1.decision_trees.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy.stats import randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "from sklearn import tree\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_score, recall_score, roc_auc_score, roc_curve\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size brca, ovca 1080 579\n",
      "normal size 1659\n",
      "1658 49554\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACAP3</th>\n",
       "      <th>ACTRT2</th>\n",
       "      <th>AGRN</th>\n",
       "      <th>ANKRD65</th>\n",
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       "      <th>AURKAIP1</th>\n",
       "      <th>B3GALT6</th>\n",
       "      <th>C1orf159</th>\n",
       "      <th>...</th>\n",
       "      <th>SNORA36A</th>\n",
       "      <th>SNORA56</th>\n",
       "      <th>TMLHE</th>\n",
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       "      <th>WASH6P|ENSG00000182484.10</th>\n",
       "      <th>WASIR1|ENSG00000185203.7</th>\n",
       "      <th>BRCA</th>\n",
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       "  </thead>\n",
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       "      <td>-0.063</td>\n",
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       "      <td>-0.063</td>\n",
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       "      <td>-0.063</td>\n",
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       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>TCGA-3C-AALK-01</th>\n",
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       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "<p>5 rows × 24777 columns</p>\n",
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      "text/plain": [
       "                 ACAP3  ACTRT2   AGRN  ANKRD65  ATAD3A  ATAD3B  ATAD3C  \\\n",
       "TCGA-3C-AAAU-01  0.069   0.069  0.069    0.069   0.069   0.069   0.069   \n",
       "TCGA-3C-AALI-01 -1.008  -1.008 -1.008   -1.008  -1.008  -1.008  -1.008   \n",
       "TCGA-3C-AALJ-01 -0.330  -0.330 -0.330   -0.330  -0.330  -0.330  -0.330   \n",
       "TCGA-3C-AALK-01 -0.023  -0.023 -0.023   -0.023  -0.023  -0.023  -0.023   \n",
       "TCGA-4H-AAAK-01  0.000   0.000  0.000    0.000   0.000   0.000   0.000   \n",
       "\n",
       "                 AURKAIP1  B3GALT6  C1orf159  ...  SNORA36A  SNORA56  TMLHE  \\\n",
       "TCGA-3C-AAAU-01     0.069    0.069     0.069  ...    -0.395   -0.395 -0.395   \n",
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       "TCGA-4H-AAAK-01     0.000    0.000     0.000  ...    -0.020   -0.020 -0.020   \n",
       "\n",
       "                  VBP1  IL9R|ENSG00000124334.12  SPRY3|ENSG00000168939.6  \\\n",
       "TCGA-3C-AAAU-01 -0.395                   -0.395                   -0.395   \n",
       "TCGA-3C-AALI-01 -0.504                   -0.504                   -0.504   \n",
       "TCGA-3C-AALJ-01 -0.063                   -0.063                   -0.063   \n",
       "TCGA-3C-AALK-01  0.008                    0.008                    0.008   \n",
       "TCGA-4H-AAAK-01 -0.020                   -0.020                   -0.020   \n",
       "\n",
       "                 VAMP7|ENSG00000124333.10  WASH6P|ENSG00000182484.10  \\\n",
       "TCGA-3C-AAAU-01                    -0.395                     -0.395   \n",
       "TCGA-3C-AALI-01                    -0.504                     -0.504   \n",
       "TCGA-3C-AALJ-01                    -0.063                     -0.063   \n",
       "TCGA-3C-AALK-01                     0.008                      0.008   \n",
       "TCGA-4H-AAAK-01                    -0.020                     -0.020   \n",
       "\n",
       "                 WASIR1|ENSG00000185203.7  BRCA  \n",
       "TCGA-3C-AAAU-01                    -0.395     1  \n",
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       "\n",
       "[5 rows x 24777 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_datasets(std_size=False):\n",
    "    # My python only likes absolute paths \n",
    "    brca = pd.read_csv('C:\\\\Users\\\\sohai\\\\PycharmProjects\\\\comp3212-cancer-exploration\\\\data\\\\Gistic2_CopyNumber_BRCA', sep='\\t')\n",
    "    ovca = pd.read_csv('C:\\\\Users\\sohai\\\\PycharmProjects\\\\comp3212-cancer-exploration\\\\data\\\\Gistic2_CopyNumber_OVCA', sep='\\t')\n",
    "    brca.set_index('Gene Symbol', inplace=True)\n",
    "    ovca.set_index('Gene Symbol', inplace=True)\n",
    "    brca = brca.transpose()\n",
    "    ovca = ovca.transpose()\n",
    "    brca['BRCA'] = 1\n",
    "    ovca['BRCA'] = 0\n",
    "    print('size brca, ovca', len(brca), len(ovca))\n",
    "    if not std_size:\n",
    "        print('normal size', len(brca)+len(ovca))\n",
    "        df = pd.concat([brca, ovca[1:]])\n",
    "    elif std_size:\n",
    "        print('sampled size', len(ovca)+len(ovca))\n",
    "        df = pd.concat([brca[0:len(ovca)], ovca[1:]])\n",
    "    print(len(df), len(brca.transpose()-2) + len(ovca.transpose()-2))\n",
    "    return df\n",
    "df = get_datasets()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"BRCA\"]\n",
    "X = df[df.columns[:-1]]\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Decision tree params: {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 3}\n",
      "Best score is 0.8864864864864865\n"
     ]
    }
   ],
   "source": [
    "# Hyper parameter tuning \n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "\n",
    "\n",
    "param_dist = {\"max_depth\": [3,None],\n",
    "             \"min_samples_leaf\": randint(1,9),\n",
    "             \"criterion\": [\"gini\",\"entropy\"],\n",
    "             }\n",
    "\n",
    "t = tree.DecisionTreeClassifier()\n",
    "# Cross validation is at 5 now\n",
    "tree_cv = RandomizedSearchCV(t,param_dist,cv=5)\n",
    "tree_cv.fit(X_train,y_train)\n",
    "\n",
    "print(\"Tuned Decision tree params: {}\".format(tree_cv.best_params_))\n",
    "print(\"Best score is {}\".format(tree_cv.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8868613138686131\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(167.4, 190.26, 'X[20721] <= -0.316\\nentropy = 0.934\\nsamples = 1110\\nvalue = [388, 722]'),\n",
       " Text(83.7, 135.9, 'X[19345] <= -0.486\\nentropy = 0.847\\nsamples = 420\\nvalue = [305, 115]'),\n",
       " Text(41.85, 81.53999999999999, 'X[4628] <= -0.576\\nentropy = 0.352\\nsamples = 226\\nvalue = [211, 15]'),\n",
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       " Text(125.55000000000001, 81.53999999999999, 'X[5212] <= 0.68\\nentropy = 0.999\\nsamples = 194\\nvalue = [94, 100]'),\n",
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       " Text(251.10000000000002, 135.9, 'X[5216] <= 0.866\\nentropy = 0.53\\nsamples = 690\\nvalue = [83, 607]'),\n",
       " Text(209.25, 81.53999999999999, 'X[22083] <= -0.424\\nentropy = 0.316\\nsamples = 613\\nvalue = [35, 578]'),\n",
       " Text(188.32500000000002, 27.180000000000007, 'entropy = 0.959\\nsamples = 21\\nvalue = [13, 8]'),\n",
       " Text(230.175, 27.180000000000007, 'entropy = 0.229\\nsamples = 592\\nvalue = [22, 570]'),\n",
       " Text(292.95, 81.53999999999999, 'X[23661] <= -0.665\\nentropy = 0.956\\nsamples = 77\\nvalue = [48, 29]'),\n",
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       " Text(313.875, 27.180000000000007, 'entropy = 0.981\\nsamples = 50\\nvalue = [21, 29]')]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making the decision tree no PCA\n",
    "from sklearn import tree\n",
    "clf = tree.DecisionTreeClassifier(criterion = 'entropy', max_depth = 3, min_samples_leaf = 2)\n",
    "X_train = pd.DataFrame(X_train, columns=df.columns[ :len(df.columns)-1])\n",
    "\n",
    "clf = clf.fit(X_train,y_train)\n",
    "print(clf.score(X_test,y_test))\n",
    "tree.plot_tree(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20721</th>\n",
       "      <td>PIAS4</td>\n",
       "      <td>0.508385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5216</th>\n",
       "      <td>TERC</td>\n",
       "      <td>0.159307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19345</th>\n",
       "      <td>RHBDL3</td>\n",
       "      <td>0.132758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5212</th>\n",
       "      <td>EGFEM1P</td>\n",
       "      <td>0.072801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22083</th>\n",
       "      <td>U2AF2</td>\n",
       "      <td>0.061061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23661</th>\n",
       "      <td>CRELD2</td>\n",
       "      <td>0.039577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4628</th>\n",
       "      <td>PXK</td>\n",
       "      <td>0.026111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACAP3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16523</th>\n",
       "      <td>LRRC9</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16522</th>\n",
       "      <td>MIR5586</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       feature  importance\n",
       "20721    PIAS4    0.508385\n",
       "5216      TERC    0.159307\n",
       "19345   RHBDL3    0.132758\n",
       "5212   EGFEM1P    0.072801\n",
       "22083    U2AF2    0.061061\n",
       "23661   CRELD2    0.039577\n",
       "4628       PXK    0.026111\n",
       "0        ACAP3    0.000000\n",
       "16523    LRRC9    0.000000\n",
       "16522  MIR5586    0.000000"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important features \n",
    "features1 = list(X_train.columns)\n",
    "fi_model = pd.DataFrame({'feature': features1,\n",
    "                   'importance': clf.feature_importances_}).\\\n",
    "                    sort_values('importance', ascending = False)\n",
    "fi_model.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at 0.9999 % of the variance ======\n",
      "original shape:    (1658, 24776)\n",
      "transformed shape: (1658, 997)\n",
      "0.9518072289156626\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(167.4, 190.26, 'X[0] <= 7.162\\nentropy = 0.938\\nsamples = 1243\\nvalue = [441, 802]'),\n",
       " Text(83.7, 135.9, 'X[0] <= -5.834\\nentropy = 0.3\\nsamples = 808\\nvalue = [43, 765]'),\n",
       " Text(41.85, 81.53999999999999, 'X[21] <= -6.895\\nentropy = 0.106\\nsamples = 576\\nvalue = [8, 568]'),\n",
       " Text(20.925, 27.180000000000007, 'entropy = 0.485\\nsamples = 57\\nvalue = [6, 51]'),\n",
       " Text(62.775000000000006, 27.180000000000007, 'entropy = 0.036\\nsamples = 519\\nvalue = [2, 517]'),\n",
       " Text(125.55000000000001, 81.53999999999999, 'X[7] <= 5.209\\nentropy = 0.612\\nsamples = 232\\nvalue = [35, 197]'),\n",
       " Text(104.625, 27.180000000000007, 'entropy = 0.351\\nsamples = 197\\nvalue = [13, 184]'),\n",
       " Text(146.475, 27.180000000000007, 'entropy = 0.952\\nsamples = 35\\nvalue = [22, 13]'),\n",
       " Text(251.10000000000002, 135.9, 'X[7] <= -10.106\\nentropy = 0.42\\nsamples = 435\\nvalue = [398, 37]'),\n",
       " Text(209.25, 81.53999999999999, 'X[4] <= -0.915\\nentropy = 0.954\\nsamples = 72\\nvalue = [45, 27]'),\n",
       " Text(188.32500000000002, 27.180000000000007, 'entropy = 0.787\\nsamples = 34\\nvalue = [8, 26]'),\n",
       " Text(230.175, 27.180000000000007, 'entropy = 0.176\\nsamples = 38\\nvalue = [37, 1]'),\n",
       " Text(292.95, 81.53999999999999, 'X[350] <= -0.049\\nentropy = 0.182\\nsamples = 363\\nvalue = [353, 10]'),\n",
       " Text(272.02500000000003, 27.180000000000007, 'entropy = 0.0\\nsamples = 193\\nvalue = [193, 0]'),\n",
       " Text(313.875, 27.180000000000007, 'entropy = 0.323\\nsamples = 170\\nvalue = [160, 10]')]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# PCA and train now\n",
    "x = 0.9999\n",
    "pca = PCA(n_components=0.99)\n",
    "pca.fit(X_train)\n",
    "X_pca = pca.transform(X)\n",
    "pca = PCA(n_components=x)\n",
    "pca.fit(X_train)\n",
    "X_pca = pca.transform(X)\n",
    "print('at', x, '% of the variance ======')\n",
    "print(\"original shape:   \", X.shape)\n",
    "print(\"transformed shape:\", X_pca.shape)\n",
    "y_red = pd.Series(y)\n",
    "\n",
    "df_redd = pd.DataFrame(X_pca)\n",
    "clf = tree.DecisionTreeClassifier(criterion = 'entropy', max_depth = 3, min_samples_leaf = 2)\n",
    "X_traind, X_testd, y_traind, y_testd = train_test_split(df_redd, y)\n",
    "\n",
    "clf.fit(X_traind,y_traind)\n",
    "print(clf.score(X_testd,y_testd))\n",
    "tree.plot_tree(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "# Making the new split given the PCA\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_redd, y)\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Oranges):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    Source: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.figure(figsize = (10, 10))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, size = 24)\n",
    "    plt.colorbar(aspect=4)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45, size = 14)\n",
    "    plt.yticks(tick_marks, classes, size = 14)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    \n",
    "    # Labeling the plot\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), fontsize = 20,\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        \n",
    "    plt.grid(None)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label', size = 18)\n",
    "    plt.xlabel('Predicted label', size = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(predictions, probs, train_predictions, train_probs):\n",
    "    \"\"\"Compare machine learning model to baseline performance.\n",
    "    Computes statistics and shows ROC curve.\"\"\"\n",
    "    \n",
    "    baseline = {}\n",
    "    \n",
    "    baseline['recall'] = recall_score(y_test, [1 for _ in range(len(y_test))])\n",
    "    baseline['precision'] = precision_score(y_test, [1 for _ in range(len(y_test))])\n",
    "    baseline['roc'] = 0.5\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    results['recall'] = recall_score(y_test, predictions)\n",
    "    results['precision'] = precision_score(y_test, predictions)\n",
    "    results['roc'] = roc_auc_score(y_test, probs)\n",
    "    \n",
    "    train_results = {}\n",
    "    train_results['recall'] = recall_score(y_train, train_predictions)\n",
    "    train_results['precision'] = precision_score(y_train, train_predictions)\n",
    "    train_results['roc'] = roc_auc_score(y_train, train_probs)\n",
    "    \n",
    "    for metric in ['recall', 'precision', 'roc']:\n",
    "        print(f'{metric.capitalize()} Baseline: {round(baseline[metric], 2)} Test: {round(results[metric], 2)} Train: {round(train_results[metric], 2)}')\n",
    "    \n",
    "    # Calculate false positive rates and true positive rates\n",
    "    base_fpr, base_tpr, _ = roc_curve(y_test, [1 for _ in range(len(y_test))])\n",
    "    model_fpr, model_tpr, _ = roc_curve(y_test, probs)\n",
    "\n",
    "    plt.figure(figsize = (8, 6))\n",
    "    plt.rcParams['font.size'] = 16\n",
    "    \n",
    "    # Plot both curves\n",
    "    plt.plot(base_fpr, base_tpr, 'b', label = 'baseline')\n",
    "    plt.plot(model_fpr, model_tpr, 'r', label = 'model')\n",
    "    plt.legend();\n",
    "    plt.xlabel('False Positive Rate'); plt.ylabel('True Positive Rate'); plt.title('ROC Curves');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_rf_predictions = clf.predict(X_train)\n",
    "train_rf_probs = clf.predict_proba(X_train)[:, 1]\n",
    "\n",
    "rf_predictions = clf.predict(X_test)\n",
    "rf_probs = clf.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Baseline: 1.0 Test: 0.98 Train: 0.98\n",
      "Precision Baseline: 0.6 Test: 0.94 Train: 0.96\n",
      "Roc Baseline: 0.5 Test: 0.97 Train: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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FC+HYY4OOKjgaAkgUWVlw881Qqxb07x90NCIiCcM5381/661QoQJMmQJt2wYdVfCUACSK4cPhiy/8BMCDDgo6GhGRhJCRATfdBK+/Di1awKuvQp06QUcVHzQEkAj++1/o2xfatIErrgg6GhGRhPD559CkCbz5JgwaBDNnqvHPTQlAIvjHP/xU1eefV91/EZEiZGfD449D8+aQmemX+d13H5QuHXRk8UVDAPFu1izfd/XAA3D00UFHIyIS19atg65dYdo0+PvfYfRoqFYt6Kjik3oA4tmuXb4o9V/+An36BB2NiEhcmzEDGjXyd/zDh8Pbb6vxL4x6AOLZE0/4dSvvvw8VKwYdjYhIXMrMhPvvh0cegQYN/Fh/w4ZBRxX/lADEq1Wr/KyVyy+H//u/oKMREYlLq1bB1VfDp59Cjx7w1FNQqVLQUSUGDQHEm82bYeBAv9NfqVL+b7OIiOznzTf9Dn7ff+9XSI8Yoca/OJQAxIuNG/1Ev7Q06NcP/vY3+PhjOOKIoCMTEYkr27bB9ddD+/ZwwgmwbBlceWXQUSUeDQEE7fff4ckn/RK/LVv8tNW+feHkk4OOTEQk7nz1FbRr56dH3XuvL4xatmzQUSUmJQBBWbfOT/IbNgy2b/fpa9++cOKJQUcmIhJ3nPP/XN55p5/ZP2MGnHNO0FElNiUAsZaV5Zf0DR/ul/m1b+8rVDRoEHRkIiJxaeNGuO46ePdduOACePllqFkz6KgSn+YAxNq0ab7L/6KL4Ntv4bXX1PiLiBRgwQK/tv+99/w/nVOmqPGPFCUAsbZhg38eMgTq1w82FhGROLVnDwwY4DfwqVDBb93bu7dfHCWRoSGAWEtP989/+lOwcYiIxKlff4VOnXxFv86dYehQbYIaDUoAYi0jwz8rARAR2c/kydCtm58iNXYsdOkSdETJS50psZae7itVaN2KiMheO3fC7bfDJZdAvXqwdKka/2hTAhBr6em6+xcRyWXFCjj9dHjuOejVy4/3H3ts0FElPw0BxFpGBhx8cNBRiIgEzjm/pO/WW33H6NSpcOGFQUeVOtQDEGvqARARISMDOnaEa6+F006DL79U4x9rSgBiTT0AIpLiPvvMVzufMMFvejpjBtSuHXRUqUcJQKylpysBEJGUlJ0Njz0GZ57pi6LOn+8LoZYuHXRkqUlzAGJNQwAikoLWrYOuXX0x1Msvh1GjfE1/CY56AGJNQwAikmKmT/flfOfNgxdegLfeUuMfD2KeAJjZEWb2tpmlm1mGmf3LzOqGeW1dMxtrZj+b2XYz+8HMBplZ5WjHHRF79vgtf5UAiEgKyMz0e5+1aQM1asDnn8MNN4BZ0JEJxHgIwMwqAbOBXUBXwAGDgDlmdpJzblsh11YGZgJlgfuBn4FmwADgGKBddKOPgC1b/LOGAEQkyf30E1x9tZ/wd8MNfiOfSpWCjkpyi/UcgO7AUUB959yPAGb2FfBv4AbgyUKuPRPf0Ldxzk0PHZtjZtWBf5hZJefc9uiFHgE5ZYDVAyAiSWz8+P/d6b/1FlxxRdARSX5iPQRwMbAop/EHcM6tAj4GLini2nKh54w8xzfjf4/471TK2QhICYCIJKFt2+C66/ydf8OGfm2/Gv/4FesE4ATgm3yOLwcaFHHtTHxPwSNm1sDMqphZK6An8EJhwwdxQzsBikiS+vJLaNoUxozxS/vmzfM1/SV+xToBqA5syuf4RqDQOaHOuZ3AX/ExLwe2ALOAqcCtkQ0zSjQEICJJxjl4/nlfzS89HWbO9MV9ymiRedwL4j+Ry+dYkd33ZlYBeBM4FOiMnwR4KtAPyAJuKuC6HkAPgLp1w1psED0aAhCRJLJxoy/lO2kSXHCBr+tfs2bQUUm4Yp0AbML3AuRVjfx7BnK7DmgBHO2cWxk6Nt/M0oGRZvaCc+7LvBc550YCIwGaNm2aX/IROxoCEJEkMX++r+W/bp2f4d+rl5b3JZpYDwEsx88DyKsB8G0R154IbMrV+Of4LPR8fAljiz4NAYhIgtuzBwYMgJYtoUIFv3Vv795q/BNRrBOAycDpZnZUzgEzS8Mv8ZtcxLVrgWpmdnSe46eFnn+LUIzRk57ui15rMayIJKBffoFWraB/f3/3v3QpnHJK0FHJgYp1AjAKWA1MMrNLzOxiYBLwCzAi501mVs/MssysX65rX8ZP/HvfzLqaWUsz+yfwOLAEv5QwvuXsA6BUWUQSzKRJ0LgxLFkCr7ziHwcdFHRUUhIxTQBCS/VaAT8A44DXgFVAK+fc1lxvNaB07vicc6uB04Fl+OqB7+MLC40EznPOZcfgVygZ7QMgIglm50647Ta49FJIS4MvvoDOnYOOSiIh5qsAnHM/A5cX8Z7V5LMywDn3LXBVdCKLAW0FLCIJ5PvvoX17v8a/d294+GEoXz7oqCRStFIzlrQVsIgkAOd8QZ/bbvNTlt57zy/zk+Si7YBjSUMAIhLn0tOhQwdf0vf00/3dvxr/5KQEIJY0BCAiceyzz+Dkk/0GPg89BNOnQ+3aQUcl0aIEIJY0BCAicSg7Gx59FM480/88fz7ce69ftSzJS3MAYsU5DQGISNxZuxa6dvV3+1dcAaNGQdWqQUclsaAEIFZ27oTMTCUAIhI3pk/3S/oyMmDECOjeXWVKUomGAGJF+wCISJzYvRvuugvatPGb9yxeDD16qPFPNeoBiBXtAyAiceCnn+Dqq/2Evxtv9Bv5VKwYdFQSBCUAsaKtgEUkYG+8ATfc4Cf3vf02XF5oSTZJdhoCiBUNAYhIQLZt8+v6O3SAE0+EZcvU+IsSgNjREICIBGDZMr9j35gxcN99MG8e1KsXdFQSD5QAxIqGAEQkhpyD55+H007z9x8zZ8KgQVBGA78SUuwEwMyqhLbrLRuNgJKWhgBEJEb++MPv3nfbbXDeeb6cb6tWQUcl8SbsBMDM2prZUiAdWAmcGDo+2sw6RCm+5JEzBKAEQESiaN48aNQIPvgAnnoKpkzxS/1E8gorATCzS4FJwO9AnzzXrQK6Rj60JJOeDpUrq/9NRKIiKwv69/d3+pUqwaJF0KuX1vZLwcLtAXgAGOOcaw08nefcN0DDiEaVjLQPgIhEyS+/+IZ/wADo1AmWLIEmTYKOSuJduAnA8cCboZ9dnnObgEMiFlGy0j4AIhIFkyb5Lv8vvoBx42DsWDjooKCjkkQQbgKQAdQo4FwasCEi0SQzbQUsIhG0cyfcequf7HfUUbB0qb/7FwlXuAnADOAeM8u9R5Qzs/LArcAHEY8s2WgIQEQi5Lvv/PK+oUPhjjvgk0/gmGOCjkoSTbgz0u4DPgNWAO/jhwHuBk4CDgYujUp0ySQjA444IugoRCSBOQcvvQS33+7nFL/3HlxwQdBRSaIKqwfAObcaaAJMBc4D9gBnAYuA05xz/4lWgElDQwAiUgLp6X4Tn+uvhzPO8Gv71fhLSYS9Js059ytwXRRjSW4aAhCRA/Tpp77x//lnGDzYb+VbunTQUUmiC7cOwGwzO66Ac8ea2ezIhpVk9uzxu3GoB0BEiiE7Gx55BP76V//zggVwzz1q/CUywu0BaAEUdPt6EHB2RKJJVtoISESKae1a6NIFZsyAK6+EkSOhatWirxMJV3HK0uVd/5/jL8DWCMSSvFQGWESKYdo03/hnZMCIEdC9uyr6SeQVmACYWTegW+ilA0aa2ZY8b6uIrwI4KzrhJQntBCgiYdi9G/r2hcceg4YNYfZsOOGEoKOSZFVYD0A2frY/gOV5neMPYDjwSORDSyJKAESkCCtX+ol+n38ON94ITz4JFSsGHZUkswITAOfcWGAsgJnNAW5yzn0fq8CSioYARKQQb7wBN9zgJ/e9/TZcfnnQEUkqCLcOQEs1/iWgHgARycfWrXDttdChA5x0EixbpsZfYqdYe9OaWSOgPlAh7znn3CuRCirpKAEQkTyWLYP27eGHH/y4/wMPaLdwia2w/rqF9gB4Dzg951DoOffKACUABdEQgIiEOAfPPw//+AfUqAGzZkHLlkFHJako3M2ABuO3/D0L3/hfBrQCXgN+Ak6NSnTJIj3dp/aa0SOS0v74w+/ed/vt0Lq1L+erxl+CEm4C0AafBCwKvf7VOTfXOdcFmAn0jEZwSSNnHwAt5BVJWfPmQaNG8OGH8PTTMHmy7wEQCUq4CUAt4Cfn3B5gJ776X45/ARdGOrCkkpGh7n+RFJWV5cf3W7WCSpVg4ULo2VP3AxK8cBOAtUBOEco1wBm5zh0d0YiSkXYCFElJv/ziG/4HH4TOnWHpUmjSJOioRLxw55x+hG/0pwLjgAfMLA3IAroCk6MRXNLQToAiKefdd/0Sv8xMePVV6Ngx6IhE9hVuAjAAqB36+TH8hMB2QCV8439b5ENLIhkZcMQRQUchIjGwY4ef4T9sGJxyCowfD0ern1TiUFgJgHNuJbAy9HMmcGfoIeFIT/eFvUUkqX33HbRrB19/DXfeCYMHQ7lyQUclkr9w5wAUyMxONrN3IhFM0tIQgEhScw5Gj/Z3/GvXwvvvw+OPq/GX+FZoAmBmpc3sVDO7wsxOznOuqZlNARYDWslaEOf8EIAmAYokpfR0v4lP9+7QvLlf2/9//xd0VCJFKzABMLM/A58CC4EJwGIze9PMypnZ6NC5VsATwFGxCDYh7djh1wEpARBJOosWQePGfgOfwYNh+nSoVSvoqETCU9gcgCHAccD9wFLgSOBe4GPgFPxOgXc759ZFO8iElrMPgIYARJJGdjY89piv4V+nDixYAGecUfR1IvGksATgHKC/c+7xnANmtgJf+e8555yq/4UjZx8A9QCIJIW1a/2a/pkz4corYeRIqFq16OtE4k1hCUBN/lf6N8fC0PNb0QknCWknQJGk8eGH0KWL38Z35Ei4/npV9JPEVdgkwFLA7jzHcl5vj044SUhDACIJb/du+Oc//eS+ww6DxYv9pD81/pLIiqoDcJGZ5V7AXgq/BfDFZtY49xudcy9FOrikoCEAkYT2449+lv/ixXDTTfDEE9rYU5JDUQnAfQUc75fntQOUAORHQwAiCev11+HGG6F0aZg4Ef7+96AjEomcwoYAjizGI+xlgGZ2hJm9bWbpZpZhZv8ys7rFuP54M3vLzH43sx1mtsLM4ndCooYARBLO1q3QrZuv33/SSX5tvxp/STYF9gA459ZE+svMrBIwG9iF30TIAYOAOWZ2knNuWxHXNw1dPxe4HkgHjgGqRDrWiMkZAlACIJIQli3z5Xz//W+4/37o1w/KhLtrikgCifVf6+743oL6zrkfAczsK+DfwA3AkwVdaGal8LUHZjnnLst1ak70wo2A9HSoUsX3IYpI3HIOnnvOT/arUQNmz4YWLYKOSiR6SrwXQDFdDCzKafwBnHOr8MWFLini2hZAAwpJEuKS9gEQiXu//w6XXAI9e0Lr1r7LX42/JLtYJwAnAN/kc3w5vnEvzF9DzxXMbJGZZZrZejN71szid06u9gEQiWtz50KjRjBtGjzzDEye7HsARJJdrBOA6sCmfI5vBKoVcW3t0PObwHTgPOBR/FyA1wu6yMx6mNliM1u8YcOG4kdcUunpSgBE4lBWlh/fb9XKj9ItWgS33661/ZI6gpja4vI5Fs7/cjnJyqvOuZxliHPNrDQwxMwaOOe+3e/LnBsJjARo2rRpft8dXenpqhMqEmd+/tnP8P/oI7jmGj/2XyV+pxKLREWxegDMrJSZNTSzs82s8gF83yZ8L0Be1ci/ZyC3P0LPM/Icnx56bkw80hCASFx55x2/g9+XX8Krr8KYMWr8JTWFnQCY2S3AWuBL/FK8+qHj75rZ7WF+zHL8PIC8GgD73b3ncy3s34OQ03uQHWYMsaUhAJG4sGMH3HyzX8//l7/AF1/4XgCRVBVWAmBm3YFngHeBduzbZb8AuDzM75sMnG5mewsHmVkacGboXGE+wNcPOD/P8Tah58VhxhBbWgUgErhvv4XTToPhw+HOO+Hjj30SIJLKwu0BuAN4wjnXA3gnz7nvCfUGhGEUsBqYZGaXmNnFwCTgF2BEzpvMrJ6ZZZnZ3pLDzrk/gIeBG81ssJmda2Z348sSj829tDBuZGXB9u3qARAJiHMwejQ0beq38X3/fXj8cShXLujIRIIX7iTAI4FpBUXSAt4AACAASURBVJzbBoQ1y805t83MWgFPAePwPQmzgF7Oua253mpAafZPUB4EtgA3A/8A/gs8BgwM79eIMW0EJBKYzZvhhhtgwgQ491x45RWoVSvoqETiR7gJwO9AWgHn6gO/hfuFzrmfKWLIwDm3mnxWBjjnHL4QUGIUA9I+ACKBWLTI7+D3668wZIiv7lcq1oueReJcuP9LTAH65R67B5yZ1QB64+cGSF7qARCJqexs3+D/NVQ2bMEC6NNHjb9IfsL936IvfgLeN8BM/Ez8Z4HvgD34rnnJS1sBi8TMf//ry/jecw9cfrmf5X/66UFHJRK/wkoAQhPwmuIn4ZUFVuKHD54HznDOpUctwkSmIQCRmPjwQ1/O95NPYNQoGD9e9bdEihJ2JUDn3Bb8ZLv4nHAXjzQEIBJVu3fDvffCE0/AiSf6hr9BUbuKiAgQfh2AJ80sPivtxTMNAYhEzY8/wpln+sb/5pvh00/V+IsUR7hzALoBS8zsGzP7p5nViWZQSSOnB0BDACIR9dprcPLJsHIl/OtfMHQoVIzfPUFF4lK4CcBhwFXAj/ghgDVmNtPMOh/gngCpIT0dypaFChWCjkQkKWzd6jfv6dTJ1/NftgwuuyzoqEQSU7iTAHc75yY65y4FagG3AxWBscA6MxsXxRgTV84+ANpfVKTEvvgCTjnFF/Tp1w/mzIG6dYOOSiRxFXt1rHNuk3NumHPuTKAlfhe/DhGPLBlkZKj7X6SEnINnnvFL+rZtg9mzYcAAKBPEZuYiSaTY/wuFuvyvADoBLYAsYGJkw0oS2glQpER+/x26dYOpU+Gii+Cll6BGjaCjEkkO4a4CKGVm55vZa8A64CWgPL4m/+HOuauiGGPiUgIgcsDmzvVr+6dPh2efhUmT1PiLRFK4PQD/AWriJwE+AowL1euXwmRkQL16QUchklCysnwX/0MPwbHHwnvv+Ql/IhJZ4SYAE4FXnHOfRjOYpKMeAJFi+fln6NABPv7Yz/Z/7jmoUiXoqESSU1gJgHPulmgHkpSUAIiE7V//guuugz17/Dr/DppaLBJVBSYAZnYWsNQ5tzX0c6Gcc/MjGlmic06rAETCsGMH3HknDB8OTZv6cr5/+UvQUYkkv8J6AOYCpwOfhX52BbzPQudKRzKwhLd9u7+VUQ+ASIGWL4f27eGbb+Af//Dj/uXKBR2VSGooLAFoCXwb+rkVBScAkh/tAyBSIOf8rn29esFBB8EHH8D55wcdlUhqKTABcM7Ny/Xz3JhEk0y0D4BIvjZvhh494K234NxzYdw4OPzwoKMSST3h1gH4ycwaFXCuoZn9FNmwkoB6AET2s3ChX9L3zjswZAhMm6bGXyQo4ZYCTsMX/slPBUCL3fNSAiCyV3Y2PPww/O1vfmuMjz6CPn2gVLGLkYtIpBSnFHBBcwCaApsjEEty0RCACAD//S907gyzZkG7djBihPJikXhQ2DLA3kDv0EsHTDGz3XneVhGoDoyPTngJTD0AInzwAXTt6rfxHT0arr1Wm2OKxIvCegB+AmaFfu4KLAY25HnPLvxKgdGRDy3BKQGQFLZ7N9xzDzz5JJx0kl/bf/zxQUclIrkVtgpgEjAJwHzK/qBzblWM4kp8OUMAqmMqKebHH/3a/iVL4JZb4PHHoUKFoKMSkbzCLQXcLdqBJJ30dL/AubTqI0nqePVVuOkmKFvWz/S/9NKgIxKRghQ2B6AfMNo595/Qz4VxzrmBkQ0twWkfAEkhW7f6u/1XXvEz/V97DY44IuioRKQwhfUA9Ac+xG8F3L+Iz3GAEoDctA+ApIilS32X/8qV8MAD0LcvlCnO+iIRCURhcwBK5fezhEk9AJLknINnnvHr+WvWhNmz4eyzg45KRMKlPD1a0tOhevWgoxCJig0boFs3eO89uOgiGDMGDjkk6KhEpDjCLQV8rJmdmut1RTN72MymmNmt0QsvgWVkqAdAktKcOdCoEcyYAc8+C5MmqfEXSUThdu0/D1yR6/VDwJ1AbeApM7sl0oElPA0BSJLJyvLj++ec46e3fPop3HabCvuIJKpwE4CTgI8BzKwU0AXo45w7BRgE9IhOeAksPV2TACVprFnjx/cfegiuucav8W/cOOioRKQkwk0AqgJ/hH4+GagGvB16PRc4KrJhJbjMTNixQz0AkhQmTvSN/ddfw+uvw0svQeXKQUclIiUVbgKwDjg69HNrYKVz7pfQ6ypAVqQDS2g5VQCVAEgC27EDbrwRrrgCjjkGvvgCrr466KhEJFLCXQUwGXjYzBoC1wAjcp07Eb9vgOTI2QdAQwCSoJYv92v7v/kG/vlPGDQIypULOioRiaRwE4C7gQpAG3wyMDjXuYuB6RGOK7GpB0ASlHMwahT06uUrWX/4IbRpE3RUIhIN4e4FsA3oXsC55hGNKBloJ0BJQJs3Q/fu8PbbcN55vqzv4YcHHZWIREuxCgGZWXXgDKA6flLgIufcxmgEltA0BCAJ5pNPoEMH+O03eOQR+Mc/oJTqf4oktbATADMbhF/7Xz7X4V1m9rhz7v6IR5bINAQgCWLPHt/g9+sHdevCRx/BaacFHZWIxEJYCYCZ9QLuBV4EXgXWAocDnYB7zWyDc+7ZqEWZaDQEIAngP/+Bzp19Df927WDECP2VFUkl4fYA3Ag845zrnevYCmCemW0FbgaUAOTQEIDEufffh65dYft2ePFFX9dfFf1EUku4o3xpwHsFnHsvdF5yZGT4NVMVKgQdicg+du2CO+6ACy+E2rVh8WK49lo1/iKpKNwE4A+gYQHnTuB/VQIFtA+AxKV//xuaN4ennoJbb/W1/I8/PuioRCQo4SYA7wADzayzmZUFMLMyZnY18CAwMVoBJiTtAyBxZtw4aNIEVq+Gd9+F555TB5VIqgs3AbgHWAaMBbab2TpgB/Aa8CV+gqDk0FbAEie2bIEuXfzj5JNh2TK45JKgoxKReBBuIaAtZnYWcCHwN3wdgI3APOAD55yLXogJSEMAEgeWLPG1+1euhP794b77oEyxKn+ISDIr9J8DM6uBX+p3NLAJmOic61OSLzSzI4CngPMAA2YCvZxzPxfzc+7BlyT+2Dn315LEFHHp6XCUNkiUYDgHTz8NffrAoYfCnDlw1llBRyUi8abABMDM6gPzgZq5Dt9tZlc45yYdyJeZWSVgNrAL6Ao4YBAwx8xOCpUcDudzjgLuA9YfSBxRpyEACciGDXDNNX6Z38UX+617Dzkk6KhEJB4VNgdgELATaAFUxu/69xnwZAm+rztwFHCpc+7dUCJxMVAPuKEYnzMcP//guxLEEj0aApAAzJ4NjRrBrFl+kt+776rxF5GCFZYAnAb0c87Nd87tcM4txzfSaWZWs5DrCnMxfv+AH3MOOOdWAR8DYU1NMrMOQBP8xMT445zvAdAqAImRrCw/vn/uuf6v3aef+mV+WtsvIoUpLAGog6/2l9sK/Lh97QP8vhOAb/I5vhxoUNTFZlYNP3/grrjdhGjbNsjOVg+AxMSaNXD22TB4sK/mt2SJ7wUQESlKYZMADdiT51h26PlA9wmrjp9MmNdGoFoY1z8G/AC8HO4XmlkPoAdA3bp1w73swGkfAImRiRPh+uv9hj5vvAHt2wcdkYgkkqIWBQ0ws99zvc7pVBxoZrnvwJ1zrmuY35nfksEiOyvN7G9AF6BJcZYdOudGAiMBmjZtGv3lijk7AWoIQKJkxw7o3dtv3nPqqb7x16ITESmuwhKAn4H8CoWuwXfl5xZuw7oJ3wuQVzXy7xnIbQR+N8Jfzaxq6FgZoHTo9Q7n3K4w44ge9QBIFH3zjb/TX74c7roLBg70206IiBRXgQmAcy4tCt+3nP2TB/Dj/98Wce3xoceN+ZzbBPQGni5RdJGgBECiwDkYORJ69fKdS9OmQevWQUclIoks1nXBJgOPm9lRzrmfAMwsDTgTuLuIa1vmc+xpoDRwG/BjPudjT0MAEmGbNkH37n7Mv3VreOUVOOywoKMSkUQX6wRgFHArMMnM+uKHDgYCv+C7+AEws3rASuBB59yDAM65uXk/zMw2A2XyOxcY9QBIBH3yiS/n+5//wKOPwp13QqkDnYIrIpJLTP8pCVX6a4WfyT8OX8xnFdDKObc111sNf2efeP/UKQGQCNizBx56yJfwLVMGPv4Y/vlPNf4iEjkx3xokVPP/8iLes5owVgY451pEJqoIysjwFViqVAk6EklQ//kPdOrka/hffTUMH658UkQiT3uDRVp6Ohx0kG7V5IC8956v5b99u6/jf801qugnItGhVirStA+AHIBdu/za/rZtoXZtX9GvWzc1/iISPcXqATCzk4CzgEOAEc65tWZ2NLDOObclGgEmHO0DIMX0ww++q3/pUrjtNj/Zr0KFoKMSkWQXVgJgZuWBV4G/48fmHTAFWAs8ip/UV9QyvtSgHgAphldegZtvhvLl/e59l4S1JZaISMmFOwTwEHAu0Bk4jH0n6H0AtIlwXIlLCYCEYcsW6NwZunaFU06BL79U4y8isRVuAnA10Nc59zp+457cVgFpkQwqoWkIQIqwZAk0aQKvvw79+8Ps2fDnPwcdlYikmnATgEOA7wr5jPKRCScJqAdACpCdDU8+CWecATt3+mV+DzwApUsHHZmIpKJwE4BVwBkFnDsVWBGZcJKAEgDJx4YNcNFFvpLfBRfAsmW+yI+ISFDCTQBeAe42s45Azt5jzsxa4jfheSkawSWc3bv9rZ2GACSX2bOhUSOYNQuefx7eeQcOOSToqEQk1YWbADwKvIcv35szB+AjYCbwoXPuuSjElnhyNgJSD4AAmZlw331w7rn+r8Snn8Itt2htv4jEh7CWATrn9gDtzWwofsb/ocAf+MZ/XhTjSyzaB0BCVq+GDh1g4UK47jp45hmoXDnoqERE/qdYhYCccwuABVGKJfFpK2AB3nrLb9/rHIwfD+3aBR2RiMj+VAo4ktQDkNK2b4cbboCrroL69eGLL9T4i0j8CrcSYDa++l+BnHNazKQEIGV98w20bw/Ll0OfPjBwIJQtG3RUIiIFC3cI4EH2TwAOAVrjawC8HMGYEpeGAFKOczBihN/I5+CDYfp0OO+8oKMSESlauJMA++d33MxK4/cESI9gTIlLPQApZdMmP9Y/cSK0bu3r+h92WNBRiYiEp0RzAEKrA4YBvSITToLLSQDUA5D0Pv4YGjeGSZP87n0ffKDGX0QSSyQmAZYHqkfgcxJfRobf1q28KiMnqz17YNAgOPtsKFPGJwL//CeU0nRaEUkw4U4CrJvP4XJAQ2AIsDiSQSUslQFOar/95nfwmzMHrr4aXnhBnT0ikrjCnQS4mvxXARiwErglUgElNCUASWvqVLjmGtixA8aM8dv4qqKfiCSycBOAbvkc2wmsAT4PzQUQbQWcdHbt8sv6nnnG1/MfPx6OOy7oqERESq7IBCA0038Z8B/n3Iboh5TA1AOQVH74wa/t/+ILuO02P9mvQoWgoxIRiYxwpi45/Bj/yVGOJfEpAUgar7wCTZrAmjV+pv+zz6rxF5HkUmQC4JzLBn4BtJVJUTQEkPC2bPET/bp2hVNOgS+/hIsvDjoqEZHIC3fx0gigl5mVi2YwCU89AAltyRJ/1//66zBgAMyeDX/+c9BRiYhER7iTAA8C/gL8ZGYfAv9l31UBzjn3QKSDSyjZ2eoBSFDZ2fD003D33b6Yz9y58Le/BR2ViEh0FZgAmNlPwGXOuS+Be3OdujaftzsgtROAbdt8YXj1ACSU9ev98r4PPoBLL4UXX4TqKmslIimgsB6ANHyVP5xzqnNWFO0DkHBmzYJOnXxN/6FD4aabtLZfRFKHGvZI0T4ACSMzE+691+/aV60afPYZ3HyzGn8RSS1FzQHIr/qf5CdnK2D1AMS11at9Gd9Fi+D66/3Yf2WtbxGRFFRUAjDAzH4P43Occ65rJAJKWBoCiHtvveW373XOV/Rr1y7oiEREglNUAtAY2BXG56inIKcHQEMAcWf7dujVC0aNgtNOgzfegCOPDDoqEZFgFZUAXOqc+ywmkSQ69QDEpa+/9uV8v/3W1/QfOBDKlg06KhGR4IVbB0CKogQgrjjnt+u94w7/n2T6dD/pT0REPK0CiJSMDD+NXDPKArdxI1xxhZ/Zf/bZvpyvGn8RkX0pAYiU9HQ//l9Kf6RB+ugjaNwYJk+Gxx+H99/31f1ERGRfBQ4BqPhPMWkfgEDt2QMPPwwPPABpafDJJ9CsWdBRiYjEL80BiBTtAxCY337zFf3mzoUOHWD4cP2nEBEpihKASFEPQCCmTvW1/HfsgDFj/Da+qugnIlI0dfNHihKAmNq1y6/tv+giOOIIWLrUJwJq/EVEwqMEIFI0BBAzP/wAZ5wBzzwDt98OCxdC/fpBRyUiklg0BBAp6gGIOufglVfgllugQgU/0/+ii4KOSkQkMakHIFKUAERVRgZ07uy7+Zs29Wv71fiLiBw4JQCRsGuXf2gIICoWL4YmTXwN/wcfhFmzoE6doKMSEUlsSgAiQVsBR0V2NjzxBDRvDrt3w7x5cP/9ULp00JGJiCQ+zQGIBO0DEHHr1/slfR9+CJddBqNHQ/XqQUclIpI8Yt4DYGZHmNnbZpZuZhlm9i8zqxvGdU3NbKSZfW9m283sZzN7zcyC39hVWwFH1MyZ0KgRzJkDw4bBxIlq/EVEIi2mCYCZVQJmA8cBXYHOwDHAHDMrahed9sAJwLPA/wF3A02AxWZ2RNSCDod6ACIiMxPuuQdat4Zq1eCzz+Cmm7S2X0QkGmI9BNAdOAqo75z7EcDMvgL+DdwAPFnItY845zbkPmBmHwOrQp/bLyoRh0MJQImtWuXL+C5aBN27w1NPaWNFEZFoivUQwMXAopzGH8A5twr4GLiksAvzNv6hY2uADUCwc8I1BFAiEyb4Hfy+/RbefBNGjlTjLyISbbFOAE4Avsnn+HKgQXE/zMyOBw4FvithXCWjHoADsn27v9tv1w4aNIBly+Cqq4KOSkQkNcQ6AagObMrn+EagWnE+yMzKAC/gewBeLHloJZCTAKgHIGxff+0L+rz4oh/3nz8fjgx+OqeISMoIog6Ay+fYgUzzeh5oDnRyzuWXVPgPNuthZovNbPGGDfuNIkRGRoavTVuuXHQ+P4k457frbdYMNm2C6dNh8GAoWzboyEREUkusE4BN+F6AvKqRf89AvszsYaAHcK1zbnph73XOjXTONXXONa1Zs2axgg2bygCHZeNGuPxyuPlmaNnSl/M999ygoxIRSU2xTgCW4+cB5NUA+DacDzCz+/BLAHs658ZFMLYDpwSgSB995Cf6TZ0Kjz8O770Hhx4adFQiIqkr1gnAZOB0Mzsq54CZpQFnhs4VysxuBwYB9znnnotSjMWnrYALtGcPDBwIZ5/tR0g++QTuvBNKqQi1iEigYv3P8ChgNTDJzC4xs4uBScAvwIicN5lZPTPLMrN+uY61B54GPgRmm9npuR7FXkEQUeoByNdvv/ku/n794OqrYelSP/FPRESCF9NCQM65bWbWCngKGIef/DcL6OWc25rrrQaUZt8E5fzQ8fNDj9zmAS2iFHbR0tPh8MMD+/p4NGUKdOsGO3fCyy9Dly6q6CciEk9ivhmQc+5n4PIi3rOaPCsDnHPXANdEK64S0RDAXjt3Qp8+8OyzcPLJfgvf+vWDjkpERPLSboCRoCEAAFasgPbtfUGfnj3hkUegfPmgoxIRkfwoASip7GzYsiWlEwDnYOxYuPVWXw5hyhRo2zboqEREpDCai11SW7f6FjBFhwAyMqBTJz/e36yZX9uvxl9EJP4pASipFN4H4PPPoUkTGD/eL/WbORPqBLstk4iIhEkJQEmlYAKQne2L+TRvDrt3w7x50LcvlC4ddGQiIhIuzQEoqRTbCnjdOujaFaZNg8sug9GjoXp+xZ1FRCSuqQegpFKoB2DGDGjUCObO9Rv6TJyoxl9EJFEpASipFEgAMjPh7ruhTRs45BA/9n/jjSrsIyKSyDQEUFJJPgSwapUv4/vpp9CjBzz1FFSqFHRUIlKQjIwM1q9fT2ZmZtChSBSVLVuWQw89lD+VoO1RAlBSSdwD8OabvtE3gwkT4Morg45IRAqTkZHBunXrqFOnDhUrVsTUTZeUnHPs2LGD3377DeCAkwANAZRUerrf2q5y5aAjiZht26B7d1/Vr0EDX9lPjb9I/Fu/fj116tShUqVKavyTmJlRqVIl6tSpw/r16w/4c5QAlFTOPgBJ8j/bV1/5HftefBHuuQfmz4e0tKCjEpFwZGZmUrFixaDDkBipWLFiiYZ6lACUVJLsA+AcDB0Kp54Kmzf7Gf+DB0PZskFHJiLFoTv/1FHS/9ZKAEoqCRKAjRvh73/3tfxbtfLlfM85J+ioREQkmpQAlFSCbwW8YAE0bgzvvQdPPAFTp8KhhwYdlYiIRJsSgJJK0B6APXvgwQehRQsoVw4++QTuuMPPZxQRiRf9+/fHzMjKygo6lL3mzp2LmTF37ty9x1q0aEGLFi0Ci+lAaBlgSWVkwHHHBR1Fsfz6q9/Bb9486NgRhg1L6E4MEZHADRs2LOgQik0JQEmlpydU6zl5st+6d9cuGDsWunQJOiIRkcTXoEGDoEMoNnX4llSCDAHs3Am33w6XXAL16sHSpWr8RSRxfPfdd7Rs2ZJKlSpRq1Yt+vXrR3Z2NgA7d+6kd+/eNGzYkCpVqnD44Ydz0UUX8f333+/zGWvXrqVr167Url2b8uXLU6tWLdq2bbvPWvrt27fTp08fjjzySMqVK8eRRx7JQw89tPe7CpJ3CCBnmGDy5Mnceuut1KhRg5o1a9KpUyc2b968z7VZWVk8/PDDHHfccZQvX57atWtz5513snPnzhL+qRVOPQAlsWuX3w83znsAVqyAdu387P5evWDIEChfPuioRETCd+mll3Lttddyzz33MG3aNAYOHEipUqXo378/u3btYsuWLfTt25datWqxceNGhg0bxumnn87333/P4YcfDkDnzp1Zs2YNjz32GEcccQTr1q1j1qxZbN++HfANcZs2bfj222+5//77OfHEE1m0aBEDBw5k48aNPPHEE8WOu2fPnrRt25bXX3+dFStWcNddd1G6dGnGjh279z2dOnViypQp9OnTh+bNm/Pdd99x//33s3r1aiZOnBiZP8B8KAEoiTgvA+wcvPyyX95XsSJMmQJt2wYdlYjEUq9evppnkBo3hqefLtlndO/enbvvvhuA1q1bk5GRwRNPPEGvXr2oWrUqo0eP3vvePXv20KZNGw477DDeeOMNevfuDcDChQsZPHgwHTt23PveK3OVOX3jjTf46KOPmDdvHmeddRYA54TWRA8YMIA+ffpwaDGXSZ111lk899xze+NesWIFo0eP5uWXX8bMWLBgAW+++SZjx46lS6hb9txzz6V69ep06tSJZcuW0bhx4+L+cYVFQwAlEccJQEaGn+B37bW+uM+XX6rxF5HEddVVV+3zun379mzdupVvvvkGgAkTJnDaaadRtWpVypQpQ+XKldm6dSsrVqzYe02zZs147LHHeOaZZ/j6669xzu3zmR9++CH16tWjefPmZGVl7X20bt2azMxMFi1aVOy4L7zwwn1en3jiiezatYt169bt/c5y5cpx+eWX7/edAPPnzy/2d4ZLPQAlEac7AX7+ua/jv2YNDBrkt/ItXTroqEQkCCW9844Xhx12WL6vf/vtN6ZMmUK7du3o2rUrDzzwADVq1KBUqVJccMEF+4yjv/nmmwwYMIBHH32UXr16UatWLW688Ub69u1LqVKlWL9+PWvWrKFsASVQ//jjj2LHXb169X1elw+Nv+bEtX79enbv3k2VKlUi9p3hUgJQEnHWA5Cd7Yv53Hsv1K7tl/mdeWbQUYmIlNy6des46qij9nkNUKdOHYYPH87RRx/Nyy+/vPd8ZmYmGzdu3OczDj30UIYOHcrQoUNZsWIFY8eO5YEHHqBmzZrcdNNNHHLIIRx55JFMmDAh3xjSorAxyiGHHEKFChVYsGBBvudr164d8e/MoQSgJOIoAVi3Drp2hWnTfFnf0aOhWrWgoxIRiYwJEybsnQMAMH78eKpUqULDhg3Zvn07Zcrs25yNGzeOPXv2FPh59evXZ/Dgwbzwwgt7hxHOP/98Jk6cSJUqVTguRvVdzj//fB555BHS09P3zjeIFSUAJREnQwAzZkDnzj4fGT4cbrghaTYnFBEBYNSoUWRnZ9OsWTOmTZvG6NGj6d+/P1WrVuX888/n3XffpXfv3rRt25YlS5bw7LPPUrVq1b3Xp6enc+6559KxY0eOO+44ypYty6RJk9i0adPe8faOHTsyZswYzjnnHO68804aNWrE7t27WblyJZMnT+bdd9+lUqVKEf29WrRowdVXX80VV1zBHXfcwamnnkqpUqVYvXo177//Po888gjHHntsRL8zhxKAkgi4ByAzE/r2hUcfhQYNYOZMaNgwkFBERKJq0qRJ3HbbbQwcOJCDDz6Yvn37cv/99wN+hcAvv/zCSy+9xIgRI2jWrBlTpkzhsssu23t9hQoVaNKkCaNGjWLNmjWUKlWK+vXr89prr3HJJZcAULZsWaZNm8aQIUMYOXIkq1atonLlyvzlL3/hwgsvpFy5clH53V599VWee+45XnrpJR566CHKly9PWlra3pUM0WJ5Z0Ems6ZNm7rFixdH7gMHDoR+/XwtgBjvm/vTT3D11fDZZ9CjBzz1FEQ4MRWRBPPdd99x/PHHBx2GxFBR/83NbIlzrml+59QDUBIZGX6BfYwb//Hj/9fNP2EC5FrGKiIiEhbVASiJGJcB3rYNrrvO3/mfcIIv7qHGX0REDoQSgJKIYQLw1VfQtCmMGeOX+c2bB1FYkSIiIilCCUBJZGREfQWAczB0qK/mt3mzn/H/0EMxH3UQEZEkozkAJRHlHoCNG30p30mT4IILfF3/mjWj9nUiIpJC1ANQElFMXU5OwQAAFe1JREFUABYsgEaN4P334ckn/UY+avxFRCRSlACURBSGAPbsgQEDoEULqFABFi6E3r2hlP5LiYhIBGkIoCQi3APw669+B7/5831lv6FD4aCDIvbxIiIieykBOFB79sCWLRFLACZN8uP9u3bB2LEQ2hZaREQkKtSxfKC2bvXPJRwC2LkTbrsNLr0U6tWDpUvV+IuISPQpAThQEdgH4Pvv4fTT4fnnoVcvP94fpT0fRESkGF5++WXMjNWrVxf7WjOjf//+EY8p0jQEcKBKkAA45wv63Habr98/dSpceGGE4xMRESmEegAO1AFuBZyeDh06+JK+p50GX36pxl9ERGJPCcCBOoAegM8+g5NPhrfegkGDfFW/2rWjFJ+ISBLo378/Zsb3339PmzZtqFy5MnXr1mXMmDEAjBs3juOOO44qVarQsmVLVq5cuffazMxM+vbtS1paGuXKlSMtLY2+ffuSmZm5z3f89NNPXHjhhVSqVImaNWvSs2dPdu3alW88o0aNolGjRlSoUIEaNWpw3XXXsXHjxuj9AUSREoADVYwEIDsbHn0UzjzTLx6YPx/uuw9Kl45yjCIiSeLKK6/kwgsv5N133+WUU07h2muv5d5772X48OEMGTKEMWPGsGLFCjp06LD3mq5duzJkyBC6dOnC1KlT6datG4/8f3vnHmZ1Ve7xzxcYGrFDDdqA5ig3SyCRwzMdRzBAuSTRUdPjBcSwojTkEdOHoIhLipRRndI4gSWCQqKpFXSRQQxMD1SUlyMhZjBAMAiCATkj13X+WGtPe/bsYTbz23v23uz38zzr+e291nrX713vvqz3t6733suYMWPq8hw6dIihQ4fy4osvMmfOHBYsWMDmzZuZOXNmAx0mT57MuHHjGDJkCEuXLmX27Nk8/fTTDB8+nKNHj7aIHdKJzQFoLikOAbz5pp/VX1kJV18NP/oRlJS0gH6GYRjgZxi/9FJ2dejTB773vUhFTJw4kU+HJVLl5eUsW7aMefPmsXnzZtqH/+Hq6momTJjAli1bOHDgAI8++ijTp0+vm5A3bNgwWrduzdSpU5k8eTK9e/dm4cKFbNq0iTVr1lBRUQHA8OHDOf/88+vdv6qqitmzZzN9+nSmTZtWF/+hD32Iiy++mGXLlnHllVdGqmNLYz0AzSWFHoDKSujd2z/xz53ru/6t8TcMwzhxhg8fXve6pKSE0tJSKioq6hp/gPPOOw+Abdu28dxzzwEwevToeuXE3q9evRqANWvWUFZWVtf4A7Rq1Yprr722ntyKFSs4duwYN9xwA0eOHKkLF154Ie3bt6+7Xz5hPQDNZd8+34ffrl2DpEOH4Gtfg9mzoVcvWLkSPvKRLOhoGIYR8ck7VyhJeHpq27Zt0jiAd999t25c/owzzqiXp1OnTgB16dXV1XTs2LHB/RLjdu3aBUD37t2T6rdnz56U6pFLmAPQXGLnAEj1ojdtgpEj/YS/m2/2B/kk8REMwzCMDNKhQwcAdu7cSbdu3erid+7cCcBpp50GeAdh/fr1DeTffPPNeu9j+SsrKxs4HvHp+USLDwFIKpP0hKR9kvZLekrS2SnKFkuaLalaUq2kNZIGZFrnpCQ5B2DJEj/Lf+NG390/d641/oZhGNlg4MCBACxZsqRe/OLFiwEYMMA3HRdddBHbtm1j7dq1dXmOHTvG448/Xk9u6NChtGrViq1bt1JeXt4gdOnSJZPVyQgt2gMgqR3wLHAQGAM4YCbwW0m9nXPvNFHEg8AIYCKwCbgVWC7pIudcy85yiXMA3nkHbrsN5s+Hfv3gJz/x2/oahmEY2aFXr16MHDmSGTNmcOTIEfr168eaNWu4++67GTlyJL179wb+tVLgqquuYtasWZSWljJ37lz2xyZ6B7p168akSZMYP348GzduZODAgRQXF7Nt2zZWrFjB2LFjueSSS7JR1WbT0kMAnwe6Ah92zr0BIOkV4K/AzcB3GxOUdAEwCvisc+6hELcaWA/cBVyeWdUTCEMAL78M11/vn/qnTIEZM6CNDawYhmFknYULF9K1a1fmz5/PzJkzOfPMM5k0aRLTp0+vy9O2bVtWrFjB+PHjGTduHKeeeiqjRo1ixIgR3HLLLfXKmzVrFj169GDOnDnMmTMHSZSVlTF48GDOPffclq5eZOSca7mbSSuBYudc/4T41QDOuYHHkZ0KTAXe75yriYv/OjAZaO+cS75zQ6C8vNytW7cuQg3+hevbl6rDH6THX5fRoQMsWgSXXpqWog3DMJrFhg0b6NGjR7bVMFqQpj5zSX9yzpUnS2vpOQC9gFeTxK8HeqYguzm+8Y+TbQskn5qZAWpr4c2N+3jh1fcxeLDfztcaf8MwDCOfaOnO6g7A20ni9wJNrZA/nmwsvQGSvgB8AeDss1Oaa9gkxcXwp9MrKOvTl1/+vMFCAMMwDMPIebIxWp1szCGVJlTNkXXOPQA8AH4IIIX7NK2IoH/VYmv4DcMwjLylpYcA3ib5k3oJyZ/u49l7HNlYeothjb9hGIaRz7S0A7AeP5afSE/gLynIdglLCRNlDwFvRFfPMAzDMAqDlnYAlgIVkrrGIiR1BvqHtKZki4Br4mTbANcBlU2tADAMwygEWnJll5Fdon7WLe0A/AioAn4h6QpJlwO/ALYB82KZJJ0j6YikuiOXwkY/jwHfkzRW0mBgCdAFmI5hGEaBU1RURG1tbbbVMFqI2tpaioqKmi3fog5A2OnvUuB14BFgMbAZuNQ598+4rAJaJ9HvM8BD+N0DfwWUAZc55/6cYdUNwzByntLSUrZv305NTY31BJzEOOeoqalh+/btlJaWNrucFl8F4JzbClzdRJ4qkszud87VAneEYBiGYcQROxp3x44dHD58OMvaGJmkqKiIjh071jsO+USxTWsNwzBOItq3bx+pUTAKhxY/DdAwDMMwjOxjDoBhGIZhFCDmABiGYRhGAWIOgGEYhmEUIOYAGIZhGEYBYg6AYRiGYRQgKqTNIiTtBrakscjTgbfSWF4hYjaMjtkwOmbD9GB2jE66bXiOc+4DyRIKygFIN5LWOefKs61HPmM2jI7ZMDpmw/RgdoxOS9rQhgAMwzAMowAxB8AwDMMwChBzAKLxQLYVOAkwG0bHbBgds2F6MDtGp8VsaHMADMMwDKMAsR4AwzAMwyhAzAFIQFKZpCck7ZO0X9JTks5OUbZY0mxJ1ZJqJa2RNCDTOucazbWhpHJJD0h6TVKNpK2SFkvq0hJ65xpRvosJ5XxFkpP0fCb0zGWi2lBSD0k/lfRW+E1vlDQhkzrnGhH/E8+WtDD8lmskvS5ppqRTM613LiHpLEn3hzahJvweO6com7F2xRyAOCS1A54FzgPGADcC5wK/TfEL+yDweWAa8EmgGlguqU9mNM49ItrweqAXcB8wHJgM9AXWSSrLmNI5SBq+i7FyugJTgF2Z0DOXiWpDSeXA74H3AGOBTwDfAVpnSudcI4oNQ/ozwABgKjAC+DFwJzA/g2rnIt2Ba4G3gd+doGzm2hXnnIUQgAnAUaB7XFwX4AhwRxOyFwAO+ExcXBtgI7A023XLExt+IEncOcAx4K5s1y1f7JhQznJgHrAKeD7b9coXG+IfjtYDP8t2PfLYhsPCf+KwhPhvBvl22a5fC9qxVdzrscEunVOQy2i7Yj0A9bkcWOuceyMW4ZzbDLwAXJGC7GHgsTjZI8AS4OOS3pN+dXOSZtvQObc7SdwWYDfwwTTrmetE+S4CIGkUvgflKxnRMPeJYsNBQE/guxnTLj+IYsO24bo/If4feAdL6VIy13HOHWumaEbbFXMA6tMLeDVJ/Hr8n0FTspudczVJZNviu4AKgSg2bICkHkApsCGiXvlGJDtKKgH+G/iyc25vmnXLF6LY8OJwLZa0VtJhSbsk3SfplLRqmdtEseEzwF+BeyX1lPReSZfiexXmOufeSa+qJyUZbVfMAahPB/wYTSJ7gZIIsrH0QiCKDeshqQ0wF98D8GB01fKKqHacDbwOLEijTvlGFBueGa6PAZXAUOBb+O7bn6RLwTyg2TZ0zr2Ld6RiwykHgJXAL4Hx6VXzpCWj7UqbKMInKck2Rkilq0oRZE820mWHHwD9gBHOuWQ/gpOdZtlR0seATwN9XRg0LGCa+12MPRwtcs5NC69XSWoNfFNST+fcX9KiYe7T3O9hMd6BKsVPHtwK/Ad+MtsR4Itp1PFkJaPtijkA9Xmb5B5VCcm9sHj2AsmWxpTEpRcCUWxYh6RvAF8AxjjnKtOkWz4RxY7z8D0mf5f0/hDXBmgd3tc65w6mTdPcJYoN94TrioT4Svwktj5AITgAUWz4Ofxciu7Oub+FuOck7QMekDTXOfdy2jQ9Oclou2JDAPVZjx9zSaQnTf/Y1wNdwrKZRNlDwBsNRU5KotgQAElT8EsAJzjnHkmjbvlEFDv2AG7B/0HHQn+gIrwulCevqL9naPj0FXvyau6krnwjig3PB96Oa/xj/CFce0TUrRDIaLtiDkB9lgIVYe00AGGzhv4hrSnZIuCaONk2wHVAZYE8cUE0GyLpNmAmMMU5d3+GdMwHotjxkiThZfxkrkuAJ9Kvbk4SxYa/AQ4ClyXEfzxc16VHxZwnig13AiWSEieqXRiu29Ok48lMZtuVbK+PzKUAnIr3qP4Pv8Tlcvwf5ybgvXH5zsGPYU1LkF+Cf8IaCwzG/9G+ix+LzXr9ct2G+I2AjuH/fCsSQs9s1y1f7NhIeasovH0Aov6ep4f4WcAQfK9ULbAg23XLBxsCnfFLAF/HbyJ0CTAxxK0jbm18IQTgv0L4Ib5n6Yvh/cAmvocZa1eybpRcC/jxlifDl/QA8HMSNmwIX2wHzEiIPwW/bnhn+IB+DwzKdp3yxYb4GeuukbAq2/XKFzs2UlbBOQBRbYjv7r8jNICHgC3AXUBRtuuVRzbsCTwObMM7T68D3wZKsl2vLNjxuP9t2WhX7DRAwzAMwyhAbA6AYRiGYRQg5gAYhmEYRgFiDoBhGIZhFCDmABiGYRhGAWIOgGEYhmEUIOYAGIZhGEYBYg6AYURA0k2SXCNhyAmUUyVpQQZVTbxfvJ5HJG2SNF/SWWm+T+dwj5vi4m6S9NkkeWO27JxOHZrQb1ASW2yV9D/hSOXmlHm7pKvSrathpBs7DMgw0sM1wN8T4nL9sJgF+IOD2uAPt/k60F9SH+dcbZruUQ1cBMTvB39TuOf8hLy/Cnmr03TvE+E24I9AO/xua5OAMuA/m1HW7cDzwFNp084wMoA5AIaRHl5yzuXbgU/bnXNrw+vnJR3AOwXDSVPj5fxe5WubzOjz7gZ2p+O+zWBDnC2elVQKjJXUyTm3M0s6GUZGsSEAw8ggkoZJ+rWkakk1kl6VdGc4V/54cp0kLZS0Q9LBIP/L0DDF8rSTdK+kzZIOhesUSc39Xf8xXLuH8s+Q9LCkt4IOr0gafSJ6Jg4BSFoFDMT3NMS63VeFtHpDAMFuf0pimzNCV/3tcXFdJC2WtDvo8ZKkTzXTDgB/Dte6o1glfVTSE5L+LqlW0kZJsySdEpenCr+n+w1x9VsQl36BpKWS3g5lvCDpYxH0NIxmYz0AhpEeWodTumI459xRoCuwErgfv493OTAD+AD+cJnGeATfkEzE76PeEd813Q7qTgRbjt9r/W78YS0VwFT8+e13NqMOXcL1H5JOBVbjzx3/atBhNPCIpHbOuQdS0TMJ44BFQGvg5hC3v5G8DwOPSurpnIsfThkVro8CSCrD74++C/gSvhfhOuBJSVc655o8hTIJnYGjQFVc3NnAS/hekgP4Y3Kn4T/j60OeTwG/xh+YMyPE7Q569gV+B7wIfB6owR/b/Iykfs65Bs6OYWSUbB+QYMFCPgf8eHayAz4aHLyDP1ymDTAFf7pXq7i0KuJOmQP+Cdx2nPveGO4zICF+Cv7gmtIm9HbAPUGfYrzzsAF4BzgTGB/yDEqQewbf0LZOUc/OoZyb4uJWNWKfmC07h/enAPuAbyTkewn4ddz7B/GN7GkJ+Vbgh2aOZ4dB4Z7Dgi3+DbgS75R8+zhysc9yNP4Ey9Pi0qqARUlkVgYbt42Lax3ifp7t77KFwgs2BGAY6eFTwEfjwuegrrt6nqQt+Ib5MDATeD9Q2khZ4LvjJ0qaIOl8SUpIvwx/Ot3/SmoTC0Al/vzwihR0/mrQpxZYE15/wjm3AxiAnyOwKkFmEb73omeKejYb5yciPonvTheApPOBC/C9AzEuwz9170uwxXLgAkntU7jdcnz99wM/A57D92rUIal9GHL5G3Aw5H8E7wyce7zCwzDBQOCnwLE4HYV3qgakoKNhpBVzAAwjPbzqnFsXFzaGsfilwCfxjf6leOfgniBTfJzyrguyXwZeAbZLmhY3vl+K73o/nBD+ENJPS0Hn+UGffwdOd871ds6tDmkdSD4bf2dceip6RuVh/Gz8QeH9jfju91/E5SkFPk1DW8wO6anY4la8LYYAjwEj8MMp8TyE77K/Dxga8t8a0o73WYK3V+tQZqKe44GSNNrMMFLC5gAYRubohh/zv9E5tygWKanJpWXOuV34xuVWSR8GxuCX6e0GfgjsATYD1zZSRFUK+lU759Y1krYX+HCS+E7huidFPaOyGtgKjJa0GhgJPOHqL1Pcgx9bv7eRMnakcJ/XY7aQ9Cx+LsNXJT3knNsmqRi4An9W+/djQqFHIhX+gR8qmEP93os6nHPHUizLMNKCOQCGkTliE+EOxyIkFQE3nEghzrmN+MboFuAjIfpp4Grgn86519KgayKrgWsk9XfOvRAXPwo/B2BDinom4yB+rL1JnHNO0mK8k/Ez4CwaNqBP4/cPWO/SsH9BuOft+Ml6k8O934N/gj+ckP2mJEUcxM9fiC/zHUm/ww9f/NkaeyMXMAfAMDLHBvw4/T2SjuIbjy81JSTpffhx4cXAa0HuCvyM/MqQbTHwGWClpO/gZ523xfc6XA5c6ZyriaD7AmAC8JSkKfhNjm7Ad33f7Jw7mqKeyfgLME7SdfgNgg4E56ExHga+AszFrzRYnZA+DT/08ZykH+B7P0rwTkhX51yDXQebwjn3sqQngc9Jusc5t0PSWuBOSdXAW8BngQ82Ur+PSfokfsjkLedcFXAHfm7BckkP4odYTgf64idVHm9ViGGkn2zPQrRgIZ8D/5q53r2R9D74XeFq8I3oXcBY4ma7h3xVhFUA+KfNecB6/Cz7/fjJdqMSyi7GLzV7Df/UuTfkmwG0aUJvB8xsIs8Z+Elub4XyXwFGx6U3qSfJVwF0wk/aOxDSViXYsnMSXf4Y0mY1outZwI+B7fjJltX4VQCjm6jjoFDukCRpPfBLAb8fV5ffBL13AT/AzxWot1oCOA8/JFET0hYklLkkyB8M34ml+MmXWf8+WyisIOcchmEYhmEUFjbr1DAMwzAKEHMADMMwDKMAMQfAMAzDMAoQcwAMwzAMowAxB8AwDMMwChBzAAzDMAyjADEHwDAMwzAKEHMADMMwDKMAMQfAMAzDMAqQ/wdz+vbBqwM5wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(rf_predictions, rf_probs, train_rf_predictions, train_rf_probs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[152  15]\n",
      " [  6 242]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, rf_predictions)\n",
    "plot_confusion_matrix(cm, classes = ['No BRCA', 'BRCA'],\n",
    "                      title = 'Cancer Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.840804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.084966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.032040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.027807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>881</td>\n",
       "      <td>0.014383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>663</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>666</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>665</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>664</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     feature  importance\n",
       "0          0    0.840804\n",
       "7          7    0.084966\n",
       "21        21    0.032040\n",
       "4          4    0.027807\n",
       "881      881    0.014383\n",
       "663      663    0.000000\n",
       "667      667    0.000000\n",
       "666      666    0.000000\n",
       "665      665    0.000000\n",
       "664      664    0.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(X_traind.columns)\n",
    "fi_model = pd.DataFrame({'feature': X_traind.columns,\n",
    "                   'importance': clf.feature_importances_}).\\\n",
    "                    sort_values('importance', ascending = False)\n",
    "fi_model.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>ACAP3</th>\n",
       "      <th>ACTRT2</th>\n",
       "      <th>AGRN</th>\n",
       "      <th>ANKRD65</th>\n",
       "      <th>ATAD3A</th>\n",
       "      <th>ATAD3B</th>\n",
       "      <th>ATAD3C</th>\n",
       "      <th>AURKAIP1</th>\n",
       "      <th>B3GALT6</th>\n",
       "      <th>C1orf159</th>\n",
       "      <th>...</th>\n",
       "      <th>SMIM9</th>\n",
       "      <th>SNORA36A</th>\n",
       "      <th>SNORA56</th>\n",
       "      <th>TMLHE</th>\n",
       "      <th>VBP1</th>\n",
       "      <th>IL9R|ENSG00000124334.12</th>\n",
       "      <th>SPRY3|ENSG00000168939.6</th>\n",
       "      <th>VAMP7|ENSG00000124333.10</th>\n",
       "      <th>WASH6P|ENSG00000182484.10</th>\n",
       "      <th>WASIR1|ENSG00000185203.7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-3C-AAAU-01</th>\n",
       "      <td>0.069</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.069</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>-0.395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-3C-AALI-01</th>\n",
       "      <td>-1.008</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.504</td>\n",
       "      <td>-0.504</td>\n",
       "      <td>-0.504</td>\n",
       "      <td>-0.504</td>\n",
       "      <td>-0.504</td>\n",
       "      <td>-0.504</td>\n",
       "      <td>-0.504</td>\n",
       "      <td>-0.504</td>\n",
       "      <td>-0.504</td>\n",
       "      <td>-0.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-3C-AALJ-01</th>\n",
       "      <td>-0.330</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-3C-AALK-01</th>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-4H-AAAK-01</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-72-4241-01</th>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-OY-A56P-01</th>\n",
       "      <td>0.813</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.813</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-OY-A56Q-01</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-VG-A8LO-01</th>\n",
       "      <td>0.137</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.137</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>-0.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-WR-A838-01</th>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1658 rows × 24776 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ACAP3  ACTRT2   AGRN  ANKRD65  ATAD3A  ATAD3B  ATAD3C  \\\n",
       "TCGA-3C-AAAU-01  0.069   0.069  0.069    0.069   0.069   0.069   0.069   \n",
       "TCGA-3C-AALI-01 -1.008  -1.008 -1.008   -1.008  -1.008  -1.008  -1.008   \n",
       "TCGA-3C-AALJ-01 -0.330  -0.330 -0.330   -0.330  -0.330  -0.330  -0.330   \n",
       "TCGA-3C-AALK-01 -0.023  -0.023 -0.023   -0.023  -0.023  -0.023  -0.023   \n",
       "TCGA-4H-AAAK-01  0.000   0.000  0.000    0.000   0.000   0.000   0.000   \n",
       "...                ...     ...    ...      ...     ...     ...     ...   \n",
       "TCGA-72-4241-01  0.339   0.339  0.339    0.339   0.339   0.339   0.339   \n",
       "TCGA-OY-A56P-01  0.813   0.813  0.813    0.813   0.813   0.813   0.813   \n",
       "TCGA-OY-A56Q-01  0.000   0.000  0.000    0.000   0.000   0.000   0.000   \n",
       "TCGA-VG-A8LO-01  0.137   0.137  0.137    0.137   0.137   0.137   0.137   \n",
       "TCGA-WR-A838-01 -0.176  -0.176 -0.176   -0.176  -0.176  -0.176  -0.176   \n",
       "\n",
       "                 AURKAIP1  B3GALT6  C1orf159  ...  SMIM9  SNORA36A  SNORA56  \\\n",
       "TCGA-3C-AAAU-01     0.069    0.069     0.069  ... -0.395    -0.395   -0.395   \n",
       "TCGA-3C-AALI-01    -1.008   -1.008    -1.008  ... -0.504    -0.504   -0.504   \n",
       "TCGA-3C-AALJ-01    -0.330   -0.330    -0.330  ... -0.063    -0.063   -0.063   \n",
       "TCGA-3C-AALK-01    -0.023   -0.023    -0.023  ...  0.008     0.008    0.008   \n",
       "TCGA-4H-AAAK-01     0.000    0.000     0.000  ... -0.020    -0.020   -0.020   \n",
       "...                   ...      ...       ...  ...    ...       ...      ...   \n",
       "TCGA-72-4241-01     0.339    0.339     0.339  ... -0.100    -0.100   -0.100   \n",
       "TCGA-OY-A56P-01     0.813    0.813     0.813  ... -0.080    -0.080   -0.080   \n",
       "TCGA-OY-A56Q-01     0.000    0.000     0.000  ... -0.195    -0.195   -0.195   \n",
       "TCGA-VG-A8LO-01     0.137    0.137     0.137  ... -0.675    -0.675   -0.675   \n",
       "TCGA-WR-A838-01    -0.176   -0.176    -0.176  ...  0.065     0.065    0.065   \n",
       "\n",
       "                 TMLHE   VBP1  IL9R|ENSG00000124334.12  \\\n",
       "TCGA-3C-AAAU-01 -0.395 -0.395                   -0.395   \n",
       "TCGA-3C-AALI-01 -0.504 -0.504                   -0.504   \n",
       "TCGA-3C-AALJ-01 -0.063 -0.063                   -0.063   \n",
       "TCGA-3C-AALK-01  0.008  0.008                    0.008   \n",
       "TCGA-4H-AAAK-01 -0.020 -0.020                   -0.020   \n",
       "...                ...    ...                      ...   \n",
       "TCGA-72-4241-01 -0.100 -0.100                   -0.100   \n",
       "TCGA-OY-A56P-01 -0.080 -0.080                   -0.080   \n",
       "TCGA-OY-A56Q-01 -0.195 -0.195                   -0.195   \n",
       "TCGA-VG-A8LO-01 -0.675 -0.675                   -0.675   \n",
       "TCGA-WR-A838-01  0.065  0.065                    0.065   \n",
       "\n",
       "                 SPRY3|ENSG00000168939.6  VAMP7|ENSG00000124333.10  \\\n",
       "TCGA-3C-AAAU-01                   -0.395                    -0.395   \n",
       "TCGA-3C-AALI-01                   -0.504                    -0.504   \n",
       "TCGA-3C-AALJ-01                   -0.063                    -0.063   \n",
       "TCGA-3C-AALK-01                    0.008                     0.008   \n",
       "TCGA-4H-AAAK-01                   -0.020                    -0.020   \n",
       "...                                  ...                       ...   \n",
       "TCGA-72-4241-01                   -0.100                    -0.100   \n",
       "TCGA-OY-A56P-01                   -0.080                    -0.080   \n",
       "TCGA-OY-A56Q-01                   -0.195                    -0.195   \n",
       "TCGA-VG-A8LO-01                   -0.675                    -0.675   \n",
       "TCGA-WR-A838-01                    0.065                     0.065   \n",
       "\n",
       "                 WASH6P|ENSG00000182484.10  WASIR1|ENSG00000185203.7  \n",
       "TCGA-3C-AAAU-01                     -0.395                    -0.395  \n",
       "TCGA-3C-AALI-01                     -0.504                    -0.504  \n",
       "TCGA-3C-AALJ-01                     -0.063                    -0.063  \n",
       "TCGA-3C-AALK-01                      0.008                     0.008  \n",
       "TCGA-4H-AAAK-01                     -0.020                    -0.020  \n",
       "...                                    ...                       ...  \n",
       "TCGA-72-4241-01                     -0.100                    -0.100  \n",
       "TCGA-OY-A56P-01                     -0.080                    -0.080  \n",
       "TCGA-OY-A56Q-01                     -0.195                    -0.195  \n",
       "TCGA-VG-A8LO-01                     -0.675                    -0.675  \n",
       "TCGA-WR-A838-01                      0.065                     0.065  \n",
       "\n",
       "[1658 rows x 24776 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10655 10657 10654 10658 10656]\n",
      "10655\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC0</td>\n",
       "      <td>[IKZF3, GRB7, ERBB2, PGAP3, STARD3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PC1</td>\n",
       "      <td>[CASC8, MYC, CCAT1, PVT1, POU5F1B]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PC2</td>\n",
       "      <td>[TRAPPC9, LINC00964, MROH1, FAM203A, MTSS1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PC3</td>\n",
       "      <td>[CHRM3, ZNF695, ZNF670, ACTN2, SNORA25|ENSG000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PC4</td>\n",
       "      <td>[PCK1, DOK5, CTCFL, ZBP1, PMEPA1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PC5</td>\n",
       "      <td>[USP32, CA4, SCARNA20|ENSG00000252577.1, TUBD1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PC6</td>\n",
       "      <td>[VMP1, USP32, PTRH2, TUBD1, MIR21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PC7</td>\n",
       "      <td>[RPS9, ZNF808, ZNF83, LENG1, CNOT3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PC8</td>\n",
       "      <td>[CLTC, PTRH2, TUBD1, RGS9, MIR548D2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PC9</td>\n",
       "      <td>[TBXAS1, MRPS33, MGAM, CNTNAP2, JHDM1D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PC10</td>\n",
       "      <td>[CACNA1A, NFIX, IER2, LYL1, TRMT1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PC11</td>\n",
       "      <td>[C6orf10, LTB, HSPA1A, HSPA1B, HSPA1L]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PC12</td>\n",
       "      <td>[GRIN2B, C1S, USP5, LPCAT3, RPL13P5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PC13</td>\n",
       "      <td>[CACNA1A, HOOK2, RTBDN, GCDH, MIR5695]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PC14</td>\n",
       "      <td>[ANKRD26P1, PICALM, SYTL2, TMEM126A, EED]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PC15</td>\n",
       "      <td>[MIR548K, KCTD14, PPFIA1, AAMDC, INTS4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PC16</td>\n",
       "      <td>[RNA5SP45, CTPS1, EDN2, HIVEP3, FOXJ3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PC17</td>\n",
       "      <td>[FGFR1, LETM2, WHSC1L1, C8orf86, RPS20P22]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PC18</td>\n",
       "      <td>[ORAOV1, CCND1, FGF19, FGF4, FGF3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PC19</td>\n",
       "      <td>[LETM2, FGFR1, RPS20P22, WHSC1L1, C8orf86]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PC20</td>\n",
       "      <td>[CALB2, CHST4, ZNF23, ZNF19, TAT]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PC21</td>\n",
       "      <td>[PPFIA1, MIR548K, SHANK2, FADD, ANO1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PC22</td>\n",
       "      <td>[STARD3, PPP1R1B, TCAP, PNMT, PGAP3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PC23</td>\n",
       "      <td>[GRB7, STARD3, ERBB2, PNMT, TCAP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PC24</td>\n",
       "      <td>[MTUS1, SNORA62|ENSG00000201157.1, MIR548V, RN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PC25</td>\n",
       "      <td>[TACC1, PLEKHA2, WHSC1L1, HTRA4, LETM2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PC26</td>\n",
       "      <td>[TERT, NKD2, MIR4457, CLPTM1L, SLC6A3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PC27</td>\n",
       "      <td>[SAMD4B, RN7SL566P, PAF1, MED29, GMFG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PC28</td>\n",
       "      <td>[RAB11FIP1, BRF2, GPR124, RN7SL709P, DDHD2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PC29</td>\n",
       "      <td>[GRB7, MIEN1, ERBB2, MIR4728, PGAP3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0                                                  1\n",
       "0    PC0                [IKZF3, GRB7, ERBB2, PGAP3, STARD3]\n",
       "1    PC1                 [CASC8, MYC, CCAT1, PVT1, POU5F1B]\n",
       "2    PC2        [TRAPPC9, LINC00964, MROH1, FAM203A, MTSS1]\n",
       "3    PC3  [CHRM3, ZNF695, ZNF670, ACTN2, SNORA25|ENSG000...\n",
       "4    PC4                  [PCK1, DOK5, CTCFL, ZBP1, PMEPA1]\n",
       "5    PC5  [USP32, CA4, SCARNA20|ENSG00000252577.1, TUBD1...\n",
       "6    PC6                 [VMP1, USP32, PTRH2, TUBD1, MIR21]\n",
       "7    PC7                [RPS9, ZNF808, ZNF83, LENG1, CNOT3]\n",
       "8    PC8               [CLTC, PTRH2, TUBD1, RGS9, MIR548D2]\n",
       "9    PC9            [TBXAS1, MRPS33, MGAM, CNTNAP2, JHDM1D]\n",
       "10  PC10                 [CACNA1A, NFIX, IER2, LYL1, TRMT1]\n",
       "11  PC11             [C6orf10, LTB, HSPA1A, HSPA1B, HSPA1L]\n",
       "12  PC12               [GRIN2B, C1S, USP5, LPCAT3, RPL13P5]\n",
       "13  PC13             [CACNA1A, HOOK2, RTBDN, GCDH, MIR5695]\n",
       "14  PC14          [ANKRD26P1, PICALM, SYTL2, TMEM126A, EED]\n",
       "15  PC15            [MIR548K, KCTD14, PPFIA1, AAMDC, INTS4]\n",
       "16  PC16             [RNA5SP45, CTPS1, EDN2, HIVEP3, FOXJ3]\n",
       "17  PC17         [FGFR1, LETM2, WHSC1L1, C8orf86, RPS20P22]\n",
       "18  PC18                 [ORAOV1, CCND1, FGF19, FGF4, FGF3]\n",
       "19  PC19         [LETM2, FGFR1, RPS20P22, WHSC1L1, C8orf86]\n",
       "20  PC20                  [CALB2, CHST4, ZNF23, ZNF19, TAT]\n",
       "21  PC21              [PPFIA1, MIR548K, SHANK2, FADD, ANO1]\n",
       "22  PC22               [STARD3, PPP1R1B, TCAP, PNMT, PGAP3]\n",
       "23  PC23                  [GRB7, STARD3, ERBB2, PNMT, TCAP]\n",
       "24  PC24  [MTUS1, SNORA62|ENSG00000201157.1, MIR548V, RN...\n",
       "25  PC25            [TACC1, PLEKHA2, WHSC1L1, HTRA4, LETM2]\n",
       "26  PC26             [TERT, NKD2, MIR4457, CLPTM1L, SLC6A3]\n",
       "27  PC27             [SAMD4B, RN7SL566P, PAF1, MED29, GMFG]\n",
       "28  PC28        [RAB11FIP1, BRF2, GPR124, RN7SL709P, DDHD2]\n",
       "29  PC29               [GRB7, MIEN1, ERBB2, MIR4728, PGAP3]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# number of components\n",
    "n_pcs= pca.components_.shape[0]\n",
    "\n",
    "# get the index of the most important feature on EACH component\n",
    "# LIST COMPREHENSION HERE\n",
    "print((np.abs(pca.components_[1]).argsort()[::-1])[:5])\n",
    "print(np.abs(pca.components_[1]).argmax())\n",
    "\n",
    "most_important = [np.abs(((np.abs(pca.components_[i]).argsort()[:5:-1])[:5])) for i in range(n_pcs)]\n",
    "\n",
    "initial_feature_names = features1\n",
    "\n",
    "most_important_names2 = []\n",
    "for x in most_important:\n",
    "    temp = []\n",
    "    for y in x:\n",
    "        temp.append(initial_feature_names[y])\n",
    "    most_important_names2.append(temp)\n",
    "        \n",
    "dic = {'PC{}'.format(i): most_important_names2[i] for i in range(n_pcs)}\n",
    "df = pd.DataFrame(dic.items())\n",
    "#print(most_important_names2)\n",
    "\n",
    "df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC0</td>\n",
       "      <td>[IKZF3, GRB7, ERBB2, PGAP3, STARD3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PC1</td>\n",
       "      <td>[CASC8, MYC, CCAT1, PVT1, POU5F1B]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PC2</td>\n",
       "      <td>[TRAPPC9, LINC00964, MROH1, FAM203A, MTSS1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PC3</td>\n",
       "      <td>[CHRM3, ZNF695, ZNF670, ACTN2, SNORA25|ENSG000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PC4</td>\n",
       "      <td>[PCK1, DOK5, CTCFL, ZBP1, PMEPA1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PC5</td>\n",
       "      <td>[USP32, CA4, SCARNA20|ENSG00000252577.1, TUBD1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PC6</td>\n",
       "      <td>[VMP1, USP32, PTRH2, TUBD1, MIR21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PC7</td>\n",
       "      <td>[RPS9, ZNF808, ZNF83, LENG1, CNOT3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PC8</td>\n",
       "      <td>[CLTC, PTRH2, TUBD1, RGS9, MIR548D2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PC9</td>\n",
       "      <td>[TBXAS1, MRPS33, MGAM, CNTNAP2, JHDM1D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PC10</td>\n",
       "      <td>[CACNA1A, NFIX, IER2, LYL1, TRMT1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PC11</td>\n",
       "      <td>[C6orf10, LTB, HSPA1A, HSPA1B, HSPA1L]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PC12</td>\n",
       "      <td>[GRIN2B, C1S, USP5, LPCAT3, RPL13P5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PC13</td>\n",
       "      <td>[CACNA1A, HOOK2, RTBDN, GCDH, MIR5695]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PC14</td>\n",
       "      <td>[ANKRD26P1, PICALM, SYTL2, TMEM126A, EED]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PC15</td>\n",
       "      <td>[MIR548K, KCTD14, PPFIA1, AAMDC, INTS4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PC16</td>\n",
       "      <td>[RNA5SP45, CTPS1, EDN2, HIVEP3, FOXJ3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PC17</td>\n",
       "      <td>[FGFR1, LETM2, WHSC1L1, C8orf86, RPS20P22]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PC18</td>\n",
       "      <td>[ORAOV1, CCND1, FGF19, FGF4, FGF3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PC19</td>\n",
       "      <td>[LETM2, FGFR1, RPS20P22, WHSC1L1, C8orf86]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PC20</td>\n",
       "      <td>[CALB2, CHST4, ZNF23, ZNF19, TAT]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PC21</td>\n",
       "      <td>[PPFIA1, MIR548K, SHANK2, FADD, ANO1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PC22</td>\n",
       "      <td>[STARD3, PPP1R1B, TCAP, PNMT, PGAP3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PC23</td>\n",
       "      <td>[GRB7, STARD3, ERBB2, PNMT, TCAP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PC24</td>\n",
       "      <td>[MTUS1, SNORA62|ENSG00000201157.1, MIR548V, RN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PC25</td>\n",
       "      <td>[TACC1, PLEKHA2, WHSC1L1, HTRA4, LETM2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PC26</td>\n",
       "      <td>[TERT, NKD2, MIR4457, CLPTM1L, SLC6A3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PC27</td>\n",
       "      <td>[SAMD4B, RN7SL566P, PAF1, MED29, GMFG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PC28</td>\n",
       "      <td>[RAB11FIP1, BRF2, GPR124, RN7SL709P, DDHD2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PC29</td>\n",
       "      <td>[GRB7, MIEN1, ERBB2, MIR4728, PGAP3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PC30</td>\n",
       "      <td>[PGAP3, MIEN1, ERBB2, MIR4728, IKZF3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PC31</td>\n",
       "      <td>[RYR1, CABLES1, GGN, SPRED3, TMEM241]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PC32</td>\n",
       "      <td>[STEAP1B, RN7SL542P, RNA5SP227, ABCB5, SKAP2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PC33</td>\n",
       "      <td>[PLCB1, CDS2, RN7SL514P, TMX4, SLC23A2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PC34</td>\n",
       "      <td>[MIR148A, NFE2L3, DSG4, HIBADH, HNRNPA2B1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PC35</td>\n",
       "      <td>[PFKP, PITRM1, LINC00700, LINC00701, CDC123]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>PC36</td>\n",
       "      <td>[ASH2L, LSM1, EIF4EBP1, ERLIN2, BRF2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>PC37</td>\n",
       "      <td>[INTS4, AAMDC, CLNS1A, KCTD14, RSF1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PC38</td>\n",
       "      <td>[TACC1, DDHD2, BAG4, LSM1, EIF4EBP1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>PC39</td>\n",
       "      <td>[CLNS1A, INTS4, RSF1, AAMDC, AQP11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>PC40</td>\n",
       "      <td>[FAM110A, ANGPT4, TMEM74B, PSMF1, C20orf202]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>PC41</td>\n",
       "      <td>[RSF1, CLNS1A, RAB11FIP1, DDHD2, AAMDC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>PC42</td>\n",
       "      <td>[RSF1, AAMDC, INTS4, AQP11, CLNS1A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>PC43</td>\n",
       "      <td>[PRED62, COL6A1, PWP2, C21orf33, C21orf90]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>PC44</td>\n",
       "      <td>[PPP1R3F, FOXP3, PRICKLE3, SYP, CACNA1F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>PC45</td>\n",
       "      <td>[TPCN2, MRGPRD, UNC93B1, ACY3, DOC2GP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>PC46</td>\n",
       "      <td>[MIR21, USP32, VMP1, TUBD1, PTRH2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>PC47</td>\n",
       "      <td>[TAF3, LINC00708, GATA3, RNA5SP299, ATP5C1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>PC48</td>\n",
       "      <td>[INTS4, BAG4, AAMDC, ADAM32, ADAM9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>PC49</td>\n",
       "      <td>[CBX1, UBE2Z, MIR1203, SNX11, ATP5G1]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0                                                  1\n",
       "0    PC0                [IKZF3, GRB7, ERBB2, PGAP3, STARD3]\n",
       "1    PC1                 [CASC8, MYC, CCAT1, PVT1, POU5F1B]\n",
       "2    PC2        [TRAPPC9, LINC00964, MROH1, FAM203A, MTSS1]\n",
       "3    PC3  [CHRM3, ZNF695, ZNF670, ACTN2, SNORA25|ENSG000...\n",
       "4    PC4                  [PCK1, DOK5, CTCFL, ZBP1, PMEPA1]\n",
       "5    PC5  [USP32, CA4, SCARNA20|ENSG00000252577.1, TUBD1...\n",
       "6    PC6                 [VMP1, USP32, PTRH2, TUBD1, MIR21]\n",
       "7    PC7                [RPS9, ZNF808, ZNF83, LENG1, CNOT3]\n",
       "8    PC8               [CLTC, PTRH2, TUBD1, RGS9, MIR548D2]\n",
       "9    PC9            [TBXAS1, MRPS33, MGAM, CNTNAP2, JHDM1D]\n",
       "10  PC10                 [CACNA1A, NFIX, IER2, LYL1, TRMT1]\n",
       "11  PC11             [C6orf10, LTB, HSPA1A, HSPA1B, HSPA1L]\n",
       "12  PC12               [GRIN2B, C1S, USP5, LPCAT3, RPL13P5]\n",
       "13  PC13             [CACNA1A, HOOK2, RTBDN, GCDH, MIR5695]\n",
       "14  PC14          [ANKRD26P1, PICALM, SYTL2, TMEM126A, EED]\n",
       "15  PC15            [MIR548K, KCTD14, PPFIA1, AAMDC, INTS4]\n",
       "16  PC16             [RNA5SP45, CTPS1, EDN2, HIVEP3, FOXJ3]\n",
       "17  PC17         [FGFR1, LETM2, WHSC1L1, C8orf86, RPS20P22]\n",
       "18  PC18                 [ORAOV1, CCND1, FGF19, FGF4, FGF3]\n",
       "19  PC19         [LETM2, FGFR1, RPS20P22, WHSC1L1, C8orf86]\n",
       "20  PC20                  [CALB2, CHST4, ZNF23, ZNF19, TAT]\n",
       "21  PC21              [PPFIA1, MIR548K, SHANK2, FADD, ANO1]\n",
       "22  PC22               [STARD3, PPP1R1B, TCAP, PNMT, PGAP3]\n",
       "23  PC23                  [GRB7, STARD3, ERBB2, PNMT, TCAP]\n",
       "24  PC24  [MTUS1, SNORA62|ENSG00000201157.1, MIR548V, RN...\n",
       "25  PC25            [TACC1, PLEKHA2, WHSC1L1, HTRA4, LETM2]\n",
       "26  PC26             [TERT, NKD2, MIR4457, CLPTM1L, SLC6A3]\n",
       "27  PC27             [SAMD4B, RN7SL566P, PAF1, MED29, GMFG]\n",
       "28  PC28        [RAB11FIP1, BRF2, GPR124, RN7SL709P, DDHD2]\n",
       "29  PC29               [GRB7, MIEN1, ERBB2, MIR4728, PGAP3]\n",
       "30  PC30              [PGAP3, MIEN1, ERBB2, MIR4728, IKZF3]\n",
       "31  PC31              [RYR1, CABLES1, GGN, SPRED3, TMEM241]\n",
       "32  PC32      [STEAP1B, RN7SL542P, RNA5SP227, ABCB5, SKAP2]\n",
       "33  PC33            [PLCB1, CDS2, RN7SL514P, TMX4, SLC23A2]\n",
       "34  PC34         [MIR148A, NFE2L3, DSG4, HIBADH, HNRNPA2B1]\n",
       "35  PC35       [PFKP, PITRM1, LINC00700, LINC00701, CDC123]\n",
       "36  PC36              [ASH2L, LSM1, EIF4EBP1, ERLIN2, BRF2]\n",
       "37  PC37               [INTS4, AAMDC, CLNS1A, KCTD14, RSF1]\n",
       "38  PC38               [TACC1, DDHD2, BAG4, LSM1, EIF4EBP1]\n",
       "39  PC39                [CLNS1A, INTS4, RSF1, AAMDC, AQP11]\n",
       "40  PC40       [FAM110A, ANGPT4, TMEM74B, PSMF1, C20orf202]\n",
       "41  PC41            [RSF1, CLNS1A, RAB11FIP1, DDHD2, AAMDC]\n",
       "42  PC42                [RSF1, AAMDC, INTS4, AQP11, CLNS1A]\n",
       "43  PC43         [PRED62, COL6A1, PWP2, C21orf33, C21orf90]\n",
       "44  PC44           [PPP1R3F, FOXP3, PRICKLE3, SYP, CACNA1F]\n",
       "45  PC45             [TPCN2, MRGPRD, UNC93B1, ACY3, DOC2GP]\n",
       "46  PC46                 [MIR21, USP32, VMP1, TUBD1, PTRH2]\n",
       "47  PC47        [TAF3, LINC00708, GATA3, RNA5SP299, ATP5C1]\n",
       "48  PC48                [INTS4, BAG4, AAMDC, ADAM32, ADAM9]\n",
       "49  PC49              [CBX1, UBE2Z, MIR1203, SNX11, ATP5G1]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC0</td>\n",
       "      <td>IKZF3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PC1</td>\n",
       "      <td>CASC8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PC2</td>\n",
       "      <td>TRAPPC9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PC3</td>\n",
       "      <td>CHRM3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PC4</td>\n",
       "      <td>PCK1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PC5</td>\n",
       "      <td>USP32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PC6</td>\n",
       "      <td>VMP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PC7</td>\n",
       "      <td>RPS9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PC8</td>\n",
       "      <td>CLTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PC9</td>\n",
       "      <td>TBXAS1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0        1\n",
       "0  PC0    IKZF3\n",
       "1  PC1    CASC8\n",
       "2  PC2  TRAPPC9\n",
       "3  PC3    CHRM3\n",
       "4  PC4     PCK1\n",
       "5  PC5    USP32\n",
       "6  PC6     VMP1\n",
       "7  PC7     RPS9\n",
       "8  PC8     CLTC\n",
       "9  PC9   TBXAS1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "# get the names\n",
    "most_important_names = [initial_feature_names[most_important[i]] for i in range(n_pcs)]\n",
    "\n",
    "# LIST COMPREHENSION HERE AGAIN\n",
    "dic = {'PC{}'.format(i): most_important_names[i] for i in range(n_pcs)}\n",
    "\n",
    "# build the dataframe\n",
    "df = pd.DataFrame(dic.items())\n",
    "\"\"\""
   ]
  }
 ],
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