Training_MetaDegron_XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os,re,sys,random\n",
    "from random import randint\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import  precision_recall_fscore_support\n",
    "from scipy import interp\n",
    "from sklearn.utils import resample, shuffle\n",
    "from matplotlib import pyplot as plt\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state=np.random.RandomState(seed=527)\n",
    "best_params_ = {'subsample': 0.7526315789473684, 'n_estimators': 380, 'min_child_weight': 3, 'max_depth': 40, 'learning_rate': 0.01, 'gamma': 0.1, 'colsample_bytree': 0.6599999999999999}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_cv_roc_and_pr_curve_xgboost(X,y,clf,random_state):\n",
    "    \n",
    "    # Run classifier with cross-validation and plot ROC curves\n",
    "    fig, ax = plt.subplots(figsize=(5,5))\n",
    "    classifier = clf\n",
    "    tprs = []\n",
    "    aucs = []\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "    \n",
    "    cv = StratifiedKFold(n_splits=5,random_state=random_state,shuffle=True)\n",
    "    \n",
    "    i = 0\n",
    "    for train, test in cv.split(X, y):\n",
    "        clf= classifier.fit(X[train], y[train])\n",
    "        probas_ = clf.predict_proba(X[test])\n",
    "        fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])\n",
    "        tprs.append(interp(mean_fpr, fpr, tpr))\n",
    "        tprs[-1][0] = 0.0\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        aucs.append(roc_auc)\n",
    "        plt.plot(fpr, tpr, lw=1.5, alpha=0.5,  label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n",
    "        \n",
    "        i = i + 1\n",
    "        \n",
    "    ax.plot([0, 1], [0, 1], linestyle='--', lw=3.5, color='r',\n",
    "             label='Luck', alpha=.8)\n",
    "\n",
    "    mean_tpr = np.mean(tprs, axis=0)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "    std_auc = np.std(aucs)\n",
    "    ax.plot(mean_fpr, mean_tpr, color='b',\n",
    "             label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n",
    "             lw=3.5, alpha=.8)\n",
    "    \n",
    "    std_tpr = np.std(tprs, axis=0)\n",
    "    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "    ax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n",
    "                     label=r'$\\pm$ 1 std. dev.')\n",
    "\n",
    "    ax.set_xlim([-0.05, 1.05])\n",
    "    ax.set_ylim([-0.05, 1.05])\n",
    "    ax.set_xlabel('False Positive Rate (FPR)',fontsize=12)\n",
    "    ax.set_ylabel('True Positive Rate (TPR)',fontsize=12)\n",
    "    plt.legend(loc=\"lower right\",fontsize=10)\n",
    "    plt.xticks(fontsize=8)\n",
    "    plt.yticks(fontsize=8)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/public/home/hxu6/anaconda3/envs/tens37/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "/public/home/hxu6/anaconda3/envs/tens37/lib/python3.7/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "feature_name =[\"ASA_SCORE\",\"DSS_SCORE\",\"FCONS_SCORE\",\"COIL\",\"ANCHOR_SCORE\",\"nflanking_ptms\",\"nflanking_ub_lysines\",\"HELIX\",\"RIG_SCORE\",\"Domain_pfam\"]\n",
    "lable_identifier = \"IS_DEGRON\"\n",
    "\n",
    "path_base =\"./data\" \n",
    "\n",
    "path_features = os.path.join(path_base,\"annotated_degrons_properties.tsv\")\n",
    "annotated_degron = pd.read_csv(path_features,sep=\"\\t\")\n",
    "positives = annotated_degron[feature_name]                    \n",
    "positives[lable_identifier] = 1.0\n",
    "\n",
    "path_negative_pep = os.path.join(path_base,\"simulated_degrons.tsv.gz\") \n",
    "negative_pep = pd.read_csv(path_negative_pep,sep=\"\\t\",compression=\"gzip\")\n",
    "negative_pep = negative_pep[negative_pep[\"CONS_SCORE\"]>0]\n",
    "negative_pep=negative_pep.dropna(axis=0)\n",
    "N = positives.shape[0]\n",
    "\n",
    "negatives_random = negative_pep.sample(n=5*len(positives), replace=True, random_state=2024509)\n",
    "\n",
    "negatives = negatives_random[feature_name]                    \n",
    "negatives[lable_identifier] = 0.0\n",
    "training_data = pd.concat([positives,negatives])\n",
    "training_data.dropna(how=\"any\",axis=0,inplace=True)\n",
    "X = training_data[feature_name].values\n",
    "y = training_data[lable_identifier].values\n",
    "X_train, y_train = shuffle(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/public/home/hxu6/anaconda3/envs/tens37/lib/python3.7/site-packages/ipykernel_launcher.py:17: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n",
      "/public/home/hxu6/anaconda3/envs/tens37/lib/python3.7/site-packages/ipykernel_launcher.py:17: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n",
      "/public/home/hxu6/anaconda3/envs/tens37/lib/python3.7/site-packages/ipykernel_launcher.py:17: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n",
      "/public/home/hxu6/anaconda3/envs/tens37/lib/python3.7/site-packages/ipykernel_launcher.py:17: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n",
      "/public/home/hxu6/anaconda3/envs/tens37/lib/python3.7/site-packages/ipykernel_launcher.py:17: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_random = xgb.XGBClassifier(**best_params_)\n",
    "draw_cv_roc_and_pr_curve_xgboost(X_train,y_train,clf_random,random_state)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tens37",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}