{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "colab_experiments.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "WWUwwNqwu9zS",
"colab_type": "text"
},
"source": [
"## Import Data and Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "aux7Xdpuu485",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 632
},
"outputId": "00434730-c6a9-43c0-eb5a-5c8904740f0f"
},
"source": [
"!git clone https://github.com/laura-health/cbms2020/\n",
"!pip install catboost\n",
"!pip install lightgbm\n",
"!pip install missingpy"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'cbms2020'...\n",
"remote: Enumerating objects: 9, done.\u001b[K\n",
"remote: Counting objects: 100% (9/9), done.\u001b[K\n",
"remote: Compressing objects: 100% (8/8), done.\u001b[K\n",
"remote: Total 9 (delta 2), reused 4 (delta 0), pack-reused 0\u001b[K\n",
"Unpacking objects: 100% (9/9), done.\n",
"Collecting catboost\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/94/ec/12b9a42b2ea7dfe5b602f235692ab2b61ee1334ff34334a15902272869e8/catboost-0.22-cp36-none-manylinux1_x86_64.whl (64.4MB)\n",
"\u001b[K |████████████████████████████████| 64.4MB 41kB/s \n",
"\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.1.3)\n",
"Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (0.25.3)\n",
"Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.17.5)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.12.0)\n",
"Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
"Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.1.0)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.6)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.6.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (45.1.0)\n",
"Installing collected packages: catboost\n",
"Successfully installed catboost-0.22\n",
"Requirement already satisfied: lightgbm in /usr/local/lib/python3.6/dist-packages (2.2.3)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.17.5)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.4.1)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from lightgbm) (0.22.1)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->lightgbm) (0.14.1)\n",
"Collecting missingpy\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/b5/be/998d04d27054b58f0974b5f09f8457778a0a72d4355e0b7ae877b6cfb850/missingpy-0.2.0-py3-none-any.whl (49kB)\n",
"\u001b[K |████████████████████████████████| 51kB 4.0MB/s \n",
"\u001b[?25hInstalling collected packages: missingpy\n",
"Successfully installed missingpy-0.2.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VYqbGMy6vT5d",
"colab_type": "text"
},
"source": [
"## Load Data and Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cJeOV93kvQoN",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from catboost import CatBoostClassifier\n",
"from missingpy import MissForest\n",
"from xgboost import XGBClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"import lightgbm as lgb\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.metrics import roc_auc_score, confusion_matrix, classification_report, f1_score\n",
"import numpy as np\n",
"import time\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yknvEU0ZvhQj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 765
},
"outputId": "ad53857c-1814-4b36-e277-5b0d5a7ec0a8"
},
"source": [
"dataset = pd.read_csv(\"cbms2020/heg_sample_data.csv\") #normalized dataset\n",
"dataset.drop('Unnamed: 0', axis=1, inplace=True)\n",
"dataset.shape, dataset.columns"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((13652, 72), Index(['days_from_entrance', 'age', 'document.sexo', 'UTI',\n",
" 'delta_collect_timestamp_t-t1', 'delta_collect_timestamp_t1-t2',\n",
" 'delta_collect_timestamp_t2-t3', 'delta_collect_timestamp_t3-t4',\n",
" 'document.freq_cardiaca(t)', 'document.freq_cardiaca(t-1)',\n",
" 'document.freq_cardiaca(t-2)', 'document.freq_cardiaca(t-3)',\n",
" 'document.freq_cardiaca(t-4)', 'document.freq_respiratoria(t)',\n",
" 'document.freq_respiratoria(t-1)', 'document.freq_respiratoria(t-2)',\n",
" 'document.freq_respiratoria(t-3)', 'document.freq_respiratoria(t-4)',\n",
" 'document.glicemia_capilar(t)', 'document.glicemia_capilar(t-1)',\n",
" 'document.glicemia_capilar(t-2)', 'document.glicemia_capilar(t-3)',\n",
" 'document.glicemia_capilar(t-4)', 'document.pa_diastolica(t)',\n",
" 'document.pa_diastolica(t-1)', 'document.pa_diastolica(t-2)',\n",
" 'document.pa_diastolica(t-3)', 'document.pa_diastolica(t-4)',\n",
" 'document.pa_sistolica(t)', 'document.pa_sistolica(t-1)',\n",
" 'document.pa_sistolica(t-2)', 'document.pa_sistolica(t-3)',\n",
" 'document.pa_sistolica(t-4)', 'document.sat_o2(t)',\n",
" 'document.sat_o2(t-1)', 'document.sat_o2(t-2)', 'document.sat_o2(t-3)',\n",
" 'document.sat_o2(t-4)', 'document.temperatura(t)',\n",
" 'document.temperatura(t-1)', 'document.temperatura(t-2)',\n",
" 'document.temperatura(t-3)', 'document.temperatura(t-4)',\n",
" 'delta_document.freq_cardiaca_t-t1',\n",
" 'delta_document.freq_cardiaca_t1-t2',\n",
" 'delta_document.freq_cardiaca_t2-t3',\n",
" 'delta_document.freq_cardiaca_t3-t4',\n",
" 'delta_document.freq_respiratoria_t-t1',\n",
" 'delta_document.freq_respiratoria_t1-t2',\n",
" 'delta_document.freq_respiratoria_t2-t3',\n",
" 'delta_document.freq_respiratoria_t3-t4',\n",
" 'delta_document.glicemia_capilar_t-t1',\n",
" 'delta_document.glicemia_capilar_t1-t2',\n",
" 'delta_document.glicemia_capilar_t2-t3',\n",
" 'delta_document.glicemia_capilar_t3-t4',\n",
" 'delta_document.pa_diastolica_t-t1',\n",
" 'delta_document.pa_diastolica_t1-t2',\n",
" 'delta_document.pa_diastolica_t2-t3',\n",
" 'delta_document.pa_diastolica_t3-t4',\n",
" 'delta_document.pa_sistolica_t-t1', 'delta_document.pa_sistolica_t1-t2',\n",
" 'delta_document.pa_sistolica_t2-t3',\n",
" 'delta_document.pa_sistolica_t3-t4', 'delta_document.sat_o2_t-t1',\n",
" 'delta_document.sat_o2_t1-t2', 'delta_document.sat_o2_t2-t3',\n",
" 'delta_document.sat_o2_t3-t4', 'delta_document.temperatura_t-t1',\n",
" 'delta_document.temperatura_t1-t2', 'delta_document.temperatura_t2-t3',\n",
" 'delta_document.temperatura_t3-t4', 'outcome'],\n",
" dtype='object'))"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DHQSXh9PwEpE",
"colab_type": "text"
},
"source": [
"## Setup Expetiments"
]
},
{
"cell_type": "code",
"metadata": {
"id": "WR0GF2tdvzMk",
"colab_type": "code",
"colab": {}
},
"source": [
"X = dataset.drop([\"outcome\"], axis = 1)\n",
"Y = dataset[\"outcome\"]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lv0hUf3kwSBC",
"colab_type": "code",
"colab": {}
},
"source": [
"kfold = KFold(n_splits=10, random_state=7, shuffle=True)\n",
"\n",
"classifiers = {\n",
" 'XGBoost' : XGBClassifier(learning_rate=0.1, n_estimators=100,random_state=7, tree_method='gpu_hist'),\n",
" 'LogReg': LogisticRegression(solver='liblinear', multi_class='ovr'),\n",
" 'D.Tree': DecisionTreeClassifier(),\n",
" 'RForest': RandomForestClassifier(n_estimators = 50),\n",
" 'CatBoos': CatBoostClassifier(learning_rate=0.1,n_estimators=100,random_state=7,task_type='GPU',verbose = False),\n",
" 'Naive': GaussianNB(),\n",
" 'Light': lgb.LGBMClassifier()\n",
"}"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8aTy1YKMwmom",
"colab_type": "text"
},
"source": [
"## Run Basic Experiments"
]
},
{
"cell_type": "code",
"metadata": {
"id": "jVef0DcCwaYI",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
},
"outputId": "b1f93f80-91ca-42df-ab0c-58d7bd857540"
},
"source": [
"for c in classifiers:\n",
" start = time.time()\n",
" model = classifiers[c]\n",
" scores = cross_val_score(model, X, Y, cv=kfold, scoring='roc_auc')\n",
" scores_f1 = cross_val_score(model, X, Y, cv=kfold, scoring='f1')\n",
" print (c + '\\t', round(scores.mean(),4), '(' + str(round(scores_f1.mean(),4)) + ')', round(time.time() - start,2), 's')"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"XGBoost\t 0.9076 (0.563) 6.44 s\n",
"LogReg\t 0.8783 (0.5129) 11.1 s\n",
"D.Tree\t 0.6973 (0.4705) 16.1 s\n",
"RForest\t 0.8853 (0.5185) 53.33 s\n",
"CatBoos\t 0.9058 (0.567) 84.57 s\n",
"Naive\t 0.7854 (0.456) 0.58 s\n",
"Light\t 0.9054 (0.5877) 27.89 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cG8GCzeNw8vd",
"colab_type": "text"
},
"source": [
"## Cross Validation by Windowing"
]
},
{
"cell_type": "code",
"metadata": {
"id": "IvQq8VhTw9fX",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 802
},
"outputId": "b4a3e4e9-f2b9-4236-be46-1242a42388d8"
},
"source": [
"cols = ['age', 'document.sexo', 'UTI', 'days_from_entrance']\n",
"t_cols = [c for c in dataset.columns if '4)' in c and (not 'time' in c)]\n",
"\n",
"for i in [4,3,2,1,0]:\n",
" \n",
" if i == 4: cols.extend(t_cols)\n",
" if i == 0:\n",
" tN_cols = [c for c in dataset.columns if ('t)' in c or '_t-' in c) and (not 'time' in c)]\n",
" cols.extend(tN_cols)\n",
" else: \n",
" tN_cols = [c for c in dataset.columns if ('t-'+str(i) in c or '_t'+str(i) in c) and (not 'time' in c)]\n",
" cols.extend(tN_cols)\n",
"\n",
" cols = list(set(cols))\n",
" print('Number of Columns:', len(cols), 'Exam(s):', 5-i)\n",
" print(cols)\n",
"\n",
" X_W = dataset[cols]\n",
" Y_W = dataset[\"outcome\"]\n",
"\n",
" for c in classifiers:\n",
" start = time.time()\n",
" model = classifiers[c]\n",
" scores = cross_val_score(model, X_W, Y_W, cv=kfold, scoring='roc_auc')\n",
" print ('\\t' + c + '\\t', round(scores.mean(),4), '(+-' + str(round(scores.std(),4)) + ')', round(time.time() - start,2), 's')"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"Number of Columns: 11 Exam(s): 1\n",
"['document.temperatura(t-4)', 'document.glicemia_capilar(t-4)', 'days_from_entrance', 'UTI', 'document.pa_diastolica(t-4)', 'document.sat_o2(t-4)', 'document.freq_respiratoria(t-4)', 'document.sexo', 'document.freq_cardiaca(t-4)', 'document.pa_sistolica(t-4)', 'age']\n",
"\tXGBoost\t 0.846 (+-0.0073) 2.25 s\n",
"\tLogReg\t 0.8045 (+-0.015) 0.54 s\n",
"\tD.Tree\t 0.6356 (+-0.0154) 0.76 s\n",
"\tRForest\t 0.8079 (+-0.0139) 7.39 s\n",
"\tCatBoos\t 0.8446 (+-0.01) 9.36 s\n",
"\tNaive\t 0.7696 (+-0.0138) 0.12 s\n",
"\tLight\t 0.8408 (+-0.0085) 2.75 s\n",
"Number of Columns: 25 Exam(s): 2\n",
"['document.temperatura(t-4)', 'delta_document.glicemia_capilar_t3-t4', 'UTI', 'document.freq_respiratoria(t-3)', 'delta_document.temperatura_t3-t4', 'age', 'document.sat_o2(t-4)', 'delta_document.freq_respiratoria_t3-t4', 'document.temperatura(t-3)', 'document.glicemia_capilar(t-3)', 'document.freq_cardiaca(t-4)', 'delta_document.sat_o2_t3-t4', 'delta_document.pa_diastolica_t3-t4', 'document.glicemia_capilar(t-4)', 'delta_document.pa_sistolica_t3-t4', 'document.freq_respiratoria(t-4)', 'document.pa_sistolica(t-3)', 'document.freq_cardiaca(t-3)', 'days_from_entrance', 'document.pa_diastolica(t-3)', 'document.pa_diastolica(t-4)', 'document.sat_o2(t-3)', 'document.sexo', 'delta_document.freq_cardiaca_t3-t4', 'document.pa_sistolica(t-4)']\n",
"\tXGBoost\t 0.8587 (+-0.007) 2.91 s\n",
"\tLogReg\t 0.8227 (+-0.0149) 1.23 s\n",
"\tD.Tree\t 0.6531 (+-0.0135) 1.79 s\n",
"\tRForest\t 0.824 (+-0.0125) 12.59 s\n",
"\tCatBoos\t 0.8575 (+-0.0079) 12.96 s\n",
"\tNaive\t 0.7643 (+-0.0199) 0.18 s\n",
"\tLight\t 0.8533 (+-0.0092) 5.05 s\n",
"Number of Columns: 39 Exam(s): 3\n",
"['document.temperatura(t-4)', 'delta_document.glicemia_capilar_t3-t4', 'delta_document.pa_diastolica_t2-t3', 'UTI', 'document.freq_respiratoria(t-3)', 'delta_document.temperatura_t3-t4', 'document.sat_o2(t-4)', 'age', 'delta_document.freq_respiratoria_t3-t4', 'document.temperatura(t-3)', 'document.glicemia_capilar(t-3)', 'delta_document.pa_sistolica_t2-t3', 'document.glicemia_capilar(t-2)', 'document.freq_cardiaca(t-4)', 'delta_document.sat_o2_t3-t4', 'document.freq_cardiaca(t-2)', 'delta_document.pa_diastolica_t3-t4', 'document.pa_sistolica(t-2)', 'document.glicemia_capilar(t-4)', 'document.freq_respiratoria(t-2)', 'delta_document.pa_sistolica_t3-t4', 'delta_document.freq_respiratoria_t2-t3', 'delta_document.glicemia_capilar_t2-t3', 'document.sat_o2(t-2)', 'delta_document.sat_o2_t2-t3', 'document.freq_respiratoria(t-4)', 'document.pa_sistolica(t-3)', 'document.freq_cardiaca(t-3)', 'document.temperatura(t-2)', 'document.pa_diastolica(t-2)', 'days_from_entrance', 'document.pa_diastolica(t-3)', 'document.pa_diastolica(t-4)', 'document.sat_o2(t-3)', 'delta_document.freq_cardiaca_t2-t3', 'document.sexo', 'delta_document.freq_cardiaca_t3-t4', 'document.pa_sistolica(t-4)', 'delta_document.temperatura_t2-t3']\n",
"\tXGBoost\t 0.8751 (+-0.0077) 2.64 s\n",
"\tLogReg\t 0.8376 (+-0.0125) 2.3 s\n",
"\tD.Tree\t 0.6621 (+-0.0167) 3.08 s\n",
"\tRForest\t 0.8424 (+-0.0149) 16.19 s\n",
"\tCatBoos\t 0.8758 (+-0.0062) 13.13 s\n",
"\tNaive\t 0.7555 (+-0.0192) 0.17 s\n",
"\tLight\t 0.8763 (+-0.0076) 7.45 s\n",
"Number of Columns: 53 Exam(s): 4\n",
"['document.temperatura(t-4)', 'delta_document.pa_diastolica_t2-t3', 'delta_document.glicemia_capilar_t3-t4', 'UTI', 'delta_document.temperatura_t1-t2', 'document.freq_respiratoria(t-3)', 'delta_document.glicemia_capilar_t1-t2', 'delta_document.temperatura_t3-t4', 'document.freq_respiratoria(t-1)', 'age', 'document.sat_o2(t-4)', 'delta_document.freq_respiratoria_t3-t4', 'document.temperatura(t-3)', 'delta_document.pa_sistolica_t1-t2', 'document.glicemia_capilar(t-3)', 'delta_document.pa_sistolica_t2-t3', 'delta_document.freq_respiratoria_t1-t2', 'document.glicemia_capilar(t-2)', 'document.pa_sistolica(t-1)', 'document.freq_cardiaca(t-4)', 'delta_document.sat_o2_t3-t4', 'document.freq_cardiaca(t-2)', 'delta_document.pa_diastolica_t3-t4', 'document.freq_cardiaca(t-1)', 'document.temperatura(t-1)', 'document.pa_sistolica(t-2)', 'document.glicemia_capilar(t-1)', 'delta_document.sat_o2_t1-t2', 'document.glicemia_capilar(t-4)', 'document.freq_respiratoria(t-2)', 'delta_document.pa_sistolica_t3-t4', 'delta_document.freq_respiratoria_t2-t3', 'delta_document.glicemia_capilar_t2-t3', 'document.sat_o2(t-2)', 'delta_document.sat_o2_t2-t3', 'delta_document.freq_cardiaca_t1-t2', 'document.freq_respiratoria(t-4)', 'document.pa_sistolica(t-3)', 'document.freq_cardiaca(t-3)', 'document.temperatura(t-2)', 'delta_document.pa_diastolica_t1-t2', 'document.pa_diastolica(t-2)', 'days_from_entrance', 'document.sat_o2(t-1)', 'document.pa_diastolica(t-3)', 'document.pa_diastolica(t-4)', 'document.sat_o2(t-3)', 'delta_document.freq_cardiaca_t2-t3', 'document.pa_diastolica(t-1)', 'document.sexo', 'delta_document.freq_cardiaca_t3-t4', 'document.pa_sistolica(t-4)', 'delta_document.temperatura_t2-t3']\n",
"\tXGBoost\t 0.887 (+-0.007) 3.26 s\n",
"\tLogReg\t 0.8543 (+-0.0111) 3.49 s\n",
"\tD.Tree\t 0.6798 (+-0.0143) 4.68 s\n",
"\tRForest\t 0.8619 (+-0.0151) 20.08 s\n",
"\tCatBoos\t 0.8886 (+-0.0067) 12.87 s\n",
"\tNaive\t 0.7596 (+-0.0197) 0.21 s\n",
"\tLight\t 0.8851 (+-0.0084) 10.27 s\n",
"Number of Columns: 67 Exam(s): 5\n",
"['document.temperatura(t-4)', 'document.pa_diastolica(t)', 'delta_document.pa_diastolica_t2-t3', 'delta_document.glicemia_capilar_t3-t4', 'document.sat_o2(t)', 'UTI', 'delta_document.temperatura_t1-t2', 'document.freq_cardiaca(t)', 'document.freq_respiratoria(t-3)', 'delta_document.glicemia_capilar_t1-t2', 'document.temperatura(t)', 'delta_document.temperatura_t3-t4', 'document.freq_respiratoria(t-1)', 'document.sat_o2(t-4)', 'age', 'delta_document.freq_respiratoria_t3-t4', 'delta_document.glicemia_capilar_t-t1', 'delta_document.freq_respiratoria_t-t1', 'delta_document.temperatura_t2-t3', 'delta_document.pa_sistolica_t-t1', 'document.temperatura(t-3)', 'delta_document.pa_sistolica_t1-t2', 'document.glicemia_capilar(t-3)', 'delta_document.pa_sistolica_t2-t3', 'delta_document.freq_respiratoria_t1-t2', 'document.glicemia_capilar(t-2)', 'document.pa_sistolica(t-1)', 'document.freq_cardiaca(t-4)', 'delta_document.sat_o2_t3-t4', 'document.freq_cardiaca(t-2)', 'document.pa_sistolica(t)', 'delta_document.pa_diastolica_t3-t4', 'document.freq_cardiaca(t-1)', 'delta_document.freq_cardiaca_t-t1', 'document.temperatura(t-1)', 'document.glicemia_capilar(t)', 'document.freq_respiratoria(t)', 'document.pa_sistolica(t-2)', 'document.glicemia_capilar(t-1)', 'delta_document.sat_o2_t1-t2', 'document.glicemia_capilar(t-4)', 'document.freq_respiratoria(t-2)', 'delta_document.pa_sistolica_t3-t4', 'delta_document.freq_respiratoria_t2-t3', 'delta_document.glicemia_capilar_t2-t3', 'document.sat_o2(t-2)', 'delta_document.sat_o2_t2-t3', 'delta_document.freq_cardiaca_t1-t2', 'document.freq_respiratoria(t-4)', 'document.pa_sistolica(t-3)', 'delta_document.pa_diastolica_t-t1', 'document.freq_cardiaca(t-3)', 'document.temperatura(t-2)', 'delta_document.pa_diastolica_t1-t2', 'document.pa_diastolica(t-2)', 'days_from_entrance', 'document.sat_o2(t-1)', 'document.pa_diastolica(t-3)', 'delta_document.sat_o2_t-t1', 'document.pa_diastolica(t-4)', 'document.sat_o2(t-3)', 'delta_document.freq_cardiaca_t2-t3', 'document.sexo', 'delta_document.temperatura_t-t1', 'delta_document.freq_cardiaca_t3-t4', 'document.pa_sistolica(t-4)', 'document.pa_diastolica(t-1)']\n",
"\tXGBoost\t 0.9072 (+-0.0067) 2.57 s\n",
"\tLogReg\t 0.8749 (+-0.0086) 4.42 s\n",
"\tD.Tree\t 0.6957 (+-0.0206) 6.53 s\n",
"\tRForest\t 0.882 (+-0.0111) 24.15 s\n",
"\tCatBoos\t 0.9071 (+-0.0083) 12.77 s\n",
"\tNaive\t 0.7735 (+-0.018) 0.29 s\n",
"\tLight\t 0.9057 (+-0.0066) 12.6 s\n"
],
"name": "stdout"
}
]
}
]
}