kkdh7969aGtrw/e+9z0AwR5sSYiawxdF6Qq751NxjYAPe7Oq88Y2xqgholOhdCX5HDpI4uYIcSrtqahWbNdCl+M2OjpaloKX5H6W6y3M5XKYO3eu97fruj7haNeuXUbv4WQYUKLOLbXQEK/KGYaucM5ko36vODnRUYRXNTfp5Zdf9n4/efIkzp07B6D8Sp6ThW3umwxgYakoutfx9VJTU4OGhgZP8ZrqlJZK7aNqoUJOOQVo4tQoMJFkj4jzGlOIutoeSCVumLOOyd5H+PytRIRcUpqamnwpQeoZk81m0dzcHNq+RedwUM9RW+54LpfDuXPn/jHJd6gGZrVSyfMKamtrJ8WtPNX5h2ECmc36Ra/duHGj7zmVKJeuUk64WlTUTbVSOXwkxFGoqOl55YbE8WtkK2JRLqtXr8aPfvSjWIJ81BDRqVC6JuNzLheFMQq2a6HmcatCXZK5n9QDVklvoU44sgk00+W15oWGGhoaYglbk9EWIy5xwl+jCK/t7e3Gs2uqiviUg23um/4vTNA25WHzivD79+9Hd3f3tAnslUAXwg7oU13iUIkwTiDZHlHJfWXHDn2v43KZ7H2Ez9/pPJf37t0bWvAuSrqSzuGQpN/2TGVWK5WT2SOJM5XWlbBFZ7N+0WvvvffeQJJ3peGhGFNlJa8E3DPAQ0V1zytXyA1rPF9J4m7W09kSRKgeeB63jsnMp+VUYj5u2rTJKhwlEfingqQRGFOV91wpokZHmM6uqSziM5UkmXu69TLTDWk6A1dbW1vF2mSUS9L7VKl7Yup1XC4zbR9JSpQzJkq6km4fs9VDmW3MaqVysnokqVTbZh1m/ZoKhaEarORJiHptKnEN4xaxmGpmUk6hMDlwIV7HVBocyp2PM1U4mqx82akgTvhr1OgI03dSKxHrKhNfTsy2/VsVzDs7O6tK7qoGdL2Oy6Ua9pGpwrZmoqYr6fYxmzFstjGrlcqZqtiUy2RWi4zKTBXggOjXZrYd2irVZiwRph6ex63zWE7l3C93Pl5OwhEx3XtU1HQCoPwCWmphGlOhmsuF2bZ/X06CeVJ0vY4rwXTvI1OFbc3wdKVisWj0VpqiBNRqvbOVWa1UzmTFZrKZ7ANnJgtwUa9NudcwjhVfEKYLEijWrVvne7yzs3NGrWvg8hGOiOlWLKKmE/DnJy2gpYaUdXZ2Jhy1UI1cToJ5OUgNgsmBRzq6rosjR44Yn6vbxy6Xs2dWK5UzWbGZDVwuiygpl6snXZhZkEDR3d0dWum62hHhaGqJewaXc3/EkzX7EZlCmC7iVHCNUq13tjKrlUpANqHp5HJZREkRT7owk5jqStfC7GCqzuBsNoutW7fCcRzceeedMj9nISJTCNPF5VTBtRxmvVIpm5BQrYgnXZhpTGcfMWFmMpVnsBiRBUGYDC6nCq7l4JiayU4lt9xyi8ubGAvC5UKhUMDevXuxZ88eUSoFQRAEQRCqjEKhgHvuuQfFYhGZTAY//OEPJ01mcxznFdd1b5mUN59kZr2nUhCqGfGkC4IgCIIgVC9SKCoa5gZkgiAIgiAIgiAIlzk7duxAX1+fhNdbEKVSEARBEARBEATBAEWWCWYk/FUQBEEQBEEQBEFIjCiVgiAIgiAIgiAIQmJEqRQEQRAEQRAEQRASI0qlIAiCIAiCIAiCkJiq6FPpOM4IgAwAhz3ssr+r7fdqGYeMT8ZXjb9XyzhkfLN7TDI+GV+1/F4t45Dxyfiq8fdqGceMGZ/rujPS6VctSuUwgHnTPQ5BEARBEARBEITpwnVdJ/xZ1ceM1IQFQRAEQRAEQRCE6kCUSkEQBEEQBEEQBCEx6ekewAR/B+DPUMqrJIrs72r7vVrGIeOT8VXj79UyDhnf7B6TjE/GVy2/V8s4ZHwyvmr8vVrGMVPGdw4zlKrIqRQEQRAEQRAEQRBmJhL+KgiCIAiCIAiCICQmUvir4zh/jlKIKgB8FMAIgN8DGIdUbRUEQRAEQRAEQagWigBqUGpTcgml0NqzAH4HoADgMxOP/QHACgB7XNf9G/4GTP/7qOu6/xz2gVE9lXcB+BOAtwB8YeKxXgBzIr5eEARBEARBEARBmHzeBjAK4A0A/wHA3wC4E8BRALUAtk78/3+e+D8ddwH4FT7U/ayEKpWO48wHcAdKXslR9sYZlDTg0SgfJAiCIAiCIAiCIEw6iwCMAZgL4M8BPOW67m8B/BcAH3Nd99jE83pR8mr6mND/PgEgj0oplShVZf0DgH8A0A/gIoCPAVg58f81UT5IEARBEARBEARBmHRSKEWUrgRwPYC/chwn5bruGQBLIrz+zwC86LruHwAMOo5zc5QPDOMuAHUA/nbi508APoVSjC55KaWErCAIgiAIgiAIwvTz9wCyAC5M/P0NAP8rxuvvQknvw8S/d4W9wFqox3GcLIB2lDRdPpC2KK8XBEEQBEEQBEEQppR213WHHMc5D+A8SrmT9zuOcw2AAdsLmf7X6jiOi1JUqus4zlddSy/KME/lXwD4LYD/6Lpu2nXdNIBfA1g48f8XUcq1HA7/boIgCIIgCIIgCMIkQUrfEsdx9qFUA+c4SnmVBQD/FsD/CHmPvwDwX13XbXJdd4XrutcB+H8o1dgx4lgUTjiO8wsADQC+6rruixOP/TsA39IM3gkZoCAIgiAIgiAIgjA1jKNUiCeFUm2cPwD4F5QUR6qL4wJ4H8AHAG4E8D8BPEG6H+Dpfx91Xbfb+Emu68qP8oNScuofATRN4xjmoFQOeCH/vcz3vBrAPwL46zLf53YAfQBWT/e9ijDWdyYW05XTOIarJsZxFYB32ONrJxYy/2lBqVrXO0k+Yxq+2wKUcquvq8B7pQC8AmAXgA/Y4/cB2DdN924+gJ8B2BvyvH8F4HUA/8fynPsnrtU4Su2Z/hKlXIfzE5t8P4Bh9vy/nHh+F3vs7/nf7Pr83XTvWRGu5Q0oVZnrmu6xVNsPgE8D+PF0j6MafibOl3cBnElyxrDz6a9QssqPAvg6e8/NlTgHy/yO9wE4VIlztBrkFc2YRgB0aB7/0sQ+uaiCnxVbrpmKMyXpZ0ycJcZzZCb9TJyfx1DycH0TwP+dkHNGJtbmEJ2tAP77xFptAvA4SnLb7dP9Hcr47hWRt2faz7QPQH40NwW4BcDvAXyN/z7d45KfRPfyUwD+GcC/n/j3nonH/9uEcsEVyvcnNtKz9LyYnxH5NRX6bv8GpRD4FyvwXo0A/mlCuboIYPfE4wcB/O9qEpg0Y//ihLDaB2Cz5Tn/hFKhs/cnDtqxiZ9+lPpG8e/dMzE/XgeQnnjsdQDP0t8z5frIT+j8+QaAVwHcNN1jkZ8pud+zfs2alMpq+JmK65/0M9g5oT1HZusPSqGZFwB8YbrHIj/l/VjDXwVBEARBEARBEATBRpSWIoIgCIIgCIIgCIKgRZRKQRAEQRAEQRAEITGiVAqCIAiCIAiCIAiJEaVSEARBEARBEARBSIwolYIgCIIgCIIgCEJiRKkUBEEQBEEQBEEQEvP/Acmy59j/uMVrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "sns.boxplot(data=X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "plt.boxplot(X)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "plt.plot(X.iloc[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cf3b670208>"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "sns.boxplot(data=Xd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "plt.boxplot(Xd)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train - Test split\n",
    "\n",
    "Since the dataset is not too large, we kept the test size at %20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the dataset, X: 125\n",
      "Length of the targets, y: 125\n",
      "Length of first row of X: 5587\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "Xd_train, Xd_test, yd_train, yd_test = train_test_split(Xd, yd, test_size=0.2, random_state=42)\n",
    "print(\"Length of the dataset, X: %d\" % len(X_train))\n",
    "print(\"Length of the targets, y: %d\" % len(y_train))\n",
    "print(\"Length of first row of X: %d\" % len(X_train.iloc[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>AACS</th>\n",
       "      <th>AAK1</th>\n",
       "      <th>AAMDC</th>\n",
       "      <th>AANAT</th>\n",
       "      <th>AARD</th>\n",
       "      <th>AARS2</th>\n",
       "      <th>AARSD1</th>\n",
       "      <th>AASDHPPT</th>\n",
       "      <th>AASS</th>\n",
       "      <th>AATK</th>\n",
       "      <th>...</th>\n",
       "      <th>ZSCAN26</th>\n",
       "      <th>ZSCAN30</th>\n",
       "      <th>ZSCAN4</th>\n",
       "      <th>ZSWIM8</th>\n",
       "      <th>ZSWIM8-AS1</th>\n",
       "      <th>ZWILCH</th>\n",
       "      <th>ZYG11A</th>\n",
       "      <th>ZYX</th>\n",
       "      <th>ZZEF1</th>\n",
       "      <th>ZZZ3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.59</th>\n",
       "      <td>5.41091</td>\n",
       "      <td>5.38142</td>\n",
       "      <td>7.42114</td>\n",
       "      <td>6.82676</td>\n",
       "      <td>5.3361</td>\n",
       "      <td>5.97844</td>\n",
       "      <td>4.99085</td>\n",
       "      <td>4.33025</td>\n",
       "      <td>4.67144</td>\n",
       "      <td>6.30093</td>\n",
       "      <td>...</td>\n",
       "      <td>5.3549</td>\n",
       "      <td>5.58172</td>\n",
       "      <td>4.59327</td>\n",
       "      <td>4.32413</td>\n",
       "      <td>7.55047</td>\n",
       "      <td>3.77648</td>\n",
       "      <td>6.53708</td>\n",
       "      <td>5.31667</td>\n",
       "      <td>9.04544</td>\n",
       "      <td>7.8442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.28</th>\n",
       "      <td>5.31147</td>\n",
       "      <td>5.61038</td>\n",
       "      <td>7.70352</td>\n",
       "      <td>6.74962</td>\n",
       "      <td>5.23202</td>\n",
       "      <td>5.96687</td>\n",
       "      <td>4.88277</td>\n",
       "      <td>4.56117</td>\n",
       "      <td>4.91479</td>\n",
       "      <td>6.19321</td>\n",
       "      <td>...</td>\n",
       "      <td>5.34273</td>\n",
       "      <td>5.49633</td>\n",
       "      <td>4.5978</td>\n",
       "      <td>4.50744</td>\n",
       "      <td>7.46635</td>\n",
       "      <td>4.25056</td>\n",
       "      <td>6.49782</td>\n",
       "      <td>5.47147</td>\n",
       "      <td>9.16267</td>\n",
       "      <td>8.08392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.48</th>\n",
       "      <td>5.39771</td>\n",
       "      <td>5.44697</td>\n",
       "      <td>7.60473</td>\n",
       "      <td>6.85792</td>\n",
       "      <td>5.2622</td>\n",
       "      <td>5.66789</td>\n",
       "      <td>4.97783</td>\n",
       "      <td>4.12217</td>\n",
       "      <td>4.70214</td>\n",
       "      <td>6.26712</td>\n",
       "      <td>...</td>\n",
       "      <td>5.3361</td>\n",
       "      <td>5.49254</td>\n",
       "      <td>4.48022</td>\n",
       "      <td>4.34924</td>\n",
       "      <td>7.40191</td>\n",
       "      <td>4.02647</td>\n",
       "      <td>6.66427</td>\n",
       "      <td>5.37776</td>\n",
       "      <td>8.70963</td>\n",
       "      <td>7.96916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.47</th>\n",
       "      <td>5.24127</td>\n",
       "      <td>5.5894</td>\n",
       "      <td>7.49207</td>\n",
       "      <td>6.69335</td>\n",
       "      <td>5.15422</td>\n",
       "      <td>5.86223</td>\n",
       "      <td>4.90096</td>\n",
       "      <td>4.44035</td>\n",
       "      <td>4.71467</td>\n",
       "      <td>6.39262</td>\n",
       "      <td>...</td>\n",
       "      <td>5.45624</td>\n",
       "      <td>5.56966</td>\n",
       "      <td>4.45467</td>\n",
       "      <td>4.34349</td>\n",
       "      <td>7.43908</td>\n",
       "      <td>4.21034</td>\n",
       "      <td>6.65607</td>\n",
       "      <td>5.44018</td>\n",
       "      <td>8.85288</td>\n",
       "      <td>7.91867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.61</th>\n",
       "      <td>5.22449</td>\n",
       "      <td>5.62184</td>\n",
       "      <td>7.79561</td>\n",
       "      <td>6.5209</td>\n",
       "      <td>5.21097</td>\n",
       "      <td>6.01341</td>\n",
       "      <td>4.58554</td>\n",
       "      <td>5.16044</td>\n",
       "      <td>5.24653</td>\n",
       "      <td>6.42666</td>\n",
       "      <td>...</td>\n",
       "      <td>5.11408</td>\n",
       "      <td>5.51659</td>\n",
       "      <td>4.66465</td>\n",
       "      <td>4.24883</td>\n",
       "      <td>7.14956</td>\n",
       "      <td>4.33025</td>\n",
       "      <td>6.55358</td>\n",
       "      <td>5.64288</td>\n",
       "      <td>8.92526</td>\n",
       "      <td>8.0112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5587 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AACS     AAK1    AAMDC    AANAT     AARD    AARS2   AARSD1 AASDHPPT  \\\n",
       "1.59  5.41091  5.38142  7.42114  6.82676   5.3361  5.97844  4.99085  4.33025   \n",
       "1.28  5.31147  5.61038  7.70352  6.74962  5.23202  5.96687  4.88277  4.56117   \n",
       "1.48  5.39771  5.44697  7.60473  6.85792   5.2622  5.66789  4.97783  4.12217   \n",
       "0.47  5.24127   5.5894  7.49207  6.69335  5.15422  5.86223  4.90096  4.44035   \n",
       "0.61  5.22449  5.62184  7.79561   6.5209  5.21097  6.01341  4.58554  5.16044   \n",
       "\n",
       "         AASS     AATK  ...  ZSCAN26  ZSCAN30   ZSCAN4   ZSWIM8 ZSWIM8-AS1  \\\n",
       "1.59  4.67144  6.30093  ...   5.3549  5.58172  4.59327  4.32413    7.55047   \n",
       "1.28  4.91479  6.19321  ...  5.34273  5.49633   4.5978  4.50744    7.46635   \n",
       "1.48  4.70214  6.26712  ...   5.3361  5.49254  4.48022  4.34924    7.40191   \n",
       "0.47  4.71467  6.39262  ...  5.45624  5.56966  4.45467  4.34349    7.43908   \n",
       "0.61  5.24653  6.42666  ...  5.11408  5.51659  4.66465  4.24883    7.14956   \n",
       "\n",
       "       ZWILCH   ZYG11A      ZYX    ZZEF1     ZZZ3  \n",
       "1.59  3.77648  6.53708  5.31667  9.04544   7.8442  \n",
       "1.28  4.25056  6.49782  5.47147  9.16267  8.08392  \n",
       "1.48  4.02647  6.66427  5.37776  8.70963  7.96916  \n",
       "0.47  4.21034  6.65607  5.44018  8.85288  7.91867  \n",
       "0.61  4.33025  6.55358  5.64288  8.92526   8.0112  \n",
       "\n",
       "[5 rows x 5587 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.0, 1.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>hs-CRP</th>\n",
       "      <th>shaps_score</th>\n",
       "      <th>psqi_score</th>\n",
       "      <th>ctq_denial_score</th>\n",
       "      <th>ctq_emoabuse_score</th>\n",
       "      <th>ctq_emoneglect_score</th>\n",
       "      <th>ctq_physabuse_score</th>\n",
       "      <th>ctq_physneglect_score</th>\n",
       "      <th>...</th>\n",
       "      <th>qids_psychomotor_score</th>\n",
       "      <th>qids_score</th>\n",
       "      <th>tci_coop_score</th>\n",
       "      <th>tci_harmavoid_score</th>\n",
       "      <th>tci_noveltyseek_score</th>\n",
       "      <th>tci_persistence_score</th>\n",
       "      <th>tci_rewarddep_score</th>\n",
       "      <th>tci_selfdirect_score</th>\n",
       "      <th>tci_selftranscend_score</th>\n",
       "      <th>tci_impulsivity_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>si</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK683</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0436</td>\n",
       "      <td>23.946667</td>\n",
       "      <td>7.281046</td>\n",
       "      <td>9.133333</td>\n",
       "      <td>9.226667</td>\n",
       "      <td>10.2</td>\n",
       "      <td>7.973333</td>\n",
       "      <td>7.226667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344371</td>\n",
       "      <td>6.138158</td>\n",
       "      <td>33.836601</td>\n",
       "      <td>14.490196</td>\n",
       "      <td>17.973856</td>\n",
       "      <td>5.541096</td>\n",
       "      <td>14.712418</td>\n",
       "      <td>29.535948</td>\n",
       "      <td>13.045752</td>\n",
       "      <td>3.938356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AE646</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AJ537</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AG245</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AH639</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  sex  hs-CRP  shaps_score  psqi_score  ctq_denial_score  \\\n",
       "si                                                                   \n",
       "AK683   27    1  3.0436    23.946667    7.281046          9.133333   \n",
       "AE646   20    1  3.2000    33.000000   13.000000         13.000000   \n",
       "AJ537   53    1  3.2000    41.000000   12.000000          5.000000   \n",
       "AG245   33    1  2.0000    14.000000    1.000000         14.000000   \n",
       "AH639   24    1  4.5000    20.000000    4.000000         14.000000   \n",
       "\n",
       "       ctq_emoabuse_score  ctq_emoneglect_score  ctq_physabuse_score  \\\n",
       "si                                                                     \n",
       "AK683            9.226667                  10.2             7.973333   \n",
       "AE646            5.000000                   6.0             7.000000   \n",
       "AJ537           11.000000                  18.0             7.000000   \n",
       "AG245            5.000000                   5.0             9.000000   \n",
       "AH639            7.000000                   5.0             5.000000   \n",
       "\n",
       "       ctq_physneglect_score  ...  qids_psychomotor_score  qids_score  \\\n",
       "si                            ...                                       \n",
       "AK683               7.226667  ...                0.344371    6.138158   \n",
       "AE646               7.000000  ...                0.000000   16.000000   \n",
       "AJ537              10.000000  ...                1.000000   14.000000   \n",
       "AG245               5.000000  ...                0.000000    2.000000   \n",
       "AH639               5.000000  ...                0.000000    0.000000   \n",
       "\n",
       "       tci_coop_score  tci_harmavoid_score  tci_noveltyseek_score  \\\n",
       "si                                                                  \n",
       "AK683       33.836601            14.490196              17.973856   \n",
       "AE646       36.000000            27.000000              21.000000   \n",
       "AJ537       28.000000            30.000000              10.000000   \n",
       "AG245       33.000000            10.000000              20.000000   \n",
       "AH639       41.000000             2.000000              28.000000   \n",
       "\n",
       "       tci_persistence_score  tci_rewarddep_score  tci_selfdirect_score  \\\n",
       "si                                                                        \n",
       "AK683               5.541096            14.712418             29.535948   \n",
       "AE646               7.000000            14.000000             20.000000   \n",
       "AJ537               3.000000            12.000000             15.000000   \n",
       "AG245               6.000000            16.000000             34.000000   \n",
       "AH639               4.000000            21.000000             39.000000   \n",
       "\n",
       "       tci_selftranscend_score  tci_impulsivity_score  \n",
       "si                                                     \n",
       "AK683                13.045752               3.938356  \n",
       "AE646                18.000000               4.000000  \n",
       "AJ537                 4.000000               3.000000  \n",
       "AG245                11.000000               4.000000  \n",
       "AH639                22.000000               8.000000  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection on Demographic Symptom\n",
    "\n",
    "### Select K Best\n",
    "    - ANOVA f-value\n",
    "    - Mutual information\n",
    "Chi square is not applicable due to negative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 10)"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsk10f = SelectKBest(f_classif, k=10)\n",
    "dsk10f.fit_transform(Xd, yd)\n",
    "Xd_k10f = Xd.iloc[:,dsk10f.get_support(indices=True)]\n",
    "Xd_k10f.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['shaps_score', 'madrs_score', 'hamd_hamd17_score', 'hamd_hamd21_score',\n",
       "       'hama_score', 'poms_score_fatigue', 'poms_score_tmd',\n",
       "       'qids_sleep_score', 'qids_appetite_score', 'qids_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd_k10f.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 10)"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsk10m = SelectKBest(mutual_info_classif, k=10)\n",
    "dsk10m.fit_transform(Xd, yd)\n",
    "Xd_k10m = Xd.iloc[:,dsk10m.get_support(indices=True)]\n",
    "Xd_k10m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['shaps_score', 'psqi_score', 'madrs_score', 'hamd_hamd17_score',\n",
       "       'hamd_hamd21_score', 'hama_score', 'poms_score_depression',\n",
       "       'poms_score_tmd', 'qids_appetite_score', 'qids_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd_k10m.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Percentile - %10\n",
    "    - ANOVA f-value\n",
    "    - Mutual information\n",
    "Chi square is not applicable due to negative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 4)"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsp1f = SelectPercentile(f_classif, percentile=10)\n",
    "dsp1f.fit_transform(Xd, yd)\n",
    "Xd_1pf = Xd.iloc[:,dsp1f.get_support(indices=True)]\n",
    "Xd_1pf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['madrs_score', 'hamd_hamd17_score', 'hamd_hamd21_score', 'hama_score'], dtype='object')"
      ]
     },
     "execution_count": 552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd_1pf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 4)"
      ]
     },
     "execution_count": 553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsp1m = SelectPercentile(mutual_info_classif, percentile=10)\n",
    "dsp1m.fit_transform(Xd, yd)\n",
    "Xd_1pm = Xd.iloc[:,dsp1m.get_support(indices=True)]\n",
    "Xd_1pm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['madrs_score', 'hamd_hamd17_score', 'hamd_hamd21_score', 'hama_score'], dtype='object')"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd_1pm.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for RFECV: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.93      0.93        14\n",
      "           1       0.94      0.94      0.94        18\n",
      "\n",
      "   micro avg       0.94      0.94      0.94        32\n",
      "   macro avg       0.94      0.94      0.94        32\n",
      "weighted avg       0.94      0.94      0.94        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Xd_train_1pm, Xd_test_1pm, yd_train_1pm, yd_test_1pm = train_test_split(Xd_1pm, yd, test_size=0.2, random_state=42)\n",
    "gnb_d1pm = GaussianNB()\n",
    "gnb_d1pm.fit(Xd_train_1pm, yd_train_1pm)\n",
    "gnb_p_d1pm = gnb_d1pm.predict(Xd_test_1pm)\n",
    "print(\"Gaussian Naive Bayes Classifier report for RFECV: \\n\\n\", classification_report(yd_test_1pm, gnb_p_d1pm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive Feature Elimination with Cross Validation (RFECV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:665: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_folds = np.zeros(n_samples, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:437: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:437: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:163: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  support_ = np.ones(n_features, dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:164: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ranking_ = np.ones(n_features, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:163: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  support_ = np.ones(n_features, dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:164: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ranking_ = np.ones(n_features, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:163: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  support_ = np.ones(n_features, dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:164: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ranking_ = np.ones(n_features, dtype=np.int)\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "\n",
    "drfecv = RFECV(\n",
    "    estimator=svc,\n",
    "    step=1,\n",
    "    cv=StratifiedKFold(2),\n",
    "    scoring=\"accuracy\",\n",
    "    min_features_to_select=2,\n",
    ")\n",
    "drfecv.fit(Xd, yd)\n",
    "\n",
    "print(\"Optimal number of features : %d\" % drfecv.n_features_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uoVK1lbS05/6SbU4HkqymrcqyEspLVRSBpKO7j6/9diOzZpRTM8Kcl5d272XmjDIuf/tr8lqeB55t5/v3P09jXSXlJbnmpMazb9Bo7ezlTUsbOOuYwyewhM5NbyPVSL4S5nj8gqhJqxWoApYCbybqgP9HoomH00pjXSWPNu/Ieay5vYfyUjF31owD9ksqmgzA6aHNV533Wt5x/BHDnvfebz/AvZvaonF8eZReJOw3n1pJQ+3Yx3ns3TfACV+4kzWb2jyQODeBhu1sN7N3A/9ANIfkWmANUVD5C2ADcJaZ/XYyCpk0qdrh06S0dHQzf041pTl+ORfLmiTp/p/GUb60Tz+6kae2746VVn88Wjp6qKkopb5mfCsGVJWX8sajGlizqW2CSuacg1FGbZnZ02b2OTM708xebWYnmdmfmtkPzGzvZBUyaTLTpGRr6Th4xFZabWU5nUVQI0lnN04PdR7OiqUpAO7bnN8v5ugzrSFjoc4xW7k0xeZXunhxV5x0cc65OOLObHcZGoeZS2Jm0WTEYTqfo1Ty+3IeS5J0DSM1So3ktUfOYnZ1ed5/4Te3dx80Cm6sVh4dBT+vlTg3cTyQjMHQ7PasNCk7e/bRubd/2EBSLE1brV29VJSWMLNq5EF9pSXitCUp1mxqzVt6/MFBY+uOPQfkLRuPVx9eR2NdpQcS5yaQB5IxGErcmDUpsXkoWWPNQddA8Sy329bZR6q2IlZT0sqmFC/v7mXzK12jnjsWL+3eS1//4IQNMZbEyqUp7tvcxuCgrw3j3EQYNZAo8meSrgjPF0gqWK6rJEgNJW48MJDkyvqbqVhqJG1dvUPNd6NZ0RQ1Fd2Tp1/4o32mY7GiKUVHdx9Pv7h7wu7p3HQWp0byr8CpRCndATqJRnFNWzUVpcwoLz0oTUpLWIdk/pzh+0iKobO9rat31P6RtHlzqjkqVcO9myZuFcpM6fk62bnLxiM9SMCbt5ybGHECyRvN7BPAXgAz2wGMbxxmkZNEqq7ioBpJc3sPh9VVDrtwVW1lGb39g/T1J3uVxNbO+IEEouatB7d00Ns/MOFlae7opqxEHDE79yJiY3HYzCqOeVUda/IU/JybbuIEkn2SSglL2kpqBJL9TTgJcqVJae7oGbEJJp1KfiJSfeTL4KDR3t1Hqi7+b4UVTY3s2TfAo807J7w8ze09HDlnBmWlE9udt7Ipxdrnd7Cnb+KDn3PTTZx/nf8C/Bw4TNJVRGuR/H95LVURaKytpK3zwFFbLe09LBihCaYY8m3t3LOPgUE7pBrJKUfVU1aivPzCb+kYfjj1eKxsaqRvYJCHnmuf8Hs7N93EWWr3h8DfAVcDLwLnmtl/xbm5pLMlbZC0WdJlOY4vlLRK0npJd0mal3FsgaQ7JT0j6emwYiKSvi/pOUmPh8eJ8d7qxEplZQDeu2+Al3bvHfFLr64IAkncOSSZ6qrKOWnBbO7Nw8TEfAWSkxfXU1FW4v0kzk2AEScKhFxb683stcAfDuXGoTnsWuCPifJxPSzpVjN7OuO0a4AbzewGSWcRBav3h2M3AleZ2W8l1XJgc9pnwuqKBZOZJqWstIRtO0YfXTS0SmKSA0kYQBB31FbayqZGvv4/G9nR3ceccaYySdu1Zx87e/ZN6IittKryUk5eVB/lCnPOjctoKVIGgXWSFozh3icDm81si5n1ATcB52SdswxYFbZXp49LWgaUpXN5mVmXmY2c03ySZadJSScWHC49CmSsSZLgkVutY6iRQDSk1gzue3bivpjTI7ZGai4cjxVNKTa83MnLu6dtth/nJkScPpK5wFOhCerW9CPGdUcCWzOeb+PgNdjXAReE7fOAOkkNwNHATkk/k/SYpK+EGk7aVaE57OuSCrLsb3aalOahYaojNG1VJX+53fQky9ESNmY7/shZzKwqY83GiQskzR3RcOp8NG1B1OEOeK3EuXGKs0LiF8Z471zTorOnEl8KfEvSh4B7gBeA/lCulcBJQAvwE+BDwHeBy4GXiIYgXwd8FrjyoBeXLgYuBliwYCwVqpFlz25v6eihtrJsxAy1tZXlQLJrJG1dfVF6lBlx/tfYr6y0hDctSXHv5jbMbEISLLYMs7bLRHnNq2bSUFPBmk2tXPD6eaNf4JzLKU5n+91E/SN14fFM2DeabcD8jOfzgO1Z995uZueb2UnA58K+XeHax0KzWD/w38DrwvEXLdILXE/UhJar3NeZ2XIzW97Y2BijuIcmO99WS0cP8+urR/wC3d9HktzEjW1dvTTETI+SbeXRKV7YuYctbd0TUpaW9h5StRVDTYITraRErGhKce/mdk+X4tw4xEmR8h6iNdrfDbwHeEjSu2Lc+2GgSdJiSRXAhcABTWKSUqFDH6Kaxvcyrp0T5qwAnAU8Ha6ZG/4KOBd4MkZZJlxjVpqUOBlqq8tLkZJeI4mfHiXb6U3Rf641GydmGPBImZQnysqmRtq6evnDS515fR3nprI4fSSfA95gZh80sw8Q1QD+YbSLQk3iEuAO4BngZjN7StKVkt4ZTjsT2CBpI3A4cFW4doCo2WuVpCeImsm+E675Ydj3BJAC/jnWO51gNZVlQ2lS4maoLSkRtRVlie8jOdSO9rT59dUsbKiesGHALR09wybAnCjpdCn3bvZZ7s6NVZw2gxIzeyXjeTsxswab2W3AbVn7rsjYvgXIOYw3jNg6Psf+s+K89mRI1VXQ2tU7lKE2Tlt+bVWyMwC3dfVy7BEzx3z9yqYUP3/0BfYNDFI+jtnovf0DbN+1Z8Ky/g7nVbOqOPrwWtZsauPi05fk9bWcm6ri/Ev/jaQ7JH0odIr/Grg9v8UqDo210aTEoU7hGF96tZXJzQA8OGi0d/WNuUYCsGJpI919AzzWMr50KS/s2IPZyKPgJsqKpY089FwHe/d5uhTnxiJOZ/tngG8T1Q5OAK4zs7/Ld8GKQSqkSTmUDLU1CQ4ku/bso/8Q06NkO3VJA6UTkC6lOQ/p44ez8ugUff2DPPx8R95fy7mpKE5n+2LgNjP7tJn9LVENZVG+C1YMUnVR4sZDyVBbl+A1SdIDB8ba2Q4wa0Y5J8ybNe7UIy0xJnhOlDcurqei1NOlODdWcZq2/osD05MMhH3TXqq2kh09fWxp7Y6doTbJqySOdVZ7tpVNjazftpNdPWMf5tzc3sOM8tJDnhg5FtUVZbx+4RwPJM6NUZxAUhZSnAAQtqf1eiRpjXWVmMHjW3fGHqaa5D6SoVnth5BCPpeVTSkGDe4fR7qUdLLGiZjYGMeKphTPvLj7oOWTnXOjixNIWjOG6yLpHMB/uhHl2wJ4cdfIWX8zJXnUVnpy5XhrJCfMn01dZdm4lt9t6eielGattPQcmPvykMHYuakuTiD5K+D/SmqRtJUoJclH81us4pD5hRu3U7iusoyuvv5EzqRu6+qlvFTMmlE+rvuUl5ZwypIG1mxqxezQ36eZRXNIJmHEVtqxR8xkTnU59/iqic4dsjijtp41s1OIMvUuM7M3mdnm/Bct+TI7peNmqK2tKsMMehI41LSts5eGmsoJaU46vSnFth17hpJZHopXOnvZu29wUkZspZWUiNOWprh3U9uYgp9z01mcUVufkjQT6Aa+LulRSW/Nf9GSbyw1kiQnbhxPepRsK9PpUsbQVJQOPvmejJjt9KZGXunsZePLXZP6us4VuzhNW39uZruBtwKHAR8GvpjXUhWJdJoUiP+ll+TEja1dvaRqJ2YcxcKGaubNmTGmvFstQ3NI8pseJduKkFY+H0sGOzeVxQkk6XaOPwGuN7N15E4RPy2l6ioOKUNterndziTWSDrHN6s9kyRWNjXywLPt9A8Mjn5Bhpb2bkoER86eMSFlieuI2TNY0ljjw4CdO0Rxvv0ekXQnsBi4XFIdB84rmdbmzppx8CorI0jqcrtmRnt3L6kJatqCaBjwj3/fwuNbd7J8UX3s65o7ejhi9gwqysaeq2usVjY18qPft/CxHzwyKa939OF1/O0fHz0pr+VcvsQJJB8BTgS2mFlPWMHww/ktVvG4+vzjDql6ltTldnft2ce+gfGlR8l22pIUJYI1m9oOLZBMQvr44Vzwunn8/rkOnm3Nfz/Jjp593P7kS3z0jKOorsjPmivOTYZR/+8N67Y/mvG8nSgDsAOWNNYe0vlDgSRhNZKJSI+SbVZ1OcfNm829m9sO6Vf31o4e3nrs4RNWjkNx3LxZ3PaplZPyWreu284nf/wYLR09HPOqsWdcdq7QJr/tYJpLaiB5pTOdHmVikxac3pTi8a072b033uCCrt5+2rv7Yg+nLmbpeTJjGSLtXJJ4IJlkNQlt2krPap/o3FYrlqYYGDQeeDZeJba5PVqmdzLnkBRK+j1u7fBA4opbrEAiqVTSEZIWpB/5LthUVVFWQmVZSeJqJG2dE5OwMdtJC+ZQU1Eae0jtUNbfAvWRTKbZ1RXMrCrzGokrenEmJP418DLwW6JFrX4N/CrOzSWdLWmDpM2SLstxfKGkVZLWS7pL0ryMYwsk3SnpGUlPp1PXhzXgH5K0SdJPwnrwRaWuKnnL7bZ19VJWMv70KNkqyko4dUlD7CG16XVIJjPPViEtbKgZes/OFas4NZJPAa82s2PN7LjwOGgJ3GySSoFrgbcTpVe5SNKyrNOuAW4M97sSuDrj2I3AV8zsNUTrxKeX+/0S8HUzawJ2EI0qKypJTCXf1hWt1V5SMvFThFYsTdHc3jNU2xhJS0cPc6rLmVk1sQEtqRbUV9MSmvOcK1ZxAslWYNcY7n0ysNnMtoTU8zcB52SdswxYFbZXp4+HgFMW1m3HzLrC0GMBZ7F/nfcbgHPHULaCqk3g4lZtXX2kxpk+fjgrj06nSxm9eaulvYcFkzyjvZAWNFSzbcceBhKYxNO5uOIEki3AXZIul/Tp9CPGdUcSBaG0bWFfpnXABWH7PKAuzFM5Gtgp6WeSHpP0lVDDaQB2mln/CPdMvCTWSFo7eye8fyTtqFQNR8yq4t4YzVvNHd2TmvW30BbWV9M/aGzfuafQRXFuzOIEkhai/pEKoC7jMZpcbSTZP7suBc6Q9BhwBvAC0E80v2VlOP4G4CjgQzHvGb24dLGktZLWtrYmK3dSbWV5IvtI8hVI0ulS7tvcNuIv730Dg2zfGX9tl6kg3RfU4v0krojFmZD4BYCQGsXMLO6U323A/Izn84DtWffeDpwf7l8LXGBmuyRtAx4zsy3h2H8DpwDfA2ZLKgu1koPumXHv64DrAJYvX56odoNo3fbkJG00M9q7Ji7PVi4rmlL8ZO1W1m/byUkL5uQ8Z/vOqIlnunS0w/7Rac3tPZy2tMCFcW6M4ozaem2oMTwJPCXpEUnHxrj3w0BTGGVVAVwI3Jp175SkdBkuJwoU6WvnSGoMz88CnrZooYjVwLvC/g8Cv4hRlkRJWtPW7j399A0MTvhkxEynLU2hkC5lOOlhsNOpaWvurBmUl4rmDu9wd8UrTtPWdcCnzWyhmS0E/g/wndEuCjWGS4A7gGeAm83sKUlXZizdeyawQdJG4HDgqnDtAFGz1ipJTxA1aaVf87PApyVtJuoz+W6sd5ogNQlbt701D+lRstXXVHDckbNGnE/SXKD08YVUWiLmz6n2SYmuqMXJFFdjZqvTT8zsLkmx/qWb2W3AbVn7rsjYvoX9I7Cyr/0tcNAw49DcdXKc10+quqoy9g0Yvf0DVJaVFro4tIbJiBM9qz3biqUprrtnC51791GXY3hvS3s3lWUlHJbHgJZECxqqfVKiK2qxRm1J+gdJi8Lj74Hn8l2wqSxpGYDTCRsnMoV8LiubGukfNB7c0pHzeHN7D/Prq/MylyXJFtZX09Le40v8uqIVa4VEoBH4GfDzsO1p5MchaYkbhwJJnmskr1s4mxnlpdw7TPNWS0fPtOofSZtfX01nbz87epIzAMO5QxFn1NYO4JOTUJZpI724VVJWSWzr6qW0RMye4PQo2SrLSjnlqPqcHe5mRktHD6cuachrGZIo3SfU0tFDfU3RZfxxbvgaiaRvhL+/lHRr9mPyijj11CWtRtLZR6q2YlKalFY0NbKlrZttOw7sE2jr6qOnb2Ba1kjSWYCbPVWKK1Ij1Uj+M/y9ZjIKMp0MLbebkBpJax4nI2Y7vSllBfraAAAcq0lEQVQFwL2b2rjw5P1JpFvC8NfpNIckbf6cMCnRO9xdkRq2RmJm6UWrTzSzuzMfREvvujFKYh/JZAWSpYfV8qqZVQc1b6Vndk+HBa2yzago5bC6Ss8C7IpWnM72D+bY96EJLse0MtRHkpRAksc8W9kksaIpxX3PHpgupbm9Bwnm18+YlHIkzcKGaq+RuKI1Uh/JRZJ+CSzO6h9Zja/ZPi51lVGndhKatswsr5l/c1nZlGJnzz6efGF/UumW9h7mzqxKxLyaQlhQX+P5tlzRGqmP5H7gRSAFfDVjfyewPp+FmuqqyksoLVEi8m3t3hulR8n3ZMRMpy0N/SSb2zhh/mwgmtU+HftH0hY2VPPTR/eyd98AVeXTM5i64jVSH0mzmd1lZqdm9ZE8mpHG3Y2BJGoqShNRI2mbhPQo2VK1lRx7xEzu2bh/Pklze8+0yvqbzddvd8UsTtLGUyQ9LKlLUp+kAUm7J6NwU1ldVTldvQOFLsZQepTJ6iNJW9GU4tGWHXT39tPT109bV++0yrGVbX5GFmDnik2czvZvARcBm4AZwF8A38xnoaaD2spkpJKfrFnt2U5vamTfgPHQc+0ZI7amcY2k3tclccUrTiDBzDYDpWY2YGbXA2/Ob7GmvqQst9s2VCOZ3BnVr184h6ryEu7Z2LY/ffw07iOpr6mgtrLMA4krSnGy//aE9UQel/Rlog746dsGMUFqK8vY2dNX6GLQ1tVHaYmYUz25gaSqvJSTFzdw7+Y2jpwdDfmdzjUSSSyor/bZ7a4oxamRvB8oJVpbpJto1cMLRrzCjaq2qiwR80jaunppqJmc9CjZTm9KsfmVLh56roOZVWXMnuRgljQL6qt9UqIrSnGSNjaHzT3AF/JbnOmjLiGrJLZO4mTEbCtCupTf/eFljj1iVkHKkCQLG6r53R9eYWDQKJ1mqfRdcRs2kISVCYddIMHMDlp0ysVXm5BVEtu6evO+DslwXn14HY11lbR29k7rOSRpCxqq6RsY5OXdezli9vSc4e+K00hNW+8A/jfwm/B4X3jcxjCrGmaTdLakDZI2S7osx/GFklZJWi/pLknzMo4NSHo8PG7N2P99Sc9lHCvKvF+1VWX09A0ckCakENq6+ia9oz1NEivD5MTpmPU328KQZ8yHALtiM9qExGbgNDP7OzN7IjwuA9422o0llQLXAm8HlgEXSVqWddo1wI2hdnMlcHXGsT1mdmJ4vDPrus9kHHt89LeZPElI3GhmtHb1Tuqs9mwrj44CyXTuaE9Lj1pLZ0J2rljE6WyvkbQi/UTSm4g3autkYLOZbTGzPuAm4Jysc5YBq8L26hzHp6y6qsIHks7efvr6Byd1Vnu2P3rN4bzj+Lmc8erGgpUhKebOqqKsRF4jcUUnTiD5CHCtpOclPQ/8K9Hyu6M5Etia8Xxb2JdpHftHgJ0H1ElKL5FXJWmtpAclnZt13VWhOezrkgr3LTgOtQlI3FioWe2ZZlaV860/fR1zZ3mfQFlpCUfOmeFzSVzRGTWQmNkjZnYCcDxwQmhOejTGvXMNO8nuELgUOEPSY8AZwAtA+pt1gZktB/4U+IakJWH/5cAxwBuAeuCzOV9cujgEorWtrbnXCC+kmsooMV8hZ7e3JSCQuAMtqK/2QOKKzkijtv7MzH4g6dNZ+wEws6+Ncu9tRHNO0uYB2zNPMLPtwPnhvrXABWa2K+MYZrZF0l3AScCzZvZiuLxX0vVEweggZnYdcB3A8uXLC9ujncP+pq3C5dtq64omRE5mCnk3soUN1fxy3Yujn+hcgoxUI0n3g9QN8xjNw0CTpMVhZvyFwAFrvUtKSUqX4XLge2H/nHSTlaQUcBrwdHg+N/wVcC7wZIyyJE4SmrYKlWfLDW9BfTW79uxjV0/h87A5F9ewNRIz+3b4O6ZJiGbWL+kS4A6imfHfM7OnJF0JrDWzW4EzgaslGXAP8Ilw+WuAb0saJAp2XzSzp8OxH0pqJGo6exz4q7GUr9CG1m0vZNNWV29B0qO44aWXGm7u6Ob46tkFLo1z8YzUtPUvI11oZp8c7eZmdhvRvJPMfVdkbN9CjjkpZnY/cNww9zxrtNctBunhv50FrpHU11T4LOoE2T8EuIfj53kgccVhpBQpj0xaKaahJMwjKWR6FJfbAl+XxBWhkZq2bpjMgkw3pSWiusCrJLYWcFa7y62msoxUbSUtHkhcERk1aWPoj/gs0eTBqvT+qdLEVEiFzrfV1tnLkpSvCJA0C+pn0Oyz210RiTMh8YfAM8Biouy/zxONyHLjVMhU8mZW0ISNbngLG2rY2rGn0MVwLrY4gaTBzL4L7DOzu83sz4FT8lyuaaGQqeS7evvp7R8saJ4tl9uC+mq279pDb3/h5hg5dyjiBJL0+NQXJf0vSScRTS5041TI5XaH0qP4ZMTEWdhQjRls2+G1Elcc4iy1+8+SZgH/B/gmMBP427yWapqoqSijrbMwnapDs9q9RpI46ZFbLe09LGmsLXBpnBtdnEDyUEhbsgt4c57LM60Uskbis9qTK73Il6/f7opFnKat+yXdKekjkubkvUTTSF0BR215IEmuxtpKqitKafEOd1ck4mT/bQL+HjgWeETSryT9Wd5LNg2kayRmk59Tsq2zlxJBfY33kSSNpJAF2GskrjjEqZFgZr83s08TLVbVAfhkxQlQW1nOwKCxd9/gpL92a1cv9TWVnh4loRbUV/vsdlc0Rg0kkmZK+qCk24H7gReJAoobp3Tixs4CJG5s7fRZ7UmWXpdkcDBxKyA4d5A4ne3rgP8GrjSzB/JcnmmlLp1va28/h8VJzD+B2rp6C7rErhvZwoZqevsHae3q5fCZVaNf4FwBxQkkR1khGvGngUImbmzr6mWxp0dJrAUNIZ18e48HEpd4cTrbPYjkydCaJJM8uz2dHsVrJMm1sN6HALviEauz3eXH0Jokk1wj6e4bYO++Qe8jSbAjZs+gRPj67a4oeCApoLoC1UiG0qP4HJLEqigr4YjZM3zklisKcUZtfTmM3CqXtEpSW9x5JJLOlrRB0mZJl+U4vjDcc72kuyTNyzg2IOnx8Lg1Y/9iSQ9J2iTpJ2E9+KJUqD4Sn4xYHBY2VHuNxBWFODWSt5rZbuAdwDbgaOAzo10kqRS4Fng70VomF0lalnXaNcCNZnY8cCVwdcaxPWZ2Yni8M2P/l4Cvh4mSO4CPxHgPiVRTqEDiNZKisKC+xgOJKwpxAkl5+PsnwI/NrCPmvU8GNpvZFjPrA24Czsk6ZxmwKmyvznH8AJIEnMX+dd5vAM6NWZ7EqSwrobxUhauReObfRFvYUE1Hdx+deyd/npFzhyJOIPmlpD8Ay4FVYcXEvTGuOxLYmvF8W9iXaR1wQdg+D6iT1BCeV0laK+lBSelg0QDsNLP0N2+uewIg6eJw/drW1tYYxZ18kqJVEie7j6SrjxJBQ43XSJLM1293xSLO8N/LgFOB5Wa2D+hmlJpDkCv3RvZQ4kuBMyQ9BpwBvACkv1UXmNly4E+Bb0haEvOe6XJfZ2bLzWx5Y2NjjOIWRiEyALd29lJfU+HpURIuHUi2evOWS7g4ne3vBvrNbEDS3wM/AI6Ice9twPyM5/OA7ZknmNl2MzvfzE4CPhf27UofC3+3AHcBJwFtwGxJZcPds9jUVpbTOck1krauXu8fKQIL0+nkPZC4hIvTtPUPZtYpaQXwNqJ+iX+Lcd3DQFMYZVUBXAjcmnmCpJSkdBkuB74X9s+RVJk+BzgNeDpMjlwNvCtc80HgFzHKklhRKvnJbQP3QFIc6qrKqa+p8KYtl3hxAkl64ej/Bfybmf0CGLWXNvRjXALcATwD3GxmT0m6UlJ6FNaZwAZJG4HDgavC/tcAayWtIwocXzSzp8OxzwKflrSZqM/kuzHeQ2IVomkrCiTe0V4M5ns6eVcE4uTaekHSt4G3AF8KNYW46edvA27L2ndFxvYt7B+BlXnO/cBxw9xzC1Mo+3BtZRlbWuMFkit/+TSPbd0x7tfcvnMvZx/rNZJisLC+mjuffonz/vW+Qhdlwn3g1IWcd9K80U90iRcnkLwHOBu4xsx2SppLjHkkLp64NZL2rl6uv/85ljbW8qpZ40vit7IpxduPmzuue7jJ8Z7l89m5Z19BFj/Lpz+81Ml37nnOA8kUMWogMbMeSc8Cb5P0NmCNmd2Z/6JND3WVZbE62+/d3IYZXPPuEzhh/uxJKJlLghVNKVY0pQpdjAl37erNfOWODbR2evLQqSDOqK1PAT8EDguPH0j663wXbLqorSyjt3+Qvv6RV0m8Z2Mbs6vLee2RsyapZM7lz8oQHO9/tq3AJXETIU5fx0eAN5rZFaF/4xTgL/NbrOkjnSale4TmLTNjzaZWVixN+dwPNyUce8QsZleXc89GDyRTQZxAIvaP3CJs+7fZBBlak2SEQLLh5U5e6ezl9KbkTqx07lCUlojTlqa4d3PrlOv/mY7iBJLrgYckfV7S54EHKfIht0lSFyNx45rwq23l0VOvrdxNX6c3pXh5dy+bXukqdFHcOMVJkfI14MNAB1G23Q+b2TfyXbDpIk6N5J5NrTQdVsvcWTMmq1jO5d2KUMO+Z2Myc+G5+EYMJJJKJD1pZo+a2b+Y2f8zs8cmq3DTwdCaJMOM3Nq7b4CHnuvg9KO9WctNLUfOnsFRjTXcu9n7SYrdiIHEzAaBdZIWTFJ5pp30KonDLbf70HMd9PUPDo1ycW4qWbk0xYNb2untHxj9ZJdYcfpI5gJPhZUMb00/8l2w6aK2MlruZbgayZqNrVSUlfDGxQ05jztXzFY2NbJ33yCPNI8/Y4MrnDgz27+Q91JMY/v7SHInbrxnUysnL6pnRkXpZBbLuUlxypIGykrEmk1tvGmJ17qL1bA1EklLJZ1mZndnPojW/9g2eUWc2qrLS5Fy10he2rWXjS93cbqP1nJTVG1lGa9bMId7N3k/STEbqWnrG0Bnjv094ZibACUloraiLGcfyZpN0WiWlT5/xE1hK5pSPLl9Fx3dfYUuihujkQLJIjNbn73TzNYCi/JWommotir3crv3bGqjsa6SY15VV4BSOTc5VjalMIP7fPRW0RopkIyUYtYnNEygmsqDMwAPDhr3bmplZVMKyRMJuKnr+HmzmVlVNlQDd8VnpEDysKSDcmpJ+gjwSP6KNP3U5ggkT27fxY6efZ4WxU15Q+lSNrV5upQiNVIg+Rvgw5LukvTV8Lgb+AvgU3FuLulsSRskbZZ0WY7jC8Ow4vXhdeZlHZ8p6QVJ38rYd1e45+PhcVi8t5pcdTnWJFkTOh+nYgpx57KtbGpk+669PNvqq0EWo2GH/5rZy8CbJL0ZeG3Y/Wsz+12cG0sqBa4F/pholNfDkm7NWDIX4BrgRjO7QdJZwNXA+zOO/xNwd47bvy/01UwJtZVlvLRr7wH77t7YyrFHzPS11d20kJ5wu2ZTK0sPqy1wadyhipNra7WZfTM8YgWR4GRgs5ltMbM+4CbgnKxzlgGrwvbqzOOSXk+0jvuUX0Qru2mrq7efR5t3eFoUN23Mr69mUUO1DwMuUrHWXh+jI4GtGc+3hX2Z1gEXhO3zgDpJDZJKgK8y/JK+14dmrX/QFOiJzh619cCz7fQPmqdFcdPKiqYUD2xpH3WRN5c8+Qwkub7gs3vSLgXOkPQYcAbwAtAPfBy4zcy2crD3mdlxwMrweH+Oc5B0saS1kta2tiZ7NEhdZRldff0MDkYfz5pNrVRXlPL6hXMKXDLnJs/KpkZ6+gZ4rMXTpRSbfAaSbcD8jOfzgO2ZJ5jZdjM738xOAj4X9u0CTgUukfQ8UT/KByR9MRx/IfztBH5E1IR2EDO7zsyWm9nyxsZkNxHVVpVhBj37osR192xs5ZSjGqgs87Qobvo4dUkDpSFdiisu+QwkDwNNkhZLqgAuBA5I9igpFZqxAC4HvgdgZu8zswVmtoio1nKjmV0mqUxSKlxbDrwDeDKP72FSZCZubGnv4fn2Hk73Zi03zcysKufE+bNZ4xMTi07eAomZ9QOXAHcAzwA3m9lTkq6U9M5w2pnABkkbiTrWrxrltpXAHZLWA48TNYV9Jx/ln0yZiRvvSadF8Y52Nw2tbEqxfttOdvZ4upRiEif775iZ2W3AbVn7rsjYvgW4ZZR7fB/4ftjuBl4/0eUstPRyu517+1mzqTVa8CdVU+BSOTf5Vjal+Mb/bOL+Z9v5k+PmFro4LqZ8Nm25mNI1kp179nH/5nZOP9rTorjp6YR5s6mr9HQpxcYDSQLUVESB5N5NbXT29nu2XzdtlZWWcOqSBu7Z6OlSiokHkgRIL7d7+xMvUiI4zRf4cdPYyqMbeWHnHp5v7yl0UVxMHkgSoDb0kWzftZcT5s9mVnV5gUvkXOGsXLo/XYorDh5IEqCmcv+YB8/266a7hQ3VzK+f4fNJiogHkgSoKCuhsiz6T+HL6rrpThIrmxp54Nl29g14upRi4IEkIeqqyqirKuOEebMLXRTnCm7l0hRdvf2s27qz0EVxMeR1HomL71WzqjgqVUtZqcd25960JEWJ4OM/fJRZM7zPcDy++8E3sKChOq+v4YEkIb7/4ZOpKvfcWs4BzKou5zNvO4YnXvAayXhVlOX/x6kHkoTwBaycO9DHzlxS6CK4mLwdxTnn3Lh4IHHOOTcuHkicc86NiwcS55xz4+KBxDnn3Lh4IHHOOTcuHkicc86NiwcS55xz46LpsHiMpFagudDliCEFFFvK02Irc7GVF7zMk6XYyjwZ5V1oZqOmJJ8WgaRYSFprZssLXY5DUWxlLrbygpd5shRbmZNUXm/acs45Ny4eSJxzzo2LB5Jkua7QBRiDYitzsZUXvMyTpdjKnJjyeh+Jc865cfEaiXPOuXHxQJIQkp6X9ISkxyWtLXR5skn6nqRXJD2Zsa9e0m8lbQp/5xSyjNmGKfPnJb0QPufHJf1JIcuYTdJ8SaslPSPpKUmfCvsT+VmPUN7Efs6SqiT9XtK6UOYvhP2LJT0UPuOfSKoodFnTRijz9yU9l/E5n1iQ8nnTVjJIeh5YbmaJHMcu6XSgC7jRzF4b9n0Z6DCzL0q6DJhjZp8tZDkzDVPmzwNdZnZNIcs2HElzgblm9qikOuAR4FzgQyTwsx6hvO8hoZ+zJAE1ZtYlqRy4F/gU8GngZ2Z2k6R/B9aZ2b8VsqxpI5T5r4BfmdkthSyf10hcLGZ2D9CRtfsc4IawfQPRF0hiDFPmRDOzF83s0bDdCTwDHElCP+sRyptYFukKT8vDw4CzgPQXcmI+YxixzInggSQ5DLhT0iOSLi50YWI63MxehOgLBTiswOWJ6xJJ60PTVyKaiHKRtAg4CXiIIviss8oLCf6cJZVKehx4Bfgt8Cyw08z6wynbSFhAzC6zmaU/56vC5/x1SQVZs9sDSXKcZmavA94OfCI0y7iJ92/AEuBE4EXgq4UtTm6SaoGfAn9jZrsLXZ7R5Chvoj9nMxswsxOBecDJwGtynTa5pRpZdpklvRa4HDgGeANQDxSkudMDSUKY2fbw9xXg50T/cyfdy6GNPN1W/kqByzMqM3s5/IMcBL5DAj/n0Ab+U+CHZvazsDuxn3Wu8hbD5wxgZjuBu4BTgNmSysKhecD2QpVrJBllPjs0LZqZ9QLXU6DP2QNJAkiqCR2VSKoB3go8OfJViXAr8MGw/UHgFwUsSyzpL+PgPBL2OYdO1e8Cz5jZ1zIOJfKzHq68Sf6cJTVKmh22ZwBvIerbWQ28K5yWmM8Yhi3zHzJ+XIioT6cgn7OP2koASUcR1UIAyoAfmdlVBSzSQST9GDiTKOPoy8A/Av8N3AwsAFqAd5tZYjq3hynzmUTNLQY8D3w03feQBJJWAGuAJ4DBsPv/EvU7JO6zHqG8F5HQz1nS8USd6aVEP6ZvNrMrw7/Dm4iaiB4D/iz80i+4Ecr8O6AREPA48FcZnfKTVz4PJM4558bDm7acc86NiwcS55xz4+KBxDnn3Lh4IHHOOTcuHkicc86NiwcSNykkmaSvZjy/NCRQnIh7f1/Su0Y/c9yv8+6Q5XZ1jmNfCVlZvzKG+56YpOy4uUga05BSSedKWjZZr+cKwwOJmyy9wPmSUoUuSCZJpYdw+keAj5vZm3Mc+yjwOjP7zBiKcSJwSIFEkWL493sucMiBxBWXYvgf0U0N/URLg/5t9oHsGkX616ikMyXdLelmSRslfVHS+8K6DE9IWpJxm7dIWhPOe0e4vjTUFB4OSe0+mnHf1ZJ+RDSRLrs8F4X7PynpS2HfFcAK4N+zax2SbgVqgIckvTfMQv5peN2HJZ0WzjtZ0v2SHgt/X61ozYsrgfcqWk/ivYrW8rg04/5PSloUHs9I+lfgUWC+pLdKekDSo5L+K+S8InxWT4f3fVAqd0lnaP8aFo9lZFb4TMbn9YVc/yGHO0fSB8K+dZL+U9KbgHcCXwmvsyQ8fqMoOekaSceEaxeH9/GwpH/K9bouwczMH/7I+4NoXZCZRLOcZwGXAp8Px74PvCvz3PD3TGAnMBeoBF4AvhCOfQr4Rsb1vyH6YdRElLm1CrgY+PtwTiWwFlgc7tsNLM5RziOIZo43EmUZ+B1wbjh2F9GaMTnfX8b2j4AVYXsBUfoQwvsvC9tvAX4atj8EfCvj+s8Dl2Y8fxJYFB6DwClhfwq4h2idCogS9l1BNDN7A/snHM/OUd5fEiUKBagN7/WtRMFe4bP8FXB61n+TnOcAx4bXTIXz6of5b7sKaArbbwR+F7ZvBT4Qtj+R+Xn6I/mPdIIy5/LOzHZLuhH4JLAn5mUPW0itIelZ4M6w/wkgs4npZosSBG6StIUoI+pbgeMzajuziAJNH/B7M3sux+u9AbjLzFrDa/6Q6Ivyv2OWF6IgsUxS+vnM8It/FnCDpCai1CHlh3DPtGYzezBsn0LUbHRfeK0K4AFgN7AX+A9Jvyb6ss92H/C18P5+ZmbbJL2V6DN7LJxTS/R53ZNx3XDnnADcYmFhNsuRviXUlt4E/FfGZ5NOe34acEHY/k/gS6N+Ei4xPJC4yfYNomaZ6zP29ROaWRV9w2QucZqZ62gw4/kgB/7/m53rx4h+Nf+1md2ReUDSmUQ1klw0zP5DUQKcamYHBEtJ3wRWm9l5itbuuGuY64c+j6AqYzuz3CJal+Ki7BtIOhn4I+BC4BKiRZuGWLTS4q+J+mYelPSWcL+rzezbI7y3nOdI+iSjp10vIVrzY7jlYD1fU5HyPhI3qcIv1ZuJOq7TngdeH7bPYWy/1N8tqST0mxxF1MxyB/AxRWnOkXS0ouzKI3kIOENSKnTEXwTcfYhluZPoy5vwuukvzllEzXMQNWeldQJ1Gc+fB14Xrn0dUXNcLg8Cp0laGs6tDu+xFphlZrcBf0PUmX8ASUvM7Akz+xJRk98xRJ/Xn2f0sxwpKXsBreHOWQW8R1JD2F+f/d4sWqfkOUnvDudI0gnhvPuIgh7A+4Z5vy6hPJC4QvgqUft+2neIvrx/T9RuPlxtYSQbiL7wbyfKgLoX+A/gaeBRSU8C32aUWnhoRrucKKX4OuBRMzvUdOKfBJaHjuenidbVBvgycLWk+4iyuKatJmoKe1zSe4nW9qhXtBrex4CNw5S1lSgg/VjSeqLAcgzRF/evwr67yTHAAfib0Im/jqiZ8XYzu5Oof+cBSU8QLTubGeAY7hwzewq4Crg73DOdUv4m4DOhQ38JUZD4SDjnKaIfDhD1eX1C0sNEAdcVEc/+65xzbly8RuKcc25cPJA455wbFw8kzjnnxsUDiXPOuXHxQOKcc25cPJA455wbFw8kzjnnxsUDiXPOuXH5/wFl+NkiMlizAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (accuracy)\")\n",
    "plt.plot(\n",
    "    range(2, len(drfecv.grid_scores_) + 2),\n",
    "    drfecv.grid_scores_,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32, 34,  2,  5,  1,  6,  8, 33, 15, 22, 29, 28,  7,  4,  1,  3, 30,\n",
       "       10, 13, 23,  9, 16, 11, 35, 18, 21, 20, 17, 12, 24, 25, 26, 14, 31,\n",
       "       19, 27])"
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drfecv.ranking_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(drfecv.support_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "psqi_score 4\n",
      "hamd_hamd21_score 14\n"
     ]
    }
   ],
   "source": [
    "drfefn = []\n",
    "for i in range(len(drfecv.support_)):\n",
    "    if drfecv.support_[i] == True:\n",
    "        print(Xd.keys().tolist()[i] + \" \"+ str(i))\n",
    "        drfefn.append(Xd.keys().tolist()[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "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>psqi_score</th>\n",
       "      <th>hamd_hamd21_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>si</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA003</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA101</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA115</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA296</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA328</th>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       psqi_score  hamd_hamd21_score\n",
       "si                                  \n",
       "AA003         5.0                1.0\n",
       "AA101         5.0                2.0\n",
       "AA115         5.0                1.0\n",
       "AA296         5.0                0.0\n",
       "AA328        12.0               19.0"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd_rfe = Xd[drfefn]\n",
    "Xd_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for RFECV: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.93      0.93        14\n",
      "           1       0.94      0.94      0.94        18\n",
      "\n",
      "   micro avg       0.94      0.94      0.94        32\n",
      "   macro avg       0.94      0.94      0.94        32\n",
      "weighted avg       0.94      0.94      0.94        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Xd_train_rfe, Xd_test_rfe, yd_train_rfe, yd_test_rfe = train_test_split(Xd_rfe, yd, test_size=0.2, random_state=42)\n",
    "gnb_drfe = GaussianNB()\n",
    "gnb_drfe.fit(Xd_train_rfe, yd_train_rfe)\n",
    "gnb_p_drfe = gnb_drfe.predict(Xd_test_rfe)\n",
    "print(\"Gaussian Naive Bayes Classifier report for RFECV: \\n\\n\", classification_report(yd_test_rfe, gnb_p_drfe))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection on Gene Expression\n",
    "\n",
    "### Select K Best\n",
    "    - Chi square\n",
    "    - ANOVA f-value\n",
    "    - Mutual information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 10)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk10 = SelectKBest(chi2, k=10)\n",
    "sk10.fit_transform(X, y)\n",
    "X_k10 = X.iloc[:,sk10.get_support(indices=True)]\n",
    "X_p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CD83', 'DPEP3', 'FAM129B', 'HMGN2P46', 'MDGA1', 'NKX1-1', 'RARS2',\n",
       "       'TNC', 'UBD', 'ZDHHC20'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_k10.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 10)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk10f = SelectKBest(f_classif, k=10)\n",
    "sk10f.fit_transform(X, y)\n",
    "X_k10f = X.iloc[:,sk10f.get_support(indices=True)]\n",
    "X_k10f.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ARFGAP1', 'BCL2L12', 'CBL', 'FAM138A', 'IRF2BPL', 'KANTR', 'MDGA1',\n",
       "       'NPFF', 'UBD', 'ZDHHC20'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_k10f.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 10)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk10m = SelectKBest(mutual_info_classif, k=10)\n",
    "sk10m.fit_transform(X, y)\n",
    "X_k10m = X.iloc[:,sk10m.get_support(indices=True)]\n",
    "X_k10m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AASS', 'ARPC4', 'BAZ2A', 'FUNDC1', 'MKNK1-AS1', 'RNASEH1-AS1', 'RPL15',\n",
       "       'SNX18', 'SRSF7', 'ZMYM6NB'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_k10m.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for 10-best with chi square: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.70      0.50      0.58        14\n",
      "         1.0       0.68      0.83      0.75        18\n",
      "\n",
      "   micro avg       0.69      0.69      0.69        32\n",
      "   macro avg       0.69      0.67      0.67        32\n",
      "weighted avg       0.69      0.69      0.68        32\n",
      "\n",
      "Gaussian Naive Bayes Classifier report for 10-best with f-value: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.75      0.43      0.55        14\n",
      "         1.0       0.67      0.89      0.76        18\n",
      "\n",
      "   micro avg       0.69      0.69      0.69        32\n",
      "   macro avg       0.71      0.66      0.65        32\n",
      "weighted avg       0.70      0.69      0.67        32\n",
      "\n",
      "Gaussian Naive Bayes Classifier report for 10-best with mutual info: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.53      0.57      0.55        14\n",
      "         1.0       0.65      0.61      0.63        18\n",
      "\n",
      "   micro avg       0.59      0.59      0.59        32\n",
      "   macro avg       0.59      0.59      0.59        32\n",
      "weighted avg       0.60      0.59      0.59        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_k10, X_test_k10, y_train_k10, y_test_k10 = train_test_split(X_k10, y, test_size=0.2, random_state=42)\n",
    "X_train_k10f, X_test_k10f, y_train_k10f, y_test_k10f = train_test_split(X_k10f, y, test_size=0.2, random_state=42)\n",
    "X_train_k10m, X_test_k10m, y_train_k10m, y_test_k10m = train_test_split(X_k10m, y, test_size=0.2, random_state=42)\n",
    "\n",
    "gnb1,gnb2,gnb3 = GaussianNB(),GaussianNB(),GaussianNB()\n",
    "gnb1.fit(X_train_k10, y_train_k10)\n",
    "gnb2.fit(X_train_k10f, y_train_k10f)\n",
    "gnb3.fit(X_train_k10m, y_train_k10m)\n",
    "gnb_k10 = gnb1.predict(X_test_k10)\n",
    "print(\"Gaussian Naive Bayes Classifier report for 10-best with chi square: \\n\\n\", classification_report(y_test_k10, gnb_k10))\n",
    "gnb_k10f = gnb2.predict(X_test_k10f)\n",
    "print(\"Gaussian Naive Bayes Classifier report for 10-best with f-value: \\n\\n\", classification_report(y_test_k10f, gnb_k10f))\n",
    "gnb_k10m = gnb3.predict(X_test_k10m)\n",
    "print(\"Gaussian Naive Bayes Classifier report for 10-best with mutual info: \\n\\n\", classification_report(y_test_k10m, gnb_k10m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Percentile - %1\n",
    "    - Chi square\n",
    "    - ANOVA f-value\n",
    "    - Mutual information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 56)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp1 = SelectPercentile(chi2, percentile=1)\n",
    "sp1.fit_transform(X, y)\n",
    "X_1p = X.iloc[:,sp1.get_support(indices=True)]\n",
    "X_1p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ABCE1', 'AHCTF1P1', 'AHSG', 'AP4B1-AS1', 'BIRC5', 'C1orf35', 'CD83',\n",
       "       'CDC14B', 'CDC20', 'CHMP1B2P', 'CHP1', 'CHSY3', 'DDHD2', 'DDX26B-AS1',\n",
       "       'DDX58', 'DPEP3', 'EHF', 'ELK4', 'FAM129B', 'FLJ31356', 'FOXRED2',\n",
       "       'GSC', 'HMGN2P46', 'HNRNPA1P10', 'ICOS', 'IFI16', 'LINC01001', 'LUC7L2',\n",
       "       'MCTS2P', 'MDGA1', 'METTL4', 'MITF', 'MRPS26', 'MYO1A', 'NAA20',\n",
       "       'NKX1-1', 'NR1H2', 'OIP5', 'PAPL', 'POGZ', 'PPP2R5C', 'PTP4A2', 'RARS2',\n",
       "       'SS18L2', 'SUMO3', 'TAF1D', 'TNC', 'TOMM34', 'UBD', 'WDR76', 'ZDHHC20',\n",
       "       'ZFP36L2', 'ZMYM6NB', 'ZNF225', 'ZNF518B', 'ZNF549'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_1p.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 56)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp1f = SelectPercentile(f_classif, percentile=1)\n",
    "sp1f.fit_transform(X, y)\n",
    "X_1pf = X.iloc[:,sp1f.get_support(indices=True)]\n",
    "X_1pf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ABCE1', 'AP1G2', 'ARFGAP1', 'ATXN7L2', 'BCL2L12', 'C16orf74',\n",
       "       'C1orf35', 'CBL', 'CCAR2', 'CD83', 'CDC20', 'CDK5RAP2', 'CSPG4',\n",
       "       'DPEP3', 'DYNLRB1', 'EIF2D', 'ELP3', 'EXOSC2', 'FAM129B', 'FAM138A',\n",
       "       'FAM193A', 'HMGN2P46', 'HNRNPUL2', 'ICOS', 'IRF2BPL', 'ISX', 'KANTR',\n",
       "       'KPNA2', 'LEUTX', 'LRRC37BP1', 'MCM3AP-AS1', 'MDGA1', 'METAP1D',\n",
       "       'NDUFB1', 'NGRN', 'NPFF', 'PANX1', 'POGZ', 'PRPF31', 'RARS2', 'RCSD1',\n",
       "       'SETDB2', 'SRSF5', 'SS18L2', 'STX16-NPEPL1', 'SUPT7L', 'TADA3',\n",
       "       'TOMM34', 'TRPC5', 'TSC2', 'TSR3', 'UBD', 'ZDHHC20', 'ZFP36L2',\n",
       "       'ZMYM6NB', 'ZNF658'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_1pf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 56)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp1m = SelectPercentile(mutual_info_classif, percentile=1)\n",
    "sp1m.fit_transform(X, y)\n",
    "X_1pm = X.iloc[:,sp1m.get_support(indices=True)]\n",
    "X_1pm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AASS', 'ANAPC4', 'ARPC4', 'BAZ2A', 'C9orf116', 'CASP10', 'CCAR2',\n",
       "       'COPB2', 'COPS6', 'DNAJC11', 'EIF3A', 'FCGBP', 'FOXN3-AS1', 'FUNDC1',\n",
       "       'GSC', 'HADH', 'HNRNPA1P10', 'HSPE1', 'IREB2', 'KBTBD3', 'KDM1B',\n",
       "       'LEUTX', 'LIMK2', '8-Mar', 'MIIP', 'MKNK1-AS1', 'MPO', 'MRPS31',\n",
       "       'N4BP2L1', 'NAB2', 'NUDT6', 'PCYT1A', 'PTCH1', 'RCSD1', 'RNASEH1-AS1',\n",
       "       'RNASEH2B-AS1', 'RPL15', 'RRP9', 'SEC61A2', 'SEPW1', 'SLC35A5', 'SNX18',\n",
       "       'SRSF7', 'STYXL1', 'SUPT7L', 'TAB3', 'TBCA', 'TRIM39', 'TYRP1', 'UBL4B',\n",
       "       'UBR4', 'VPS18', 'WSCD1', 'ZBTB11-AS1', 'ZFP91-CNTF', 'ZMYM6NB'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_1pm.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for best %1 with chi square: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.60      0.43      0.50        14\n",
      "         1.0       0.64      0.78      0.70        18\n",
      "\n",
      "   micro avg       0.62      0.62      0.62        32\n",
      "   macro avg       0.62      0.60      0.60        32\n",
      "weighted avg       0.62      0.62      0.61        32\n",
      "\n",
      "Gaussian Naive Bayes Classifier report for best %1 with f-value: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.67      0.43      0.52        14\n",
      "         1.0       0.65      0.83      0.73        18\n",
      "\n",
      "   micro avg       0.66      0.66      0.66        32\n",
      "   macro avg       0.66      0.63      0.63        32\n",
      "weighted avg       0.66      0.66      0.64        32\n",
      "\n",
      "Gaussian Naive Bayes Classifier report for best %1 with mutual info: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.38      0.36      0.37        14\n",
      "         1.0       0.53      0.56      0.54        18\n",
      "\n",
      "   micro avg       0.47      0.47      0.47        32\n",
      "   macro avg       0.46      0.46      0.46        32\n",
      "weighted avg       0.46      0.47      0.47        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_1p, X_test_1p, y_train_1p, y_test_1p = train_test_split(X_1p, y, test_size=0.2, random_state=42)\n",
    "X_train_1pf, X_test_1pf, y_train_1pf, y_test_1pf = train_test_split(X_1pf, y, test_size=0.2, random_state=42)\n",
    "X_train_1pm, X_test_1pm, y_train_1pm, y_test_1pm = train_test_split(X_1pm, y, test_size=0.2, random_state=42)\n",
    "\n",
    "gnb4,gnb5,gnb6 = GaussianNB(),GaussianNB(),GaussianNB()\n",
    "gnb4.fit(X_train_1p, y_train_1p)\n",
    "gnb5.fit(X_train_1pf, y_train_1pf)\n",
    "gnb6.fit(X_train_1pm, y_train_1pm)\n",
    "gnb_1p = gnb4.predict(X_test_1p)\n",
    "print(\"Gaussian Naive Bayes Classifier report for best %1 with chi square: \\n\\n\", classification_report(y_test_1p, gnb_1p))\n",
    "gnb_1pf = gnb5.predict(X_test_1pf)\n",
    "print(\"Gaussian Naive Bayes Classifier report for best %1 with f-value: \\n\\n\", classification_report(y_test_1pf, gnb_1pf))\n",
    "gnb_1pm = gnb6.predict(X_test_1pm)\n",
    "print(\"Gaussian Naive Bayes Classifier report for best %1 with mutual info: \\n\\n\", classification_report(y_test_1pm, gnb_1pm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select Percentile %0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 6)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp01f = SelectPercentile(f_classif, percentile=0.1)\n",
    "sp01f.fit_transform(X, y)\n",
    "X_01pf = X.iloc[:,sp01f.get_support(indices=True)]\n",
    "X_01pf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ARFGAP1', 'FAM138A', 'IRF2BPL', 'MDGA1', 'NPFF', 'ZDHHC20'], dtype='object')"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_01pf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for best %0.1 with f-value: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.70      0.50      0.58        14\n",
      "         1.0       0.68      0.83      0.75        18\n",
      "\n",
      "   micro avg       0.69      0.69      0.69        32\n",
      "   macro avg       0.69      0.67      0.67        32\n",
      "weighted avg       0.69      0.69      0.68        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_01pf, X_test_01pf, y_train_01pf, y_test_01pf = train_test_split(X_01pf, y, test_size=0.2, random_state=42)\n",
    "gnb7 = GaussianNB()\n",
    "gnb7.fit(X_train_01pf, y_train_01pf)\n",
    "gnb_p_01pf = gnb7.predict(X_test_01pf)\n",
    "print(\"Gaussian Naive Bayes Classifier report for best %0.1 with f-value: \\n\\n\", classification_report(y_test_01pf, gnb_p_01pf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### False Positive Rate\n",
    "    - Chi square\n",
    "    - ANOVA f-value\n",
    "Mutual information is not included for this method due to NoneType values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr = SelectFpr(chi2, alpha=0.79)\n",
    "fpr.fit_transform(X, y)\n",
    "fprf = SelectFpr(f_classif, alpha=0.001)\n",
    "fprf.fit_transform(X, y)\n",
    "#fprm = SelectFpr(mutual_info_classif, alpha=1)\n",
    "#fprm.fit_transform(X, y)\n",
    "X_fpr = X.iloc[:,fpr.get_support(indices=True)]\n",
    "X_fprf = X.iloc[:,fprf.get_support(indices=True)]\n",
    "#X_fprm = X.iloc[:,fprm.get_support(indices=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ABCE1', 'AHCTF1P1', 'AHSG', 'AP4B1-AS1', 'BIRC5', 'C1orf35', 'CD83',\n",
       "       'CDC14B', 'CDC20', 'DDHD2', 'DDX26B-AS1', 'DPEP3', 'EHF', 'FAM129B',\n",
       "       'FLJ31356', 'FOXRED2', 'GSC', 'HMGN2P46', 'HNRNPA1P10', 'ICOS', 'IFI16',\n",
       "       'LINC01001', 'LUC7L2', 'MDGA1', 'METTL4', 'MRPS26', 'NAA20', 'NKX1-1',\n",
       "       'NR1H2', 'OIP5', 'PAPL', 'POGZ', 'RARS2', 'SS18L2', 'SUMO3', 'TNC',\n",
       "       'UBD', 'WDR76', 'ZDHHC20', 'ZFP36L2', 'ZMYM6NB', 'ZNF225', 'ZNF518B',\n",
       "       'ZNF549'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_fpr.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ABCE1', 'ARFGAP1', 'ATXN7L2', 'BCL2L12', 'C16orf74', 'C1orf35', 'CBL',\n",
       "       'CCAR2', 'CD83', 'CDK5RAP2', 'CSPG4', 'DPEP3', 'DYNLRB1', 'EIF2D',\n",
       "       'ELP3', 'FAM138A', 'FAM193A', 'HMGN2P46', 'HNRNPUL2', 'ICOS', 'IRF2BPL',\n",
       "       'ISX', 'KANTR', 'LEUTX', 'MCM3AP-AS1', 'MDGA1', 'NDUFB1', 'NGRN',\n",
       "       'NPFF', 'PANX1', 'POGZ', 'RARS2', 'RCSD1', 'SETDB2', 'SRSF5', 'SS18L2',\n",
       "       'STX16-NPEPL1', 'SUPT7L', 'TADA3', 'TOMM34', 'TRPC5', 'TSC2', 'TSR3',\n",
       "       'UBD', 'ZDHHC20', 'ZFP36L2', 'ZMYM6NB', 'ZNF658'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_fprf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 765,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 765,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_fprf.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for FPR chi square: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.60      0.43      0.50        14\n",
      "         1.0       0.64      0.78      0.70        18\n",
      "\n",
      "   micro avg       0.62      0.62      0.62        32\n",
      "   macro avg       0.62      0.60      0.60        32\n",
      "weighted avg       0.62      0.62      0.61        32\n",
      "\n",
      "Gaussian Naive Bayes Classifier report for FPR f-value: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.67      0.43      0.52        14\n",
      "         1.0       0.65      0.83      0.73        18\n",
      "\n",
      "   micro avg       0.66      0.66      0.66        32\n",
      "   macro avg       0.66      0.63      0.63        32\n",
      "weighted avg       0.66      0.66      0.64        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_fpr, X_test_fpr, y_train_fpr, y_test_fpr = train_test_split(X_fpr, y, test_size=0.2, random_state=42)\n",
    "X_train_fprf, X_test_fprf, y_train_fprf, y_test_fprf = train_test_split(X_fprf, y, test_size=0.2, random_state=42)\n",
    "\n",
    "gnb_fpr,gnb_fprf = GaussianNB(),GaussianNB()\n",
    "gnb_fpr.fit(X_train_fpr, y_train_fpr)\n",
    "gnb_fprf.fit(X_train_fprf, y_train_fprf)\n",
    "gnb_p_fpr = gnb_fpr.predict(X_test_fpr)\n",
    "print(\"Gaussian Naive Bayes Classifier report for FPR chi square: \\n\\n\", classification_report(y_test_fpr, gnb_p_fpr))\n",
    "gnb_p_fprf = gnb_fprf.predict(X_test_fprf)\n",
    "print(\"Gaussian Naive Bayes Classifier report for FPR f-value: \\n\\n\", classification_report(y_test_fprf, gnb_p_fprf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### False Discovery Rate\n",
    "    - ANOVA f-value\n",
    "Chi square is excluded since there is no significance found in false discoveries with this method.\n",
    "\n",
    "Mutual information is not included for this method due to NoneType values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\base.py:80: UserWarning: No features were selected: either the data is too noisy or the selection test too strict.\n",
      "  UserWarning)\n"
     ]
    }
   ],
   "source": [
    "fdr = SelectFdr(chi2, alpha=0.99)\n",
    "fdr.fit_transform(X, y)\n",
    "fdrf = SelectFdr(f_classif, alpha=0.11)\n",
    "fdrf.fit_transform(X, y)\n",
    "#fdrm = SelectFdr(mutual_info_classif, alpha=1)\n",
    "#fdrm.fit_transform(X, y)\n",
    "X_fdr = X.iloc[:,fdr.get_support(indices=True)]\n",
    "X_fdrf = X.iloc[:,fdrf.get_support(indices=True)]\n",
    "#X_fdrm = X.iloc[:,fdrm.get_support(indices=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_fdr.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ABCE1', 'ARFGAP1', 'ATXN7L2', 'BCL2L12', 'C16orf74', 'CBL', 'CCAR2',\n",
       "       'CD83', 'CDK5RAP2', 'CSPG4', 'DPEP3', 'DYNLRB1', 'EIF2D', 'FAM138A',\n",
       "       'FAM193A', 'HMGN2P46', 'HNRNPUL2', 'ICOS', 'IRF2BPL', 'ISX', 'KANTR',\n",
       "       'MDGA1', 'NDUFB1', 'NGRN', 'NPFF', 'PANX1', 'POGZ', 'RARS2', 'RCSD1',\n",
       "       'SETDB2', 'SRSF5', 'SS18L2', 'STX16-NPEPL1', 'SUPT7L', 'TADA3',\n",
       "       'TOMM34', 'TRPC5', 'TSC2', 'TSR3', 'UBD', 'ZDHHC20', 'ZFP36L2',\n",
       "       'ZMYM6NB', 'ZNF658'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_fdrf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 766,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_fdrf.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for best FDR f-value: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.75      0.43      0.55        14\n",
      "         1.0       0.67      0.89      0.76        18\n",
      "\n",
      "   micro avg       0.69      0.69      0.69        32\n",
      "   macro avg       0.71      0.66      0.65        32\n",
      "weighted avg       0.70      0.69      0.67        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_fdrf, X_test_fdrf, y_train_fdrf, y_test_fdrf = train_test_split(X_fdrf, y, test_size=0.2, random_state=42)\n",
    "gnb_fdrf = GaussianNB()\n",
    "gnb_fdrf.fit(X_train_fdrf, y_train_fdrf)\n",
    "gnb_p_fdrf = gnb_fdrf.predict(X_test_fdrf)\n",
    "print(\"Gaussian Naive Bayes Classifier report for best FDR f-value: \\n\\n\", classification_report(y_test_fdrf, gnb_p_fdrf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Family Wise Error\n",
    "    - ANOVA f-value\n",
    "Chi square is excluded since there is no significance found in false discoveries with this method.\n",
    "\n",
    "Mutual information is not included for this method due to NoneType values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\base.py:80: UserWarning: No features were selected: either the data is too noisy or the selection test too strict.\n",
      "  UserWarning)\n"
     ]
    }
   ],
   "source": [
    "fwe = SelectFwe(chi2, alpha=0.99)\n",
    "fwe.fit_transform(X, y)\n",
    "fwef = SelectFwe(f_classif, alpha=0.99)\n",
    "fwef.fit_transform(X, y)\n",
    "#fwem = SelectFwe(mutual_info_classif, alpha=1)\n",
    "#fwem.fit_transform(X, y)\n",
    "X_fwe = X.iloc[:,fwe.get_support(indices=True)]\n",
    "X_fwef = X.iloc[:,fwef.get_support(indices=True)]\n",
    "#X_fwem = X.iloc[:,fwem.get_support(indices=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_fwe.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ARFGAP1', 'MDGA1', 'NPFF', 'ZDHHC20'], dtype='object')"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_fwef.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for best FWE f-value: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.60      0.43      0.50        14\n",
      "         1.0       0.64      0.78      0.70        18\n",
      "\n",
      "   micro avg       0.62      0.62      0.62        32\n",
      "   macro avg       0.62      0.60      0.60        32\n",
      "weighted avg       0.62      0.62      0.61        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_fwef, X_test_fwef, y_train_fwef, y_test_fwef = train_test_split(X_fwef, y, test_size=0.2, random_state=42)\n",
    "gnb_fwef = GaussianNB()\n",
    "gnb_fwef.fit(X_train_fwef, y_train_fwef)\n",
    "gnb_p_fwef = gnb_fwef.predict(X_test_fwef)\n",
    "print(\"Gaussian Naive Bayes Classifier report for best FWE f-value: \\n\\n\", classification_report(y_test_fwef, gnb_p_fwef))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive Feature Elimination with Cross Validation (RFECV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:665: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_folds = np.zeros(n_samples, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:437: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:437: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:163: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  support_ = np.ones(n_features, dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:164: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ranking_ = np.ones(n_features, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:163: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  support_ = np.ones(n_features, dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:164: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ranking_ = np.ones(n_features, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:163: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  support_ = np.ones(n_features, dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\rfe.py:164: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ranking_ = np.ones(n_features, dtype=np.int)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 4\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "\n",
    "rfecv = RFECV(\n",
    "    estimator=svc,\n",
    "    step=1,\n",
    "    cv=StratifiedKFold(2),\n",
    "    scoring=\"accuracy\",\n",
    "    min_features_to_select=2,\n",
    ")\n",
    "rfecv.fit(X, y)\n",
    "\n",
    "print(\"Optimal number of features : %d\" % rfecv.n_features_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (accuracy)\")\n",
    "plt.plot(\n",
    "    range(2, len(rfecv.grid_scores_) + 2),\n",
    "    rfecv.grid_scores_,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5251,  834, 5443, ..., 1538, 3434, 4062])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfecv.ranking_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5587"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rfecv.support_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CDC14B 771\n",
      "ELP3 1445\n",
      "MICU1 2664\n",
      "TUSC3 5034\n"
     ]
    }
   ],
   "source": [
    "rfegn = []\n",
    "for i in range(len(rfecv.support_)):\n",
    "    if rfecv.support_[i] == True:\n",
    "        print(X.keys().tolist()[i] + \" \"+ str(i))\n",
    "        rfegn.append(X.keys().tolist()[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>CDC14B</th>\n",
       "      <th>ELP3</th>\n",
       "      <th>MICU1</th>\n",
       "      <th>TUSC3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.14148</td>\n",
       "      <td>6.02133</td>\n",
       "      <td>5.13999</td>\n",
       "      <td>4.69555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>5.92282</td>\n",
       "      <td>6.2366</td>\n",
       "      <td>4.94526</td>\n",
       "      <td>4.75867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>5.78886</td>\n",
       "      <td>5.8695</td>\n",
       "      <td>5.21779</td>\n",
       "      <td>4.79789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>5.20258</td>\n",
       "      <td>5.88696</td>\n",
       "      <td>5.69822</td>\n",
       "      <td>4.96119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3265</td>\n",
       "      <td>6.05481</td>\n",
       "      <td>5.40493</td>\n",
       "      <td>4.6487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CDC14B     ELP3    MICU1    TUSC3\n",
       "0    6.14148  6.02133  5.13999  4.69555\n",
       "0.1  5.92282   6.2366  4.94526  4.75867\n",
       "0.2  5.78886   5.8695  5.21779  4.79789\n",
       "0.3  5.20258  5.88696  5.69822  4.96119\n",
       "1     6.3265  6.05481  5.40493   4.6487"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_rfe = X[rfegn]\n",
    "X_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the dataset, X: 125\n",
      "Length of the targets, y: 125\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe, X_test_rfe, y_train_rfe, y_test_rfe = train_test_split(X_rfe, y, test_size=0.2, random_state=42)\n",
    "print(\"Length of the dataset, X: %d\" % len(X_train_rfe))\n",
    "print(\"Length of the targets, y: %d\" % len(y_train_rfe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for RFECV: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.60      0.64      0.62        14\n",
      "         1.0       0.71      0.67      0.69        18\n",
      "\n",
      "   micro avg       0.66      0.66      0.66        32\n",
      "   macro avg       0.65      0.65      0.65        32\n",
      "weighted avg       0.66      0.66      0.66        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gnb_rfe = GaussianNB()\n",
    "gnb_rfe.fit(X_train_rfe, y_train_rfe)\n",
    "gnb_p_rfe = gnb_rfe.predict(X_test_rfe)\n",
    "print(\"Gaussian Naive Bayes Classifier report for RFECV: \\n\\n\", classification_report(y_test_rfe, gnb_p_rfe))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L-1 Based Feature Selection with SelectFromModel\n",
    "\n",
    "Threshold parameter selects features with greater \"mean\" than the given value. When set to 0.1, results with 5 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(157, 5)"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfm = SelectFromModel(estimator=LinearSVC(C=0.1, penalty=\"l1\", dual=False),threshold=0.1).fit(X, y)\n",
    "X_sfm = X.iloc[:,sfm.get_support(indices=True)]\n",
    "X_sfm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GSC', 'HMGN2P46', 'MDGA1', 'PANX1', 'UBD'], dtype='object')"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for SFM L1: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.64      0.50      0.56        14\n",
      "         1.0       0.67      0.78      0.72        18\n",
      "\n",
      "   micro avg       0.66      0.66      0.66        32\n",
      "   macro avg       0.65      0.64      0.64        32\n",
      "weighted avg       0.65      0.66      0.65        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_sfm, X_test_sfm, y_train_sfm, y_test_sfm = train_test_split(X_sfm, y, test_size=0.2, random_state=42)\n",
    "gnb8 = GaussianNB()\n",
    "gnb8.fit(X_train_sfm, y_train_sfm)\n",
    "gnb_p_sfm = gnb8.predict(X_test_sfm)\n",
    "print(\"Gaussian Naive Bayes Classifier report for SFM L1: \\n\\n\", classification_report(y_test_sfm, gnb_p_sfm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Tree Based Feature Selection with SelectFromModel\n",
    "\n",
    "Threshold parameter selects features with greater \"mean\" than the given value. When set to 0.015, results with 6 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:487: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_store_unique_indices = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(157, 8)"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfmt = SelectFromModel(estimator=RandomForestClassifier(),threshold=0.015).fit(X, y)\n",
    "X_sfmt = X.iloc[:,sfmt.get_support(indices=True)]\n",
    "X_sfmt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['C6orf118', 'ELK4', 'FAM109A', 'FOXL1', 'N4BP2', 'UBE2Q1', 'ZDHHC20',\n",
       "       'ZFYVE27'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sfmt.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for SFM Tree: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.67      0.57      0.62        14\n",
      "         1.0       0.70      0.78      0.74        18\n",
      "\n",
      "   micro avg       0.69      0.69      0.69        32\n",
      "   macro avg       0.68      0.67      0.68        32\n",
      "weighted avg       0.69      0.69      0.68        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_sfmt, X_test_sfmt, y_train_sfmt, y_test_sfmt = train_test_split(X_sfmt, y, test_size=0.2, random_state=42)\n",
    "gnb_sfmt = GaussianNB()\n",
    "gnb_sfmt.fit(X_train_sfmt, y_train_sfmt)\n",
    "gnb_p_sfmt = gnb_sfmt.predict(X_test_sfmt)\n",
    "print(\"Gaussian Naive Bayes Classifier report for SFM Tree: \\n\\n\", classification_report(y_test_sfmt, gnb_p_sfmt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding most common genes from different feature selection methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MDGA1', 'ZDHHC20', 'UBD', 'HMGN2P46', 'ZMYM6NB', 'ARFGAP1', 'NPFF', 'CD83', 'DPEP3', 'RARS2']\n"
     ]
    }
   ],
   "source": [
    "keys = [X_01pf, X_1p, X_1pf, X_1pm, X_fdr, X_fdrf, X_fpr, X_fprf, X_fwe, X_fwef, X_k10, X_k10f, X_k10m, X_rfe, X_sfm, X_sfmt]\n",
    "nkeys = []\n",
    "for i in keys:\n",
    "    for j in i.keys().tolist():\n",
    "        nkeys.append(j)\n",
    "from collections import Counter\n",
    "c = Counter(nkeys)\n",
    "gns = np.asarray(c.most_common(10)).T[0].tolist()\n",
    "print(gns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('MDGA1', 10),\n",
       " ('ZDHHC20', 10),\n",
       " ('UBD', 8),\n",
       " ('HMGN2P46', 7),\n",
       " ('ZMYM6NB', 7),\n",
       " ('ARFGAP1', 6),\n",
       " ('NPFF', 6),\n",
       " ('CD83', 6),\n",
       " ('DPEP3', 6),\n",
       " ('RARS2', 6),\n",
       " ('FAM138A', 5),\n",
       " ('IRF2BPL', 5),\n",
       " ('ABCE1', 5),\n",
       " ('ICOS', 5),\n",
       " ('POGZ', 5)]"
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "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>MDGA1</th>\n",
       "      <th>ZDHHC20</th>\n",
       "      <th>UBD</th>\n",
       "      <th>HMGN2P46</th>\n",
       "      <th>ZMYM6NB</th>\n",
       "      <th>ARFGAP1</th>\n",
       "      <th>NPFF</th>\n",
       "      <th>CD83</th>\n",
       "      <th>DPEP3</th>\n",
       "      <th>RARS2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA003</th>\n",
       "      <td>6.93689</td>\n",
       "      <td>5.46878</td>\n",
       "      <td>10.3297</td>\n",
       "      <td>7.18671</td>\n",
       "      <td>6.57007</td>\n",
       "      <td>7.96652</td>\n",
       "      <td>6.63618</td>\n",
       "      <td>5.72517</td>\n",
       "      <td>6.7784</td>\n",
       "      <td>5.29601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA101</th>\n",
       "      <td>6.85034</td>\n",
       "      <td>5.58767</td>\n",
       "      <td>10.4978</td>\n",
       "      <td>7.23615</td>\n",
       "      <td>6.40964</td>\n",
       "      <td>7.81677</td>\n",
       "      <td>6.55868</td>\n",
       "      <td>5.51419</td>\n",
       "      <td>6.78086</td>\n",
       "      <td>5.1249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA115</th>\n",
       "      <td>6.80861</td>\n",
       "      <td>5.84074</td>\n",
       "      <td>10.5517</td>\n",
       "      <td>6.89765</td>\n",
       "      <td>6.41542</td>\n",
       "      <td>7.90836</td>\n",
       "      <td>6.79757</td>\n",
       "      <td>5.50925</td>\n",
       "      <td>6.90754</td>\n",
       "      <td>5.09822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA296</th>\n",
       "      <td>6.56737</td>\n",
       "      <td>5.78972</td>\n",
       "      <td>10.7572</td>\n",
       "      <td>6.97251</td>\n",
       "      <td>6.29413</td>\n",
       "      <td>8.10685</td>\n",
       "      <td>6.56952</td>\n",
       "      <td>5.4127</td>\n",
       "      <td>6.37762</td>\n",
       "      <td>5.34857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA328</th>\n",
       "      <td>7.03226</td>\n",
       "      <td>5.78293</td>\n",
       "      <td>10.9429</td>\n",
       "      <td>6.90454</td>\n",
       "      <td>6.55422</td>\n",
       "      <td>7.86732</td>\n",
       "      <td>6.56504</td>\n",
       "      <td>5.75105</td>\n",
       "      <td>6.6797</td>\n",
       "      <td>5.17789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MDGA1  ZDHHC20      UBD HMGN2P46  ZMYM6NB  ARFGAP1     NPFF     CD83  \\\n",
       "AA003  6.93689  5.46878  10.3297  7.18671  6.57007  7.96652  6.63618  5.72517   \n",
       "AA101  6.85034  5.58767  10.4978  7.23615  6.40964  7.81677  6.55868  5.51419   \n",
       "AA115  6.80861  5.84074  10.5517  6.89765  6.41542  7.90836  6.79757  5.50925   \n",
       "AA296  6.56737  5.78972  10.7572  6.97251  6.29413  8.10685  6.56952   5.4127   \n",
       "AA328  7.03226  5.78293  10.9429  6.90454  6.55422  7.86732  6.56504  5.75105   \n",
       "\n",
       "         DPEP3    RARS2  \n",
       "AA003   6.7784  5.29601  \n",
       "AA101  6.78086   5.1249  \n",
       "AA115  6.90754  5.09822  \n",
       "AA296  6.37762  5.34857  \n",
       "AA328   6.6797  5.17789  "
      ]
     },
     "execution_count": 582,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xn = X[gns]\n",
    "Xn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for selected genes: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.80      0.57      0.67        14\n",
      "         1.0       0.73      0.89      0.80        18\n",
      "\n",
      "   micro avg       0.75      0.75      0.75        32\n",
      "   macro avg       0.76      0.73      0.73        32\n",
      "weighted avg       0.76      0.75      0.74        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Xn_train, Xn_test, yn_train, yn_test = train_test_split(Xn, y, test_size=0.2, random_state=42)\n",
    "ngnb = GaussianNB()\n",
    "ngnb.fit(Xn_train, y_train)\n",
    "ngnb_p = ngnb.predict(Xn_test)\n",
    "print(\"Gaussian Naive Bayes Classifier report for selected genes: \\n\\n\", classification_report(yn_test, ngnb_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 767,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:665: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_folds = np.zeros(n_samples, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:437: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:437: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.38461538, 0.61538462, 0.69230769, 0.53846154, 0.69230769,\n",
       "       0.66666667, 0.58333333, 0.66666667, 0.5       , 0.75      ])"
      ]
     },
     "execution_count": 767,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(random_state=0)\n",
    "cross_val_score(clf, X_train, y_train, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:665: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_folds = np.zeros(n_samples, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:437: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:437: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:113: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.53846154, 0.61538462, 0.61538462, 0.46153846, 0.61538462,\n",
       "       0.33333333, 0.58333333, 0.66666667, 0.66666667, 0.5       ])"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nclf = DecisionTreeClassifier(random_state=0)\n",
    "cross_val_score(nclf, Xn_train, yn_train, cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.50      0.43      0.46        14\n",
      "         1.0       0.60      0.67      0.63        18\n",
      "\n",
      "   micro avg       0.56      0.56      0.56        32\n",
      "   macro avg       0.55      0.55      0.55        32\n",
      "weighted avg       0.56      0.56      0.56        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "gnb_p = gnb.predict(X_test)\n",
    "print(\"Gaussian Naive Bayes Classifier report: \\n\\n\", classification_report(y_test, gnb_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Classifier report for selected genes: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.80      0.57      0.67        14\n",
      "         1.0       0.73      0.89      0.80        18\n",
      "\n",
      "   micro avg       0.75      0.75      0.75        32\n",
      "   macro avg       0.76      0.73      0.73        32\n",
      "weighted avg       0.76      0.75      0.74        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ngnb = GaussianNB()\n",
    "ngnb.fit(Xn_train, yn_train)\n",
    "ngnb_p = ngnb.predict(Xn_test)\n",
    "print(\"Gaussian Naive Bayes Classifier report for selected genes: \\n\\n\", classification_report(yn_test, ngnb_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbor Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.53      0.57      0.55        14\n",
      "         1.0       0.65      0.61      0.63        18\n",
      "\n",
      "   micro avg       0.59      0.59      0.59        32\n",
      "   macro avg       0.59      0.59      0.59        32\n",
      "weighted avg       0.60      0.59      0.59        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\base.py:907: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  self._y = np.empty(y.shape, dtype=np.int)\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(13)\n",
    "knn.fit(X_train, y_train)\n",
    "knn_p = knn.predict(X_test)\n",
    "print(\"K-Nearest Neighbor Classifier report: \\n\\n\", classification_report(y_test, knn_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbor Classifier report for selected genes: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.67      0.86      0.75        14\n",
      "         1.0       0.86      0.67      0.75        18\n",
      "\n",
      "   micro avg       0.75      0.75      0.75        32\n",
      "   macro avg       0.76      0.76      0.75        32\n",
      "weighted avg       0.77      0.75      0.75        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\base.py:907: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  self._y = np.empty(y.shape, dtype=np.int)\n"
     ]
    }
   ],
   "source": [
    "nknn = KNeighborsClassifier(6)\n",
    "nknn.fit(Xn_train, yn_train)\n",
    "nknn_p = nknn.predict(Xn_test)\n",
    "print(\"K-Nearest Neighbor Classifier report for selected genes: \\n\\n\", classification_report(yn_test, nknn_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear SVC report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.42      0.57      0.48        14\n",
      "         1.0       0.54      0.39      0.45        18\n",
      "\n",
      "   micro avg       0.47      0.47      0.47        32\n",
      "   macro avg       0.48      0.48      0.47        32\n",
      "weighted avg       0.49      0.47      0.47        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py:283: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  indices = (scores > 0).astype(np.int)\n"
     ]
    }
   ],
   "source": [
    "lsvc = LinearSVC(C=0.1, penalty=\"l1\", dual=False)\n",
    "lsvc.fit(X_train, y_train)\n",
    "lsvc_p = lsvc.predict(X_test)\n",
    "print(\"Linear SVC report: \\n\\n\", classification_report(y_test, lsvc_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 748,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear SVC report for selected genes: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.73      0.57      0.64        14\n",
      "         1.0       0.71      0.83      0.77        18\n",
      "\n",
      "   micro avg       0.72      0.72      0.72        32\n",
      "   macro avg       0.72      0.70      0.70        32\n",
      "weighted avg       0.72      0.72      0.71        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py:283: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  indices = (scores > 0).astype(np.int)\n"
     ]
    }
   ],
   "source": [
    "nlsvc = LinearSVC(C=0.25, penalty=\"l1\", dual=False)\n",
    "nlsvc.fit(Xn_train, yn_train)\n",
    "nlsvc_p = nlsvc.predict(Xn_test)\n",
    "print(\"Linear SVC report for selected genes: \\n\\n\", classification_report(yn_test, nlsvc_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RBF kernel SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBF kernel SVC report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.44      1.00      0.61        14\n",
      "         1.0       0.00      0.00      0.00        18\n",
      "\n",
      "   micro avg       0.44      0.44      0.44        32\n",
      "   macro avg       0.22      0.50      0.30        32\n",
      "weighted avg       0.19      0.44      0.27        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "rsvc = SVC(gamma=0.15, C=1)\n",
    "rsvc.fit(X_train, y_train)\n",
    "rsvc_p = rsvc.predict(X_test)\n",
    "print(\"RBF kernel SVC report: \\n\\n\", classification_report(y_test, rsvc_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RBF kernel SVC report for selected genes: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.83      0.71      0.77        14\n",
      "         1.0       0.80      0.89      0.84        18\n",
      "\n",
      "   micro avg       0.81      0.81      0.81        32\n",
      "   macro avg       0.82      0.80      0.81        32\n",
      "weighted avg       0.81      0.81      0.81        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nrsvc = SVC(gamma=8.2, C=1)\n",
    "nrsvc.fit(Xn_train, yn_train)\n",
    "nrsvc_p = nrsvc.predict(Xn_test)\n",
    "print(\"RBF kernel SVC report for selected genes: \\n\\n\", classification_report(yn_test, nrsvc_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Dense NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "125/125 [==============================] - 0s 2ms/sample - loss: 0.7399 - acc: 0.5120\n",
      "Epoch 2/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.6983 - acc: 0.4800\n",
      "Epoch 3/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6827 - acc: 0.5520\n",
      "Epoch 4/1000\n",
      "125/125 [==============================] - 0s 168us/sample - loss: 0.6905 - acc: 0.5200\n",
      "Epoch 5/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6928 - acc: 0.4880\n",
      "Epoch 6/1000\n",
      "125/125 [==============================] - 0s 165us/sample - loss: 0.6858 - acc: 0.5360\n",
      "Epoch 7/1000\n",
      "125/125 [==============================] - 0s 135us/sample - loss: 0.6733 - acc: 0.6160\n",
      "Epoch 8/1000\n",
      "125/125 [==============================] - 0s 151us/sample - loss: 0.6823 - acc: 0.5520\n",
      "Epoch 9/1000\n",
      "125/125 [==============================] - 0s 167us/sample - loss: 0.6706 - acc: 0.6320\n",
      "Epoch 10/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6797 - acc: 0.5600\n",
      "Epoch 11/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6759 - acc: 0.5760\n",
      "Epoch 12/1000\n",
      "125/125 [==============================] - 0s 176us/sample - loss: 0.6705 - acc: 0.5680\n",
      "Epoch 13/1000\n",
      "125/125 [==============================] - 0s 135us/sample - loss: 0.6704 - acc: 0.5920\n",
      "Epoch 14/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6683 - acc: 0.6480\n",
      "Epoch 15/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6981 - acc: 0.5200\n",
      "Epoch 16/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6889 - acc: 0.5520\n",
      "Epoch 17/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6753 - acc: 0.5760\n",
      "Epoch 18/1000\n",
      "125/125 [==============================] - 0s 143us/sample - loss: 0.6740 - acc: 0.6080\n",
      "Epoch 19/1000\n",
      "125/125 [==============================] - 0s 151us/sample - loss: 0.6700 - acc: 0.5840\n",
      "Epoch 20/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6818 - acc: 0.5600\n",
      "Epoch 21/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.6696 - acc: 0.6000\n",
      "Epoch 22/1000\n",
      "125/125 [==============================] - 0s 118us/sample - loss: 0.6696 - acc: 0.5360\n",
      "Epoch 23/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6691 - acc: 0.5920\n",
      "Epoch 24/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6571 - acc: 0.6080\n",
      "Epoch 25/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6576 - acc: 0.6880\n",
      "Epoch 26/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6544 - acc: 0.5760\n",
      "Epoch 27/1000\n",
      "125/125 [==============================] - 0s 134us/sample - loss: 0.6510 - acc: 0.6400\n",
      "Epoch 28/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6653 - acc: 0.5760\n",
      "Epoch 29/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6895 - acc: 0.5520\n",
      "Epoch 30/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6835 - acc: 0.5200\n",
      "Epoch 31/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6582 - acc: 0.6800\n",
      "Epoch 32/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6602 - acc: 0.5600\n",
      "Epoch 33/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6579 - acc: 0.6480\n",
      "Epoch 34/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6530 - acc: 0.5920\n",
      "Epoch 35/1000\n",
      "125/125 [==============================] - 0s 151us/sample - loss: 0.6593 - acc: 0.6880\n",
      "Epoch 36/1000\n",
      "125/125 [==============================] - 0s 135us/sample - loss: 0.6479 - acc: 0.6320\n",
      "Epoch 37/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6428 - acc: 0.6400\n",
      "Epoch 38/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6486 - acc: 0.6240\n",
      "Epoch 39/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6475 - acc: 0.6080\n",
      "Epoch 40/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6662 - acc: 0.6160\n",
      "Epoch 41/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6542 - acc: 0.6000\n",
      "Epoch 42/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.6441 - acc: 0.6640\n",
      "Epoch 43/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6484 - acc: 0.5840\n",
      "Epoch 44/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6432 - acc: 0.6960\n",
      "Epoch 45/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6466 - acc: 0.6480\n",
      "Epoch 46/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6614 - acc: 0.5680\n",
      "Epoch 47/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.6574 - acc: 0.5920\n",
      "Epoch 48/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.6580 - acc: 0.5840\n",
      "Epoch 49/1000\n",
      "125/125 [==============================] - 0s 143us/sample - loss: 0.6439 - acc: 0.6480\n",
      "Epoch 50/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6661 - acc: 0.5440\n",
      "Epoch 51/1000\n",
      "125/125 [==============================] - 0s 160us/sample - loss: 0.6543 - acc: 0.6080\n",
      "Epoch 52/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6343 - acc: 0.6400\n",
      "Epoch 53/1000\n",
      "125/125 [==============================] - 0s 160us/sample - loss: 0.6369 - acc: 0.6400\n",
      "Epoch 54/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6358 - acc: 0.6880\n",
      "Epoch 55/1000\n",
      "125/125 [==============================] - 0s 191us/sample - loss: 0.6325 - acc: 0.7200\n",
      "Epoch 56/1000\n",
      "125/125 [==============================] - 0s 148us/sample - loss: 0.6298 - acc: 0.6800\n",
      "Epoch 57/1000\n",
      "125/125 [==============================] - 0s 140us/sample - loss: 0.6385 - acc: 0.6480\n",
      "Epoch 58/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6296 - acc: 0.6720\n",
      "Epoch 59/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.6265 - acc: 0.6560\n",
      "Epoch 60/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.6466 - acc: 0.6320\n",
      "Epoch 61/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6281 - acc: 0.6720\n",
      "Epoch 62/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.6256 - acc: 0.6960\n",
      "Epoch 63/1000\n",
      "125/125 [==============================] - 0s 159us/sample - loss: 0.6367 - acc: 0.6400\n",
      "Epoch 64/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6485 - acc: 0.5840\n",
      "Epoch 65/1000\n",
      "125/125 [==============================] - 0s 139us/sample - loss: 0.6269 - acc: 0.6720\n",
      "Epoch 66/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6239 - acc: 0.6320\n",
      "Epoch 67/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6505 - acc: 0.6000\n",
      "Epoch 68/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6427 - acc: 0.6080\n",
      "Epoch 69/1000\n",
      "125/125 [==============================] - 0s 135us/sample - loss: 0.6328 - acc: 0.6640\n",
      "Epoch 70/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6304 - acc: 0.6560\n",
      "Epoch 71/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6292 - acc: 0.6800\n",
      "Epoch 72/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6568 - acc: 0.6240\n",
      "Epoch 73/1000\n",
      "125/125 [==============================] - 0s 160us/sample - loss: 0.6242 - acc: 0.7200\n",
      "Epoch 74/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6496 - acc: 0.6000\n",
      "Epoch 75/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6241 - acc: 0.6960\n",
      "Epoch 76/1000\n",
      "125/125 [==============================] - 0s 168us/sample - loss: 0.6261 - acc: 0.6160\n",
      "Epoch 77/1000\n",
      "125/125 [==============================] - 0s 160us/sample - loss: 0.6512 - acc: 0.6560\n",
      "Epoch 78/1000\n",
      "125/125 [==============================] - 0s 168us/sample - loss: 0.6204 - acc: 0.6160\n",
      "Epoch 79/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6204 - acc: 0.6880\n",
      "Epoch 80/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6300 - acc: 0.6400\n",
      "Epoch 81/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6395 - acc: 0.6080\n",
      "Epoch 82/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6467 - acc: 0.6480\n",
      "Epoch 83/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6475 - acc: 0.5840\n",
      "Epoch 84/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6220 - acc: 0.6880\n",
      "Epoch 85/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6220 - acc: 0.7120\n",
      "Epoch 86/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6213 - acc: 0.6880\n",
      "Epoch 87/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6221 - acc: 0.6640\n",
      "Epoch 88/1000\n",
      "125/125 [==============================] - 0s 176us/sample - loss: 0.6249 - acc: 0.6320\n",
      "Epoch 89/1000\n",
      "125/125 [==============================] - 0s 180us/sample - loss: 0.6143 - acc: 0.6880\n",
      "Epoch 90/1000\n",
      "125/125 [==============================] - 0s 191us/sample - loss: 0.6142 - acc: 0.7040\n",
      "Epoch 91/1000\n",
      "125/125 [==============================] - 0s 168us/sample - loss: 0.6168 - acc: 0.6880\n",
      "Epoch 92/1000\n",
      "125/125 [==============================] - 0s 176us/sample - loss: 0.6167 - acc: 0.6800\n",
      "Epoch 93/1000\n",
      "125/125 [==============================] - 0s 168us/sample - loss: 0.6164 - acc: 0.6480\n",
      "Epoch 94/1000\n",
      "125/125 [==============================] - 0s 175us/sample - loss: 0.6301 - acc: 0.6560\n",
      "Epoch 95/1000\n",
      "125/125 [==============================] - 0s 160us/sample - loss: 0.6096 - acc: 0.6960\n",
      "Epoch 96/1000\n",
      "125/125 [==============================] - 0s 199us/sample - loss: 0.6149 - acc: 0.6480\n",
      "Epoch 97/1000\n",
      "125/125 [==============================] - 0s 176us/sample - loss: 0.6286 - acc: 0.6960\n",
      "Epoch 98/1000\n",
      "125/125 [==============================] - 0s 184us/sample - loss: 0.6113 - acc: 0.6480\n",
      "Epoch 99/1000\n",
      "125/125 [==============================] - 0s 176us/sample - loss: 0.6096 - acc: 0.6640\n",
      "Epoch 100/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.6181 - acc: 0.7040\n",
      "Epoch 101/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6057 - acc: 0.6720\n",
      "Epoch 102/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.6179 - acc: 0.6880\n",
      "Epoch 103/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6093 - acc: 0.6480\n",
      "Epoch 104/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6147 - acc: 0.6800\n",
      "Epoch 105/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6136 - acc: 0.6400\n",
      "Epoch 106/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6057 - acc: 0.7120\n",
      "Epoch 107/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6069 - acc: 0.6960\n",
      "Epoch 108/1000\n",
      "125/125 [==============================] - 0s 138us/sample - loss: 0.6093 - acc: 0.6800\n",
      "Epoch 109/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.6090 - acc: 0.6720\n",
      "Epoch 110/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6083 - acc: 0.6960\n",
      "Epoch 111/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.6055 - acc: 0.6720\n",
      "Epoch 112/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6052 - acc: 0.7280\n",
      "Epoch 113/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6016 - acc: 0.6640\n",
      "Epoch 114/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6101 - acc: 0.6640\n",
      "Epoch 115/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.6089 - acc: 0.6880\n",
      "Epoch 116/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6183 - acc: 0.6400\n",
      "Epoch 117/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.6107 - acc: 0.6800\n",
      "Epoch 118/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.6050 - acc: 0.6480\n",
      "Epoch 119/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6114 - acc: 0.6800\n",
      "Epoch 120/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6015 - acc: 0.7120\n",
      "Epoch 121/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6247 - acc: 0.6800\n",
      "Epoch 122/1000\n",
      "125/125 [==============================] - 0s 144us/sample - loss: 0.6108 - acc: 0.6320\n",
      "Epoch 123/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.6678 - acc: 0.6000\n",
      "Epoch 124/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.6715 - acc: 0.5600\n",
      "Epoch 125/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.6160 - acc: 0.6560\n",
      "Epoch 126/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.6103 - acc: 0.6480\n",
      "Epoch 127/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6240 - acc: 0.6560\n",
      "Epoch 128/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6115 - acc: 0.6320\n",
      "Epoch 129/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6013 - acc: 0.6960\n",
      "Epoch 130/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6052 - acc: 0.6480\n",
      "Epoch 131/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.6060 - acc: 0.6640\n",
      "Epoch 132/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.6025 - acc: 0.7200\n",
      "Epoch 133/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6013 - acc: 0.6960\n",
      "Epoch 134/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5978 - acc: 0.7200\n",
      "Epoch 135/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.6033 - acc: 0.6880\n",
      "Epoch 136/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.6133 - acc: 0.6480\n",
      "Epoch 137/1000\n",
      "125/125 [==============================] - 0s 135us/sample - loss: 0.6064 - acc: 0.6400\n",
      "Epoch 138/1000\n",
      "125/125 [==============================] - 0s 152us/sample - loss: 0.6012 - acc: 0.7040\n",
      "Epoch 139/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6028 - acc: 0.6800\n",
      "Epoch 140/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.6038 - acc: 0.6880\n",
      "Epoch 141/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.6059 - acc: 0.6560\n",
      "Epoch 142/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5940 - acc: 0.7040\n",
      "Epoch 143/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6043 - acc: 0.6560\n",
      "Epoch 144/1000\n",
      "125/125 [==============================] - 0s 135us/sample - loss: 0.6113 - acc: 0.6640\n",
      "Epoch 145/1000\n",
      "125/125 [==============================] - 0s 135us/sample - loss: 0.6076 - acc: 0.6320\n",
      "Epoch 146/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.5969 - acc: 0.6960\n",
      "Epoch 147/1000\n",
      "125/125 [==============================] - 0s 151us/sample - loss: 0.5935 - acc: 0.7040\n",
      "Epoch 148/1000\n",
      "125/125 [==============================] - 0s 143us/sample - loss: 0.6118 - acc: 0.6080\n",
      "Epoch 149/1000\n",
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      "Epoch 851/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5762 - acc: 0.7120\n",
      "Epoch 852/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.5714 - acc: 0.7120\n",
      "Epoch 853/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5735 - acc: 0.6960\n",
      "Epoch 854/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5778 - acc: 0.7120\n",
      "Epoch 855/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5738 - acc: 0.6960\n",
      "Epoch 856/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5738 - acc: 0.7040\n",
      "Epoch 857/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5712 - acc: 0.6960\n",
      "Epoch 858/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5787 - acc: 0.7040\n",
      "Epoch 859/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5740 - acc: 0.7040\n",
      "Epoch 860/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5767 - acc: 0.6720\n",
      "Epoch 861/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5835 - acc: 0.7120\n",
      "Epoch 862/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5766 - acc: 0.6960\n",
      "Epoch 863/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5995 - acc: 0.6720\n",
      "Epoch 864/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5818 - acc: 0.6800\n",
      "Epoch 865/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5818 - acc: 0.7200\n",
      "Epoch 866/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5727 - acc: 0.7040\n",
      "Epoch 867/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5742 - acc: 0.6960\n",
      "Epoch 868/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.5774 - acc: 0.6960\n",
      "Epoch 869/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5914 - acc: 0.6880\n",
      "Epoch 870/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.5855 - acc: 0.6880\n",
      "Epoch 871/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5688 - acc: 0.7040\n",
      "Epoch 872/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5916 - acc: 0.6880\n",
      "Epoch 873/1000\n",
      "125/125 [==============================] - 0s 167us/sample - loss: 0.5745 - acc: 0.6960\n",
      "Epoch 874/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5725 - acc: 0.7120\n",
      "Epoch 875/1000\n",
      "125/125 [==============================] - 0s 103us/sample - loss: 0.5835 - acc: 0.6800\n",
      "Epoch 876/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.5766 - acc: 0.7120\n",
      "Epoch 877/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5857 - acc: 0.7200\n",
      "Epoch 878/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5757 - acc: 0.7040\n",
      "Epoch 879/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5789 - acc: 0.6960\n",
      "Epoch 880/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5770 - acc: 0.7040\n",
      "Epoch 881/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5763 - acc: 0.7040\n",
      "Epoch 882/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5716 - acc: 0.7200\n",
      "Epoch 883/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5750 - acc: 0.6960\n",
      "Epoch 884/1000\n",
      "125/125 [==============================] - 0s 95us/sample - loss: 0.5797 - acc: 0.6960\n",
      "Epoch 885/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.5763 - acc: 0.6880\n",
      "Epoch 886/1000\n",
      "125/125 [==============================] - 0s 96us/sample - loss: 0.5858 - acc: 0.7040\n",
      "Epoch 887/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5859 - acc: 0.6880\n",
      "Epoch 888/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.6179 - acc: 0.6960\n",
      "Epoch 889/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5938 - acc: 0.6720\n",
      "Epoch 890/1000\n",
      "125/125 [==============================] - 0s 96us/sample - loss: 0.5786 - acc: 0.6800\n",
      "Epoch 891/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5712 - acc: 0.6960\n",
      "Epoch 892/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.5693 - acc: 0.6960\n",
      "Epoch 893/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.5706 - acc: 0.7040\n",
      "Epoch 894/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5971 - acc: 0.6880\n",
      "Epoch 895/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5789 - acc: 0.6880\n",
      "Epoch 896/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5705 - acc: 0.7120\n",
      "Epoch 897/1000\n",
      "125/125 [==============================] - 0s 109us/sample - loss: 0.5682 - acc: 0.7200\n",
      "Epoch 898/1000\n",
      "125/125 [==============================] - 0s 96us/sample - loss: 0.5789 - acc: 0.7040\n",
      "Epoch 899/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5671 - acc: 0.7120\n",
      "Epoch 900/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5884 - acc: 0.6960\n",
      "Epoch 901/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5798 - acc: 0.6960\n",
      "Epoch 902/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5799 - acc: 0.6880\n",
      "Epoch 903/1000\n",
      "125/125 [==============================] - 0s 109us/sample - loss: 0.5834 - acc: 0.7280\n",
      "Epoch 904/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5817 - acc: 0.6880\n",
      "Epoch 905/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5897 - acc: 0.6880\n",
      "Epoch 906/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5983 - acc: 0.6960\n",
      "Epoch 907/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5708 - acc: 0.6880\n",
      "Epoch 908/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5752 - acc: 0.6880\n",
      "Epoch 909/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.5785 - acc: 0.6960\n",
      "Epoch 910/1000\n",
      "125/125 [==============================] - 0s 115us/sample - loss: 0.5722 - acc: 0.6960\n",
      "Epoch 911/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5804 - acc: 0.6720\n",
      "Epoch 912/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5759 - acc: 0.6800\n",
      "Epoch 913/1000\n",
      "125/125 [==============================] - 0s 100us/sample - loss: 0.5921 - acc: 0.6720\n",
      "Epoch 914/1000\n",
      "125/125 [==============================] - 0s 135us/sample - loss: 0.5879 - acc: 0.6960\n",
      "Epoch 915/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5819 - acc: 0.6960\n",
      "Epoch 916/1000\n",
      "125/125 [==============================] - 0s 113us/sample - loss: 0.5673 - acc: 0.7120\n",
      "Epoch 917/1000\n",
      "125/125 [==============================] - 0s 96us/sample - loss: 0.5671 - acc: 0.6960\n",
      "Epoch 918/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5803 - acc: 0.6960\n",
      "Epoch 919/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5902 - acc: 0.6880\n",
      "Epoch 920/1000\n",
      "125/125 [==============================] - 0s 116us/sample - loss: 0.5725 - acc: 0.6960\n",
      "Epoch 921/1000\n",
      "125/125 [==============================] - 0s 96us/sample - loss: 0.5677 - acc: 0.7120\n",
      "Epoch 922/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5757 - acc: 0.7040\n",
      "Epoch 923/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5888 - acc: 0.7120\n",
      "Epoch 924/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5843 - acc: 0.6800\n",
      "Epoch 925/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5856 - acc: 0.6720\n",
      "Epoch 926/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5893 - acc: 0.7040\n",
      "Epoch 927/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5903 - acc: 0.6960\n",
      "Epoch 928/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5801 - acc: 0.6960\n",
      "Epoch 929/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5794 - acc: 0.7040\n",
      "Epoch 930/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5731 - acc: 0.6800\n",
      "Epoch 931/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5859 - acc: 0.7120\n",
      "Epoch 932/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5826 - acc: 0.6800\n",
      "Epoch 933/1000\n",
      "125/125 [==============================] - 0s 114us/sample - loss: 0.5786 - acc: 0.6800\n",
      "Epoch 934/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.6386 - acc: 0.6640\n",
      "Epoch 935/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5787 - acc: 0.6960\n",
      "Epoch 936/1000\n",
      "125/125 [==============================] - 0s 113us/sample - loss: 0.5869 - acc: 0.6960\n",
      "Epoch 937/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5701 - acc: 0.7120\n",
      "Epoch 938/1000\n",
      "125/125 [==============================] - 0s 96us/sample - loss: 0.5746 - acc: 0.6880\n",
      "Epoch 939/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5987 - acc: 0.6800\n",
      "Epoch 940/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5759 - acc: 0.7040\n",
      "Epoch 941/1000\n",
      "125/125 [==============================] - 0s 113us/sample - loss: 0.5683 - acc: 0.7120\n",
      "Epoch 942/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5712 - acc: 0.7040\n",
      "Epoch 943/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5758 - acc: 0.7120\n",
      "Epoch 944/1000\n",
      "125/125 [==============================] - 0s 143us/sample - loss: 0.5701 - acc: 0.7200\n",
      "Epoch 945/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.5763 - acc: 0.6720\n",
      "Epoch 946/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5778 - acc: 0.6720\n",
      "Epoch 947/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.5697 - acc: 0.7120\n",
      "Epoch 948/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5837 - acc: 0.7040\n",
      "Epoch 949/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5866 - acc: 0.7120\n",
      "Epoch 950/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5776 - acc: 0.6800\n",
      "Epoch 951/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5791 - acc: 0.6800\n",
      "Epoch 952/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5731 - acc: 0.7120\n",
      "Epoch 953/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5767 - acc: 0.7040\n",
      "Epoch 954/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5720 - acc: 0.7200\n",
      "Epoch 955/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5739 - acc: 0.7040\n",
      "Epoch 956/1000\n",
      "125/125 [==============================] - 0s 95us/sample - loss: 0.5729 - acc: 0.6800\n",
      "Epoch 957/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5819 - acc: 0.6880\n",
      "Epoch 958/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5770 - acc: 0.6720\n",
      "Epoch 959/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5779 - acc: 0.7120\n",
      "Epoch 960/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5704 - acc: 0.7280\n",
      "Epoch 961/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5768 - acc: 0.7120\n",
      "Epoch 962/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.5772 - acc: 0.6800\n",
      "Epoch 963/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5713 - acc: 0.6880\n",
      "Epoch 964/1000\n",
      "125/125 [==============================] - 0s 103us/sample - loss: 0.5718 - acc: 0.7040\n",
      "Epoch 965/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5729 - acc: 0.7200\n",
      "Epoch 966/1000\n",
      "125/125 [==============================] - 0s 136us/sample - loss: 0.5689 - acc: 0.6960\n",
      "Epoch 967/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5741 - acc: 0.7040\n",
      "Epoch 968/1000\n",
      "125/125 [==============================] - 0s 367us/sample - loss: 0.5753 - acc: 0.7120\n",
      "Epoch 969/1000\n",
      "125/125 [==============================] - 0s 111us/sample - loss: 0.5854 - acc: 0.6640\n",
      "Epoch 970/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5798 - acc: 0.6960\n",
      "Epoch 971/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5702 - acc: 0.7200\n",
      "Epoch 972/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5671 - acc: 0.7120\n",
      "Epoch 973/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5726 - acc: 0.6880\n",
      "Epoch 974/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5768 - acc: 0.7040\n",
      "Epoch 975/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5653 - acc: 0.7120\n",
      "Epoch 976/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5724 - acc: 0.6880\n",
      "Epoch 977/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5744 - acc: 0.6960\n",
      "Epoch 978/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5738 - acc: 0.6960\n",
      "Epoch 979/1000\n",
      "125/125 [==============================] - 0s 119us/sample - loss: 0.5792 - acc: 0.6880\n",
      "Epoch 980/1000\n",
      "125/125 [==============================] - 0s 135us/sample - loss: 0.5720 - acc: 0.6960\n",
      "Epoch 981/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5687 - acc: 0.7120\n",
      "Epoch 982/1000\n",
      "125/125 [==============================] - 0s 95us/sample - loss: 0.5742 - acc: 0.6880\n",
      "Epoch 983/1000\n",
      "125/125 [==============================] - 0s 127us/sample - loss: 0.5874 - acc: 0.6560\n",
      "Epoch 984/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5836 - acc: 0.6800\n",
      "Epoch 985/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5697 - acc: 0.6960\n",
      "Epoch 986/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5673 - acc: 0.6960\n",
      "Epoch 987/1000\n",
      "125/125 [==============================] - 0s 96us/sample - loss: 0.5728 - acc: 0.6960\n",
      "Epoch 988/1000\n",
      "125/125 [==============================] - 0s 114us/sample - loss: 0.5722 - acc: 0.7120\n",
      "Epoch 989/1000\n",
      "125/125 [==============================] - 0s 128us/sample - loss: 0.5783 - acc: 0.6880\n",
      "Epoch 990/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5665 - acc: 0.7120\n",
      "Epoch 991/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5783 - acc: 0.7120\n",
      "Epoch 992/1000\n",
      "125/125 [==============================] - 0s 103us/sample - loss: 0.5742 - acc: 0.7120\n",
      "Epoch 993/1000\n",
      "125/125 [==============================] - 0s 112us/sample - loss: 0.5712 - acc: 0.6960\n",
      "Epoch 994/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5701 - acc: 0.7120\n",
      "Epoch 995/1000\n",
      "125/125 [==============================] - 0s 116us/sample - loss: 0.5727 - acc: 0.6960\n",
      "Epoch 996/1000\n",
      "125/125 [==============================] - 0s 117us/sample - loss: 0.5675 - acc: 0.7120\n",
      "Epoch 997/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.5804 - acc: 0.7040\n",
      "Epoch 998/1000\n",
      "125/125 [==============================] - 0s 104us/sample - loss: 0.6002 - acc: 0.6640\n",
      "Epoch 999/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5827 - acc: 0.7040\n",
      "Epoch 1000/1000\n",
      "125/125 [==============================] - 0s 120us/sample - loss: 0.5760 - acc: 0.6640\n",
      "Simple DNN report for selected genes:\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.78      0.50      0.61        14\n",
      "         1.0       0.70      0.89      0.78        18\n",
      "\n",
      "   micro avg       0.72      0.72      0.72        32\n",
      "   macro avg       0.74      0.69      0.69        32\n",
      "weighted avg       0.73      0.72      0.71        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nn = Sequential()\n",
    "\n",
    "nn.add(Dense(16, activation = 'relu', input_dim=10))\n",
    "nn.add(Dense(8, activation = 'relu'))\n",
    "nn.add(Dense(1, activation = 'sigmoid'))\n",
    "nn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "                   \n",
    "nn.fit(Xn_train, yn_train, batch_size = 10, epochs = 1000)\n",
    "\n",
    "yn_pred = nn.predict(Xn_test)\n",
    "yn_pred = (yn_pred > 0.5)\n",
    "print (\"Simple DNN report for selected genes:\\n\\n\",classification_report(yn_test, yn_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simple DNN report for selected genes:\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.69      0.79      0.73        14\n",
      "         1.0       0.81      0.72      0.76        18\n",
      "\n",
      "   micro avg       0.75      0.75      0.75        32\n",
      "   macro avg       0.75      0.75      0.75        32\n",
      "weighted avg       0.76      0.75      0.75        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nn = Sequential()\n",
    "\n",
    "nn.add(Dense(16, activation = 'relu', input_dim=10))\n",
    "nn.add(Dense(8, activation = 'relu'))\n",
    "nn.add(Dense(4, activation = 'relu'))\n",
    "nn.add(Dense(1, activation = 'sigmoid'))\n",
    "nn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "                   \n",
    "nn.fit(Xn_train, yn_train, batch_size = 10, epochs = 1000, verbose=0)\n",
    "\n",
    "yn_pred = nn.predict(Xn_test)\n",
    "yn_pred = (yn_pred > 0.5)\n",
    "print (\"Simple DNN report for selected genes:\\n\\n\",classification_report(yn_test, yn_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 735,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simple DNN report for selected genes:\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.82      0.64      0.72        14\n",
      "         1.0       0.76      0.89      0.82        18\n",
      "\n",
      "   micro avg       0.78      0.78      0.78        32\n",
      "   macro avg       0.79      0.77      0.77        32\n",
      "weighted avg       0.79      0.78      0.78        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nn22 = Sequential()\n",
    "\n",
    "nn22.add(Dense(16, activation = 'relu', input_dim=10))\n",
    "nn22.add(layers.Dropout(0.2))\n",
    "nn22.add(Dense(8, activation = 'tanh'))\n",
    "nn22.add(Dense(1, activation = 'sigmoid'))\n",
    "nn22.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "                   \n",
    "nn22.fit(Xn_train, yn_train, batch_size = 10, epochs = 1000, verbose=0)\n",
    "\n",
    "yn_pred = nn22.predict(Xn_test)\n",
    "yn_pred = (yn_pred > 0.5)\n",
    "print (\"Simple DNN report for selected genes:\\n\\n\",classification_report(yn_test, yn_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression report for SFM L1: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.33      0.43      0.38        14\n",
      "         1.0       0.43      0.33      0.38        18\n",
      "\n",
      "   micro avg       0.38      0.38      0.38        32\n",
      "   macro avg       0.38      0.38      0.38        32\n",
      "weighted avg       0.39      0.38      0.38        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py:283: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  indices = (scores > 0).astype(np.int)\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(random_state=0).fit(X_train, y_train)\n",
    "lr_p = lr.predict(X_test)\n",
    "print(\"Logistic Regression report: \\n\\n\", classification_report(y_test, lr_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression report for selected genes: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.73      0.57      0.64        14\n",
      "         1.0       0.71      0.83      0.77        18\n",
      "\n",
      "   micro avg       0.72      0.72      0.72        32\n",
      "   macro avg       0.72      0.70      0.70        32\n",
      "weighted avg       0.72      0.72      0.71        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py:283: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  indices = (scores > 0).astype(np.int)\n"
     ]
    }
   ],
   "source": [
    "nlr = LogisticRegression(random_state=0).fit(Xn_train, yn_train)\n",
    "nlr_p = nlr.predict(Xn_test)\n",
    "print(\"Logistic Regression report for selected genes: \\n\\n\", classification_report(yn_test, nlr_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.44      0.57      0.50        14\n",
      "         1.0       0.57      0.44      0.50        18\n",
      "\n",
      "   micro avg       0.50      0.50      0.50        32\n",
      "   macro avg       0.51      0.51      0.50        32\n",
      "weighted avg       0.52      0.50      0.50        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:487: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_store_unique_indices = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\base.py:158: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dtype=np.int)\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier().fit(X_train, y_train)\n",
    "rfc_p = rfc.predict(X_test)\n",
    "print(\"Random Forest Classifier report: \\n\\n\", classification_report(y_test, rfc_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier report for selected genes: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.61      0.79      0.69        14\n",
      "         1.0       0.79      0.61      0.69        18\n",
      "\n",
      "   micro avg       0.69      0.69      0.69        32\n",
      "   macro avg       0.70      0.70      0.69        32\n",
      "weighted avg       0.71      0.69      0.69        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:487: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_store_unique_indices = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\base.py:158: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dtype=np.int)\n"
     ]
    }
   ],
   "source": [
    "nrfc = RandomForestClassifier().fit(Xn_train, yn_train)\n",
    "nrfc_p = nrfc.predict(Xn_test)\n",
    "print(\"Random Forest Classifier report for selected genes: \\n\\n\", classification_report(yn_test, nrfc_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.32      0.43      0.36        14\n",
      "         1.0       0.38      0.28      0.32        18\n",
      "\n",
      "   micro avg       0.34      0.34      0.34        32\n",
      "   macro avg       0.35      0.35      0.34        32\n",
      "weighted avg       0.35      0.34      0.34        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n"
     ]
    }
   ],
   "source": [
    "ada = AdaBoostClassifier().fit(X_train, y_train)\n",
    "ada_p = ada.predict(X_test)\n",
    "print(\"AdaBoost Classifier report: \\n\\n\", classification_report(y_test, ada_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost Classifier report for selected genes: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.71      0.71      0.71        14\n",
      "         1.0       0.78      0.78      0.78        18\n",
      "\n",
      "   micro avg       0.75      0.75      0.75        32\n",
      "   macro avg       0.75      0.75      0.75        32\n",
      "weighted avg       0.75      0.75      0.75        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py:149: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  y_encoded = np.zeros(y.shape, dtype=np.int)\n"
     ]
    }
   ],
   "source": [
    "nada = AdaBoostClassifier().fit(Xn_train, yn_train)\n",
    "nada_p = nada.predict(Xn_test)\n",
    "print(\"AdaBoost Classifier report for selected genes: \\n\\n\", classification_report(yn_test, nada_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quadratic Discriminant Analysis (QDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QDA report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.33      0.36      0.34        14\n",
      "         1.0       0.47      0.44      0.46        18\n",
      "\n",
      "   micro avg       0.41      0.41      0.41        32\n",
      "   macro avg       0.40      0.40      0.40        32\n",
      "weighted avg       0.41      0.41      0.41        32\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\slm\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:692: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    }
   ],
   "source": [
    "qda = QuadraticDiscriminantAnalysis().fit(X_train, y_train)\n",
    "qda_p = qda.predict(X_test)\n",
    "print(\"QDA report: \\n\\n\", classification_report(y_test, qda_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QDA report for selected genes: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.53      0.71      0.61        14\n",
      "         1.0       0.69      0.50      0.58        18\n",
      "\n",
      "   micro avg       0.59      0.59      0.59        32\n",
      "   macro avg       0.61      0.61      0.59        32\n",
      "weighted avg       0.62      0.59      0.59        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nqda = QuadraticDiscriminantAnalysis().fit(Xn_train, yn_train)\n",
    "nqda_p = nqda.predict(Xn_test)\n",
    "print(\"QDA report for selected genes: \\n\\n\", classification_report(yn_test, nqda_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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