From 0f5684dc6271faca337a5afd0bfd253bd34af41d Mon Sep 17 00:00:00 2001
From: aakan96 <aakan96@mi.fu-berlin.de>
Date: Fri, 14 Jul 2023 21:55:54 +0000
Subject: [PATCH] =?UTF-8?q?L=C3=B6sche=20DS=5FmiRNA=5Flimma=5Fdataset=5Fxg?=
=?UTF-8?q?b=5Ffinal.ipynb?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
.../DS_miRNA_limma_dataset_xgb_final.ipynb | 1133 -----------------
1 file changed, 1133 deletions(-)
delete mode 100644 Machine Learning/DS_miRNA_limma_dataset_xgb_final.ipynb
diff --git a/Machine Learning/DS_miRNA_limma_dataset_xgb_final.ipynb b/Machine Learning/DS_miRNA_limma_dataset_xgb_final.ipynb
deleted file mode 100644
index 2ce7432..0000000
--- a/Machine Learning/DS_miRNA_limma_dataset_xgb_final.ipynb
+++ /dev/null
@@ -1,1133 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 245,
- "id": "f097ad55",
- "metadata": {},
- "outputs": [],
- "source": [
- "import warnings\n",
- "warnings.filterwarnings('ignore')\n",
- "import pandas as pd\n",
- "from sklearn.model_selection import train_test_split\n",
- "#from sklearn.model_selection import cross_val_score\n",
- "#from sklearn.metrics import accuracy_score\n",
- "#import sklearn.metrics as metrics\n",
- "#from sklearn.metrics import auc\n",
- "from sklearn.metrics import RocCurveDisplay\n",
- "#from sklearn.model_selection import KFold\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "from imblearn.over_sampling import SMOTE\n",
- "from sklearn.linear_model import Lasso\n",
- "import xgboost as xgb\n",
- "from sklearn.model_selection import GridSearchCV\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "\n",
- "#np.random.seed(7)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "73b6611a",
- "metadata": {},
- "source": [
- "# Data Preprocessing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 246,
- "id": "0eeb7a35",
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.read_csv(\"DS/miRNA_DS_preprocessed_data.csv\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 247,
- "id": "6e7836e1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(230, 239)"
- ]
- },
- "execution_count": 247,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 248,
- "id": "683b63ce",
- "metadata": {},
- "outputs": [],
- "source": [
- "df = df.T"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 249,
- "id": "2e78017d",
- "metadata": {},
- "outputs": [],
- "source": [
- "#Transform the input data\n",
- "df.rename(columns=df.iloc[0], inplace = True)\n",
- "df.drop(df.index[0], inplace = True)\n",
- "df=df.reset_index()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 250,
- "id": "4c50c510",
- "metadata": {},
- "outputs": [],
- "source": [
- "metadata = pd.read_csv(\"DS/miRNA_DS_metadata_col_info.csv\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 251,
- "id": "55f4abc3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Unnamed: 0</th>\n",
- " <th>title0</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>GSM1069774</td>\n",
- " <td>tissue type: Cancer tissue</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>GSM1069775</td>\n",
- " <td>tissue type: Cancer tissue</td>\n",
- " </tr>\n",
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- " <th>2</th>\n",
- " <td>GSM1069776</td>\n",
- " <td>tissue type: Cancer tissue</td>\n",
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- " <td>tissue type: Cancer tissue</td>\n",
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- " <th>4</th>\n",
- " <td>GSM1069778</td>\n",
- " <td>tissue type: Adjacent normal tissue</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>233</th>\n",
- " <td>GSM1070007</td>\n",
- " <td>tissue type: Cancer tissue</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>234</th>\n",
- " <td>GSM1070008</td>\n",
- " <td>tissue type: Adjacent normal tissue</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>235</th>\n",
- " <td>GSM1070009</td>\n",
- " <td>tissue type: Adjacent normal tissue</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>236</th>\n",
- " <td>GSM1070010</td>\n",
- " <td>tissue type: Adjacent normal tissue</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>237</th>\n",
- " <td>GSM1070011</td>\n",
- " <td>tissue type: Adjacent normal tissue</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>238 rows × 2 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " Unnamed: 0 title0\n",
- "0 GSM1069774 tissue type: Cancer tissue\n",
- "1 GSM1069775 tissue type: Cancer tissue\n",
- "2 GSM1069776 tissue type: Cancer tissue\n",
- "3 GSM1069777 tissue type: Cancer tissue\n",
- "4 GSM1069778 tissue type: Adjacent normal tissue\n",
- ".. ... ...\n",
- "233 GSM1070007 tissue type: Cancer tissue\n",
- "234 GSM1070008 tissue type: Adjacent normal tissue\n",
- "235 GSM1070009 tissue type: Adjacent normal tissue\n",
- "236 GSM1070010 tissue type: Adjacent normal tissue\n",
- "237 GSM1070011 tissue type: Adjacent normal tissue\n",
- "\n",
- "[238 rows x 2 columns]"
- ]
- },
- "execution_count": 251,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metadata"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 252,
- "id": "6730cf89",
- "metadata": {},
- "outputs": [],
- "source": [
- "df= df.merge(metadata, left_on=\"index\", right_on= \"Unnamed: 0\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 253,
- "id": "7a8ad8ad",
- "metadata": {},
- "outputs": [],
- "source": [
- "df['title0'] = df['title0'].replace('(?i)mucosa|normal|healthy', 0, regex=True)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 254,
- "id": "a8cf8643",
- "metadata": {},
- "outputs": [],
- "source": [
- "df['title0'] = df['title0'].replace('(?i)Tumor|Cancer|carcinoma', 1, regex=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 255,
- "id": "5c852a3f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "title0\n",
- "1 119\n",
- "0 119\n",
- "Name: count, dtype: int64"
- ]
- },
- "execution_count": 255,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df['title0'].value_counts()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 256,
- "id": "f5d203aa",
- "metadata": {},
- "outputs": [],
- "source": [
- "df = df[pd.to_numeric(df['title0'], errors='coerce').notnull()]#remove all non-numeric data from the column."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 257,
- "id": "523bdaa6",
- "metadata": {},
- "outputs": [],
- "source": [
- "df= df.drop(['index', 'Unnamed: 0'], axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 258,
- "id": "46a6fb36",
- "metadata": {},
- "outputs": [],
- "source": [
- "df= df.rename(columns={\"title0\": \"index\"})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 259,
- "id": "e26f88c5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "index\n",
- "1 119\n",
- "0 119\n",
- "Name: count, dtype: int64"
- ]
- },
- "execution_count": 259,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df['index'].value_counts()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 260,
- "id": "fbaf2507",
- "metadata": {},
- "outputs": [],
- "source": [
- "df= df.apply(pd.to_numeric)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 261,
- "id": "f3f7adb5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "index\n",
- "1 119\n",
- "0 119\n",
- "Name: count, dtype: int64"
- ]
- },
- "execution_count": 261,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df['index'].value_counts()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 262,
- "id": "6a50f416",
- "metadata": {},
- "outputs": [],
- "source": [
- "X=df.drop(\"index\",axis=1)\n",
- "y=df['index']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 263,
- "id": "e644ab0e",
- "metadata": {},
- "outputs": [],
- "source": [
- "y=y.astype('int')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6cee6462",
- "metadata": {},
- "source": [
- "# Test train split"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 264,
- "id": "1da48142",
- "metadata": {},
- "outputs": [],
- "source": [
- "# split data into training and testing data-sets\n",
- "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=7)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 265,
- "id": "129430e6",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(index\n",
- " 1 61\n",
- " 0 58\n",
- " Name: count, dtype: int64,\n",
- " index\n",
- " 0 61\n",
- " 1 58\n",
- " Name: count, dtype: int64)"
- ]
- },
- "execution_count": 265,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "y_test.value_counts(),y_train.value_counts()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1cfe2a06",
- "metadata": {},
- "source": [
- "# Cross validation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 266,
- "id": "d3550b5e",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n",
- "[CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.958 total time= 0.2s\n",
- "[CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.958 total time= 0.1s\n",
- "[CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.958 total time= 0.1s\n",
- "[CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=1.000 total time= 0.1s\n",
- "[CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=1.000 total time= 0.1s\n",
- "[CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.917 total time= 0.1s\n",
- "[CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.958 total time= 0.1s\n",
- "[CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.958 total time= 0.1s\n",
- "[CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=1.000 total time= 0.2s\n",
- "[CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=1.000 total time= 0.2s\n",
- "[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.917 total time= 0.2s\n",
- "[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.958 total time= 0.1s\n",
- "[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.958 total time= 0.2s\n",
- "[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.958 total time= 0.1s\n",
- "[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.957 total time= 0.1s\n"
- ]
- },
- {
- "data": {
- "text/html": [
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- " callbacks=None, colsample_bylevel=None,\n",
- " colsample_bynode=None,\n",
- " colsample_bytree=None,\n",
- " early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None,\n",
- " feature_types=None, gamma=None,\n",
- " gpu_id=None, grow_policy=None,\n",
- " importance_type=None,\n",
- " interaction_constraints=None,\n",
- " learning_rate=None, max_bin=None,\n",
- " max_cat_threshold=None,\n",
- " max_cat_to_onehot=None,\n",
- " max_delta_step=None, max_depth=None,\n",
- " max_leaves=None, min_child_weight=None,\n",
- " missing=nan, monotone_constraints=None,\n",
- " n_estimators=100, n_jobs=None,\n",
- " num_parallel_tree=None, predictor=None,\n",
- " random_state=42, ...),\n",
- " param_grid={'learning_rate': [0.1, 0.01, 0.001], 'max_depth': [3],\n",
- " 'n_estimators': [100]},\n",
- " verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
- " callbacks=None, colsample_bylevel=None,\n",
- " colsample_bynode=None,\n",
- " colsample_bytree=None,\n",
- " early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None,\n",
- " feature_types=None, gamma=None,\n",
- " gpu_id=None, grow_policy=None,\n",
- " importance_type=None,\n",
- " interaction_constraints=None,\n",
- " learning_rate=None, max_bin=None,\n",
- " max_cat_threshold=None,\n",
- " max_cat_to_onehot=None,\n",
- " max_delta_step=None, max_depth=None,\n",
- " max_leaves=None, min_child_weight=None,\n",
- " missing=nan, monotone_constraints=None,\n",
- " n_estimators=100, n_jobs=None,\n",
- " num_parallel_tree=None, predictor=None,\n",
- " random_state=42, ...),\n",
- " param_grid={'learning_rate': [0.1, 0.01, 0.001], 'max_depth': [3],\n",
- " 'n_estimators': [100]},\n",
- " verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
- " colsample_bylevel=None, colsample_bynode=None,\n",
- " colsample_bytree=None, early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None, feature_types=None,\n",
- " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
- " interaction_constraints=None, learning_rate=None, max_bin=None,\n",
- " max_cat_threshold=None, max_cat_to_onehot=None,\n",
- " max_delta_step=None, max_depth=None, max_leaves=None,\n",
- " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
- " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
- " predictor=None, random_state=42, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
- " colsample_bylevel=None, colsample_bynode=None,\n",
- " colsample_bytree=None, early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None, feature_types=None,\n",
- " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
- " interaction_constraints=None, learning_rate=None, max_bin=None,\n",
- " max_cat_threshold=None, max_cat_to_onehot=None,\n",
- " max_delta_step=None, max_depth=None, max_leaves=None,\n",
- " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
- " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
- " predictor=None, random_state=42, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
- ],
- "text/plain": [
- "GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
- " callbacks=None, colsample_bylevel=None,\n",
- " colsample_bynode=None,\n",
- " colsample_bytree=None,\n",
- " early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None,\n",
- " feature_types=None, gamma=None,\n",
- " gpu_id=None, grow_policy=None,\n",
- " importance_type=None,\n",
- " interaction_constraints=None,\n",
- " learning_rate=None, max_bin=None,\n",
- " max_cat_threshold=None,\n",
- " max_cat_to_onehot=None,\n",
- " max_delta_step=None, max_depth=None,\n",
- " max_leaves=None, min_child_weight=None,\n",
- " missing=nan, monotone_constraints=None,\n",
- " n_estimators=100, n_jobs=None,\n",
- " num_parallel_tree=None, predictor=None,\n",
- " random_state=42, ...),\n",
- " param_grid={'learning_rate': [0.1, 0.01, 0.001], 'max_depth': [3],\n",
- " 'n_estimators': [100]},\n",
- " verbose=3)"
- ]
- },
- "execution_count": 266,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model = xgb.XGBClassifier(random_state=42)\n",
- "\n",
- "# Defining parameter range\n",
- "param_grid = {\n",
- " 'max_depth': [3],\n",
- " 'learning_rate': [0.1 ,0.01, 0.001],\n",
- " 'n_estimators': [100]\n",
- "}\n",
- "\n",
- "\n",
- "grid = GridSearchCV(model, param_grid, refit=True, verbose=3)\n",
- "\n",
- "# Fitting the model for grid search\n",
- "grid.fit(X_train, y_train)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 267,
- "id": "556e249c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100}\n",
- "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
- " colsample_bylevel=None, colsample_bynode=None,\n",
- " colsample_bytree=None, early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None, feature_types=None,\n",
- " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
- " interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
- " max_cat_threshold=None, max_cat_to_onehot=None,\n",
- " max_delta_step=None, max_depth=3, max_leaves=None,\n",
- " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
- " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
- " predictor=None, random_state=42, ...)\n"
- ]
- }
- ],
- "source": [
- "# print best parameter after tuning\n",
- "print(grid.best_params_)\n",
- " \n",
- "# print how our model looks after hyper-parameter tuning\n",
- "print(grid.best_estimator_)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 268,
- "id": "53a7f793",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
- " colsample_bylevel=None, colsample_bynode=None,\n",
- " colsample_bytree=None, early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None, feature_types=None,\n",
- " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
- " interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
- " max_cat_threshold=None, max_cat_to_onehot=None,\n",
- " max_delta_step=None, max_depth=3, max_leaves=None,\n",
- " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
- " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
- " predictor=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" checked><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
- " colsample_bylevel=None, colsample_bynode=None,\n",
- " colsample_bytree=None, early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None, feature_types=None,\n",
- " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
- " interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
- " max_cat_threshold=None, max_cat_to_onehot=None,\n",
- " max_delta_step=None, max_depth=3, max_leaves=None,\n",
- " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
- " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
- " predictor=None, random_state=42, ...)</pre></div></div></div></div></div>"
- ],
- "text/plain": [
- "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
- " colsample_bylevel=None, colsample_bynode=None,\n",
- " colsample_bytree=None, early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None, feature_types=None,\n",
- " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
- " interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
- " max_cat_threshold=None, max_cat_to_onehot=None,\n",
- " max_delta_step=None, max_depth=3, max_leaves=None,\n",
- " min_child_weight=None, missing=nan, monotone_constraints=None,\n",
- " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
- " predictor=None, random_state=42, ...)"
- ]
- },
- "execution_count": 268,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model_xgb = grid.best_estimator_\n",
- "model_xgb.fit(X_train,y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 269,
- "id": "9ed43446",
- "metadata": {},
- "outputs": [],
- "source": [
- "y_proba = model_xgb.fit(X_train, y_train).predict_proba(X_test)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 270,
- "id": "c0193b78",
- "metadata": {},
- "outputs": [],
- "source": [
- "classes = model_xgb.classes_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 271,
- "id": "d723c69f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0, 1])"
- ]
- },
- "execution_count": 271,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "classes"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7317ba93",
- "metadata": {},
- "source": [
- "# Classification report"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 272,
- "id": "c33739d1",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " precision recall f1-score support\n",
- "\n",
- " 0 0.97 0.98 0.97 58\n",
- " 1 0.98 0.97 0.98 61\n",
- "\n",
- " accuracy 0.97 119\n",
- " macro avg 0.97 0.97 0.97 119\n",
- "weighted avg 0.97 0.97 0.97 119\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from sklearn.metrics import classification_report, confusion_matrix\n",
- "grid_predictions = grid.predict(X_test)\n",
- "print(classification_report(y_test, grid_predictions))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 273,
- "id": "937e8f1b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#######CONFUSION MATRIX ###########\n",
- "from sklearn import metrics\n",
- "y_test_pred_xgb = model_xgb.predict(X_test)\n",
- "confusion_matrix_test = metrics.confusion_matrix(y_test, y_test_pred_xgb)\n",
- "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix_test)\n",
- "cm_display.plot()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 274,
- "id": "e4c5c1d9",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy : 0.9747899159663865\n",
- "Sensitivity : 0.9827586206896551\n",
- "Specificity : 0.9672131147540983\n"
- ]
- }
- ],
- "source": [
- "total1=sum(sum(confusion_matrix_test))\n",
- "#####from confusion matrix calculate accuracy\n",
- "accuracy1=(confusion_matrix_test[0,0]+confusion_matrix_test[1,1])/total1\n",
- "print ('Accuracy : ', accuracy1)\n",
- "\n",
- "sensitivity1 = confusion_matrix_test[0,0]/(confusion_matrix_test[0,0]+confusion_matrix_test[0,1])\n",
- "print('Sensitivity : ', sensitivity1 )\n",
- "\n",
- "specificity1 = confusion_matrix_test[1,1]/(confusion_matrix_test[1,0]+confusion_matrix_test[1,1])\n",
- "print('Specificity : ', specificity1)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6603d82c",
- "metadata": {},
- "source": [
- "# ROC curve"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 275,
- "id": "0e2a2694",
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.model_selection import StratifiedKFold\n",
- "from sklearn.feature_selection import SelectKBest, f_classif\n",
- "from sklearn.metrics import auc\n",
- "def roc(X_train,y_train,model,label):\n",
- " cv = StratifiedKFold(n_splits=6)\n",
- " classifier = model\n",
- " tprs = []\n",
- " aucs = []\n",
- " mean_fpr = np.linspace(0, 1, 100)\n",
- "\n",
- " fig, ax = plt.subplots(figsize=(6, 6))\n",
- " for fold, (train, test) in enumerate(cv.split(X_train, y_train)):\n",
- " classifier.fit(X_train.iloc[train], y_train.iloc[train])\n",
- " viz = RocCurveDisplay.from_estimator(\n",
- " classifier,\n",
- " X_train.iloc[test],\n",
- " y_train.iloc[test],\n",
- " name=f\"ROC fold {fold}\",\n",
- " alpha=0.3,\n",
- " lw=1,\n",
- " ax=ax,\n",
- " )\n",
- " interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)\n",
- " interp_tpr[0] = 0.0\n",
- " tprs.append(interp_tpr)\n",
- " aucs.append(viz.roc_auc)\n",
- " ax.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\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(\n",
- " mean_fpr,\n",
- " mean_tpr,\n",
- " color=\"b\",\n",
- " label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n",
- " lw=2,\n",
- " alpha=0.8,\n",
- " )\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(\n",
- " mean_fpr,\n",
- " tprs_lower,\n",
- " tprs_upper,\n",
- " color=\"grey\",\n",
- " alpha=0.2,\n",
- " label=r\"$\\pm$ 1 std. dev.\",\n",
- " )\n",
- "\n",
- " ax.set(\n",
- " xlim=[-0.05, 1.05],\n",
- " ylim=[-0.05, 1.05],\n",
- " xlabel=\"False Positive Rate\",\n",
- " ylabel=\"True Positive Rate\",\n",
- " title=label,\n",
- " )\n",
- " ax.axis(\"square\")\n",
- " ax.legend(loc=\"lower right\")\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 276,
- "id": "d4cc8e6d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 600x600 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = model_xgb\n",
- "label=\"ROC curve of training data\"\n",
- "roc(X_train,y_train,model,label)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 277,
- "id": "1199e2e4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 600x600 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "label=\"ROC curve of testing data\"\n",
- "roc(X_test,y_test,model,label)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bee03388",
- "metadata": {},
- "source": [
- "# Feature importance"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 240,
- "id": "6688e037",
- "metadata": {},
- "outputs": [],
- "source": [
- "# for important features:\n",
- "important_feat = model_xgb.feature_importances_\n",
- "#get indices of those important features\n",
- "idx = important_feat.argsort(kind= \"quicksort\")\n",
- "idx= idx[::-1][:50]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 241,
- "id": "4e6a7ea1",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 66, 65, 84, 94, 140, 32, 169, 137, 23, 212, 10, 166, 13,\n",
- " 36, 56, 126, 48, 57, 42, 208, 37, 113, 29, 160, 22, 96,\n",
- " 162, 229, 189, 101, 104, 127, 135, 21, 79, 78, 77, 76, 75,\n",
- " 74, 73, 72, 202, 71, 69, 68, 67, 64, 63, 62])"
- ]
- },
- "execution_count": 241,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "idx"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 242,
- "id": "f2101fe1",
- "metadata": {},
- "outputs": [],
- "source": [
- "df1 = X.T"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 243,
- "id": "2cbf1166",
- "metadata": {},
- "outputs": [],
- "source": [
- "top_met = df1.iloc[idx]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 244,
- "id": "2370b2df",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['hsa-miR-18b-5p', 'hsa-miR-18a-5p', 'hsa-miR-21-5p', 'hsa-miR-25-3p',\n",
- " 'hsa-miR-424-5p', 'hsa-miR-130b-3p', 'hsa-miR-455-3p', 'hsa-miR-378i',\n",
- " 'hsa-miR-1268a', 'hsa-miR-93-5p', 'hsa-miR-106b-5p', 'hsa-miR-451a',\n",
- " 'hsa-miR-10b-5p', 'hsa-miR-140-3p', 'hsa-miR-15b-5p', 'hsa-miR-3651',\n",
- " 'hsa-miR-150-5p', 'hsa-miR-16-2-3p', 'hsa-miR-145-5p', 'hsa-miR-7-5p',\n",
- " 'hsa-miR-140-5p', 'hsa-miR-3198', 'hsa-miR-1290', 'hsa-miR-4465',\n",
- " 'hsa-miR-126-3p', 'hsa-miR-26b-5p', 'hsa-miR-4497', 'mr_1',\n",
- " 'hsa-miR-497-5p', 'hsa-miR-29c-3p', 'hsa-miR-30a-5p', 'hsa-miR-3656',\n",
- " 'hsa-miR-378a-3p', 'hsa-miR-125b-5p', 'hsa-miR-200c-3p',\n",
- " 'hsa-miR-200b-3p', 'hsa-miR-19b-3p', 'hsa-miR-19a-3p',\n",
- " 'hsa-miR-199a-5p', 'hsa-miR-199a-3p', 'hsa-miR-1973', 'hsa-miR-197-5p',\n",
- " 'hsa-miR-642a-3p', 'hsa-miR-197-3p', 'hsa-miR-193b-3p',\n",
- " 'hsa-miR-193a-5p', 'hsa-miR-1915-3p', 'hsa-miR-188-5p',\n",
- " 'hsa-miR-185-5p', 'hsa-miR-181b-5p'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 244,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "top_met.index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c9d668aa",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "03b3840d",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6eddc5ba",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c5d1c810",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "677c2598",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c392229",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d829a8f3",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "68c4a3d7",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "50880573",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7c91af09",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "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": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--
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