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 "cells": [

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   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h1 align=\"center\">Machine learning-based prediction of early recurrence in glioblastoma patients: a glance towards precision medicine <br><br>[Null-classifier]</h1>"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h2>[1] Library</h2>"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {

    "collapsed": true

   },

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "/Users/valerio_mc/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",

      "  warnings.warn(message, FutureWarning)\n",

      "/Users/valerio_mc/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.bagging module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",

      "  warnings.warn(message, FutureWarning)\n",

      "/Users/valerio_mc/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",

      "  warnings.warn(message, FutureWarning)\n",

      "/Users/valerio_mc/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.forest module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",

      "  warnings.warn(message, FutureWarning)\n",

      "/Users/valerio_mc/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",

      "  warnings.warn(message, FutureWarning)\n",

      "/Users/valerio_mc/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",

      "  warnings.warn(message, FutureWarning)\n"

     ]

    }

   ],

   "source": [

    "# OS library\n",

    "import os\n",

    "import sys\n",

    "import argparse\n",

    "import itertools\n",

    "import random\n",

    "\n",

    "# Analysis\n",

    "import numpy as np\n",

    "import pandas as pd\n",

    "import seaborn as sns\n",

    "import matplotlib.pyplot as plt\n",

    "\n",

    "# Sklearn\n",

    "from boruta import BorutaPy\n",

    "from sklearn.preprocessing import LabelEncoder\n",

    "from sklearn.model_selection import train_test_split\n",

    "from sklearn.ensemble import RandomForestClassifier\n",

    "from sklearn.metrics import confusion_matrix, f1_score, recall_score, classification_report, accuracy_score, auc, roc_curve\n",

    "from sklearn.model_selection import RandomizedSearchCV\n",

    "from sklearn.dummy import DummyClassifier\n",

    "\n",

    "import scikitplot as skplt\n",

    "from imblearn.over_sampling import RandomOverSampler, SMOTENC, SMOTE"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h2>[2] Exploratory data analysis and Data Preprocessing</h2>"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h4>[-] Load the database</h4>"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "file = os.path.join(sys.path[0], \"db.xlsx\")\n",

    "db = pd.read_excel(file)\n",

    "\n",

    "print(\"N° of patients: {}\".format(len(db)))\n",

    "print(\"N° of columns: {}\".format(db.shape[1]))\n",

    "db.head()"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h4>[-] Drop unwanted columns + create <i>'results'</i> column</h4>"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "df = db.drop(['Name_Surname','...'], axis = 'columns')\n",

    "\n",

    "print(\"Effective features to consider: {} \".format(len(df.columns)-1))\n",

    "print(\"Creating 'result' column...\")\n",

    "\n",

    "# 0 = No relapse\n",

    "df.loc[df['PFS'] > 6, 'result'] = 0\n",

    "\n",

    "# 1 = Early relapse (within 6 months)\n",

    "df.loc[df['PFS'] <= 6, 'result'] = 1\n",

    "\n",

    "df.head()"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h4>[-] Check for class imbalance in the <i>'results'</i> column </h4>"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "print(\"PFS Overview\")\n",

    "print(df.result.value_counts())\n",

    "\n",

    "df.result.value_counts().plot(kind='pie', autopct='%1.0f%%', colors=['skyblue', 'orange'], explode=(0.05, 0.05))"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h4>[-] Label encoding of the categorical variables </h4>"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "df['sex'] =df['sex'].astype('category')\n",

    "df['ceus'] =df['ceus'].astype('category')\n",

    "df['ala'] =df['ala'].astype('category')\n",

    "\n",

    "#df['Ki67'] =df['Ki67'].astype(int)\n",

    "df['MGMT'] =df['MGMT'].astype('category')\n",

    "df['IDH1'] =df['IDH1'].astype('category')\n",

    "\n",

    "df['side'] =df['side'].astype('category')\n",

    "df['ependima'] =df['ependima'].astype('category')\n",

    "df['cc'] =df['cc'].astype('category')\n",

    "df['necrotico_cistico'] =df['necrotico_cistico'].astype('category')\n",

    "df['shift'] =df['shift'].astype('category')\n",

    "\n",

    "## VARIABLE TO ONE-HOT-ENCODE\n",

    "df['localization'] =df['localization'].astype(int)\n",

    "df['clinica_esordio'] =df['clinica_esordio'].astype(int)\n",

    "df['immediate_p_o'] =df['immediate_p_o'].astype(int)\n",

    "df['onco_Protocol'] =df['onco_Protocol'].astype(int)\n",

    "\n",

    "df['result'] =df['result'].astype(int)\n",

    "\n",

    "dummy_v = ['localization', 'clinica_esordio', 'onco_Protocol', 'immediate_p_o']\n",

    "\n",

    "df = pd.get_dummies(df, columns = dummy_v, prefix = dummy_v)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h2>[3] Prediction Models</h2>"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h4> [-] Training and testing set splitting</h4>"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "metadata": {

    "collapsed": true

   },

   "outputs": [

    {

     "ename": "NameError",

     "evalue": "name 'df' is not defined",

     "output_type": "error",

     "traceback": [

      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",

      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",

      "\u001b[0;32m<ipython-input-5-48cdcc32916c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'result'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'result'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PFS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'columns'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",

      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"

     ]

    }

   ],

   "source": [

    "target = df['result']\n",

    "inputs = df.drop(['result', 'PFS'], axis = 'columns')\n",

    "x_train, x_test, y_train, y_test = train_test_split(inputs['...'],target,test_size=0.20, random_state=10)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h4> [-] Dummy Training </h4>"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "dummy_train = DummyClassifier(strategy=\"uniform\", random_state = 42)\n",

    "dummy_train.fit(x_train, y_train)\n",

    "\n",

    "score_dummy_train = dummy_train.score(x_train, y_train)\n",

    "print(\"Dummy Train accuracy ***TRAIN***: \", round(score_dummy_train*100,2), \"% \\n\")\n",

    "\n",

    "y_dummy_train_predicted = dummy_train.predict(x_train)\n",

    "y_dummy_train_proba = dummy_train.predict_proba(x_train)\n",

    "\n",

    "cm_dummy_train = confusion_matrix(y_train, y_dummy_train_predicted)\n",

    "print(cm_dummy_train, \"\\n\")\n",

    "\n",

    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_train, y_dummy_train_predicted)\n",

    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",

    "\n",

    "\n",

    "print('1. The F-1 Score of the model {} \\n '.format(round(f1_score(y_train, y_dummy_train_predicted, average = 'macro'), 2)))\n",

    "print('2. The Recall Score of the model {} \\n '.format(round(recall_score(y_train, y_dummy_train_predicted, average = 'macro'), 2)))\n",

    "print('3. Classification report \\n {} \\n'.format(classification_report(y_train, y_dummy_train_predicted)))\n",

    "print('3. AUC: \\n {} \\n'.format(roc_auc))\n",

    "\n",

    "tn, fp, fn, tp = cm_dummy_train.ravel()\n",

    "\n",

    "# Sensitivity, hit rate, Recall, or true positive rate\n",

    "tpr = tp/(tp+fn)\n",

    "print(\"Sensitivity (TPR): {}\".format(tpr))\n",

    "\n",

    "# Specificity or true negative rate\n",

    "tnr = tn/(tn+fp)\n",

    "print(\"Specificity (TNR): {}\".format(tnr))\n",

    "\n",

    "# Precision or positive predictive value\n",

    "ppv = tp/(tp+fp)\n",

    "print(\"Precision (PPV): {}\".format(ppv))\n",

    "\n",

    "# Negative predictive value\n",

    "npv = tn/(tn+fn)\n",

    "print(\"Negative Predictive Value (NPV): {}\".format(npv))\n",

    "\n",

    "# False positive rate\n",

    "fpr = fp / (fp + tn)\n",

    "print(\"False Positive Rate (FPV): {}\".format(fpr))\n",

    "\n",

    "tnr = tn/(tn+fp)\n"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<h4> [-] Dummy Testing </h4>"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "dummy_testing = DummyClassifier(strategy=\"uniform\", random_state = 42)\n",

    "dummy_testing.fit(x_test, y_test)\n",

    "\n",

    "score_dummy_testing = dummy_testing.score(x_test, y_test)\n",

    "print(\"Dummy Test accuracy ***TEST***: \", round(score_dummy_testing*100,2), \"% \\n\")\n",

    "\n",

    "y_dummy_testing_predicted = dummy_testing.predict(x_test)\n",

    "y_dummy_testing_proba = dummy_testing.predict_proba(x_test)\n",

    "\n",

    "cm_dummy_testing = confusion_matrix(y_test, y_dummy_testing_predicted)\n",

    "print(cm_dummy_testing, \"\\n\")\n",

    "\n",

    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_dummy_testing_predicted)\n",

    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",

    "\n",

    "\n",

    "print('1. The F-1 Score of the model {} \\n '.format(round(f1_score(y_test, y_dummy_testing_predicted, average = 'macro'), 2)))\n",

    "print('2. The Recall Score of the model {} \\n '.format(round(recall_score(y_test, y_dummy_testing_predicted, average = 'macro'), 2)))\n",

    "print('3. Classification report \\n {} \\n'.format(classification_report(y_test, y_dummy_testing_predicted)))\n",

    "print('3. AUC: \\n {} \\n'.format(roc_auc))\n",

    "\n",

    "tn, fp, fn, tp = cm_dummy_train.ravel()\n",

    "\n",

    "# Sensitivity, hit rate, Recall, or true positive rate\n",

    "tpr = tp/(tp+fn)\n",

    "print(\"Sensitivity (TPR): {}\".format(tpr))\n",

    "\n",

    "# Specificity or true negative rate\n",

    "tnr = tn/(tn+fp)\n",

    "print(\"Specificity (TNR): {}\".format(tnr))\n",

    "\n",

    "# Precision or positive predictive value\n",

    "ppv = tp/(tp+fp)\n",

    "print(\"Precision (PPV): {}\".format(ppv))\n",

    "\n",

    "# Negative predictive value\n",

    "npv = tn/(tn+fn)\n",

    "print(\"Negative Predictive Value (NPV): {}\".format(npv))\n",

    "\n",

    "# False positive rate\n",

    "fpr = fp / (fp + tn)\n",

    "print(\"False Positive Rate (FPV): {}\".format(fpr))\n",

    "\n",

    "tnr = tn/(tn+fp)"

   ]

  }

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