3.1.random_forestBasic .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_score, recall_score, roc_auc_score, roc_curve\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size brca, ovca 1080 579\n",
      "normal size 1659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danielraad/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "def get_datasets(std_size=False):\n",
    "    # My python only likes absolute paths \n",
    "    brca = pd.read_csv('/Users/danielraad/Desktop/data/Gistic2_CopyNumber_BRCA', sep='\\t')\n",
    "    ovca = pd.read_csv('/Users/danielraad/Desktop/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",
    "    return df\n",
    "df = get_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperate the target variable from the other variables\n",
    "from sklearn.model_selection import train_test_split\n",
    "y = df[\"BRCA\"]\n",
    "X = df[df.columns[:-1]]\n",
    "X_train, X_test, y_train, y_tests = train_test_split(X, y, test_size = 0.33)\n",
    "y_red = pd.Series(y)\n",
    "features = list(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperate the target variable from the other variables\n",
    "from sklearn.model_selection import train_test_split\n",
    "X, y = df.drop(['BRCA'], axis = 1), df['BRCA']\n",
    "X_train, X_test, y_train, y_tests = train_test_split(X, y, test_size = 0.33)\n",
    "y_red = pd.Series(y)\n",
    "features = list(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danielraad/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Random forest classifier model \n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "model = RandomForestClassifier(random_state = 42)\n",
    "model.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of nodes 97\n",
      "Average maximum depth 15\n"
     ]
    }
   ],
   "source": [
    "#Depths of the decision trees \n",
    "n_nodes = []\n",
    "max_depths = []\n",
    "\n",
    "for ind_tree in model.estimators_:\n",
    "    n_nodes.append(ind_tree.tree_.node_count)\n",
    "    max_depths.append(ind_tree.tree_.max_depth)\n",
    "    \n",
    "print(f'Average number of nodes {int(np.mean(n_nodes))}')\n",
    "print(f'Average maximum depth {int(np.mean(max_depths))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Evaluation methods adapted from:\n",
    "# https://github.com/WillKoehrsen/Machine-Learning-Projects/blob/master/Random%20Forest%20Tutorial.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Evaluation\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\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": 15,
   "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_tests, [1 for _ in range(len(y_tests))])\n",
    "    baseline['precision'] = precision_score(y_tests, [1 for _ in range(len(y_tests))])\n",
    "    baseline['roc'] = 0.5\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    results['recall'] = recall_score(y_tests, predictions)\n",
    "    results['precision'] = precision_score(y_tests, predictions)\n",
    "    results['roc'] = roc_auc_score(y_tests, 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_tests, [1 for _ in range(len(y_tests))])\n",
    "    model_fpr, model_tpr, _ = roc_curve(y_tests, 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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>97</th>\n",
       "      <td>ABHD15</td>\n",
       "      <td>0.045714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21417</th>\n",
       "      <td>TIMM13</td>\n",
       "      <td>0.043389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18420</th>\n",
       "      <td>SCARNA20|ENSG00000251818.1</td>\n",
       "      <td>0.040420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22866</th>\n",
       "      <td>UNC119</td>\n",
       "      <td>0.036979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>AMH</td>\n",
       "      <td>0.034165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5069</th>\n",
       "      <td>EBI3</td>\n",
       "      <td>0.033721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24230</th>\n",
       "      <td>ZNRF4</td>\n",
       "      <td>0.031988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>CCT6B</td>\n",
       "      <td>0.031720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17599</th>\n",
       "      <td>RNA5SP438</td>\n",
       "      <td>0.029827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15328</th>\n",
       "      <td>PSMD3</td>\n",
       "      <td>0.029080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          feature  importance\n",
       "97                         ABHD15    0.045714\n",
       "21417                      TIMM13    0.043389\n",
       "18420  SCARNA20|ENSG00000251818.1    0.040420\n",
       "22866                      UNC119    0.036979\n",
       "632                           AMH    0.034165\n",
       "5069                         EBI3    0.033721\n",
       "24230                       ZNRF4    0.031988\n",
       "2915                        CCT6B    0.031720\n",
       "17599                   RNA5SP438    0.029827\n",
       "15328                       PSMD3    0.029080"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The most important features \n",
    "fi_model = pd.DataFrame({'feature': features,\n",
    "                   'importance': model.feature_importances_}).\\\n",
    "                    sort_values('importance', ascending = False)\n",
    "fi_model.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#results from the model \n",
    "train_rf_predictions = model.predict(X_train)\n",
    "train_rf_probs       = model.predict_proba(X_train)[:, 1]\n",
    "\n",
    "rf_predictions = model.predict(X_test)\n",
    "rf_probs       = model.predict_proba(X_test)[:, 1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Baseline: 1.0 Test: 0.93 Train: 1.0\n",
      "Precision Baseline: 0.64 Test: 0.96 Train: 1.0\n",
      "Roc Baseline: 0.5 Test: 0.97 Train: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\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": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[185  13]\n",
      " [ 25 325]]\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_tests, rf_predictions)\n",
    "plot_confusion_matrix(cm, classes = ['No BRCA', 'BRCA'],\n",
    "                      title = 'Cancer Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9215954728002922\n"
     ]
    }
   ],
   "source": [
    "#Scoring with kfold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(model,df,y_red,cv=10,scoring='accuracy')\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}