4.2.xgboostPruned.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",
    "import xgboost as xgb \n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score, recall_score, roc_auc_score, roc_curve\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "#https://www.datacamp.com/community/tutorials/xgboost-in-python \n",
    "#tutorial above to help create the model "
   ]
  },
  {
   "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",
    "X = df.drop(['BRCA'], axis = 1)\n",
    "y = 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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [16:50<00:00, 35.20s/trial, best loss: 0.227029]           \n"
     ]
    }
   ],
   "source": [
    "import hyperopt as hp \n",
    "from hyperopt import Trials,fmin,STATUS_OK\n",
    "\n",
    "\n",
    "### Step 1 : defining the objective function\n",
    "def objective(params, n_folds=3):\n",
    "\n",
    "# Converting pandas dataframe into xgboost format\n",
    "    d_train = xgb.DMatrix(data=X, label=y)  \n",
    "\n",
    "# Running cross validation on your xgboost model\n",
    "    cv_results = xgb.cv(params, d_train, nfold = n_folds, num_boost_round = 500, \n",
    "                        early_stopping_rounds = 25, metrics = 'rmse', seed = 0)\n",
    "  \n",
    "# returns the loss on validation set\n",
    "    loss = min(cv_results['test-rmse-mean'])\n",
    "    return loss\n",
    "\n",
    "\n",
    "### step 2 : defining the search space\n",
    "xgb_space = {\n",
    "#    max_depth : maximum depth allowed for every tree\n",
    "# hp.choice.choice will select 1 value from the given list\n",
    "    'max_depth':hp.hp.choice('max_depth', np.arange(6, 14,1, dtype=int)),\n",
    "#    subsample : maximum allowed rows for every tree\n",
    "    'subsample':hp.hp.quniform('subsample',0.5,1.0,0.05),\n",
    "#    colsample_bytree : maximum allowed features for every tree\n",
    "# hp.hp.quniform returns a float between a given range\n",
    "    'colsample_bytree':hp.hp.quniform('colsample_bytree',0.5,1.0,0.05),\n",
    "#    min_child-weight : minimum number of instances required in each node\n",
    "    'min_child_weight':hp.hp.quniform('min_child_weight', 100, 1000,100),\n",
    "#    reg_alpha : L1 regularisation term on weights\n",
    "    'reg_alpha': hp.hp.uniform('reg_alpha', 0.0, 1.0),\n",
    "#    reg_lambda : L2 regularisation term on weights\n",
    "    'reg_lambda': hp.hp.uniform('reg_lambda', 0.0, 1.0)}\n",
    "    \n",
    "    \n",
    "### step 3 : storing the results of every iteration    \n",
    "bayes_trials = Trials()\n",
    "MAX_EVALS = 20\n",
    "\n",
    "# Optimize\n",
    "best = fmin(fn = objective, space = xgb_space, algo = hp.tpe.suggest, \n",
    "max_evals = MAX_EVALS, trials = bayes_trials)\n",
    "\n",
    "### best will the return the the best hyperparameter set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'colsample_bytree': 0.55, 'max_depth': 2, 'min_child_weight': 100.0, 'reg_alpha': 0.07675346261208471, 'reg_lambda': 0.8148727138018221, 'subsample': 0.9}\n"
     ]
    }
   ],
   "source": [
    "print(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating the Classification model, running \n",
    "xg_class = xgb.XGBClassifier(colsample_bytree = 0.55, max_depth = 2, min_child_weight = 100, reg_alpha = 0.07675346261208471, reg_lambda = 0.8148727138018221, subsample = 0.9  )\n",
    "xg_class.fit(X_train,y_train)\n",
    "\n",
    "train_xg_predictions = xg_class.predict(X_train)\n",
    "train_xg_probs       = xg_class.predict_proba(X_train)[:, 1]\n",
    "\n",
    "xg_predictions = xg_class.predict(X_test)\n",
    "xg_probs       = xg_class.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9860825118656443\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "y_red = pd.Series(y)\n",
    "scores = cross_val_score(xg_class,df,y_red,cv=10,scoring='accuracy')\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "plot_importance(xg_class)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[150  49]\n",
      " [ 16 333]]\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, xg_predictions)\n",
    "plot_confusion_matrix(cm, classes = ['No BRCA', 'BRCA'],\n",
    "                      title = 'Cancer Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Baseline: 1.0 Test: 0.95 Train: 0.93\n",
      "Precision Baseline: 0.64 Test: 0.87 Train: 0.88\n",
      "Roc Baseline: 0.5 Test: 0.94 Train: 0.93\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(xg_predictions, xg_probs, train_xg_predictions, train_xg_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 88.14%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_tests, xg_predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  }
 ],
 "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
}