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{
"cells": [
{
"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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