{
"cells": [
{
"cell_type": "markdown",
"id": "8527ac20",
"metadata": {},
"source": [
"# EDA - Thyroid Classification"
]
},
{
"cell_type": "markdown",
"id": "6c85d10d",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1 </span>Introduction</a></span></li><li><span><a href=\"#Read-the-data\" data-toc-modified-id=\"Read-the-data-2\"><span class=\"toc-item-num\">2 </span>Read the data</a></span></li><li><span><a href=\"#Data-Exploration\" data-toc-modified-id=\"Data-Exploration-3\"><span class=\"toc-item-num\">3 </span>Data Exploration</a></span><ul class=\"toc-item\"><li><span><a href=\"#Missing-Values\" data-toc-modified-id=\"Missing-Values-3.1\"><span class=\"toc-item-num\">3.1 </span>Missing Values</a></span><ul class=\"toc-item\"><li><span><a href=\"#Identify-missing\" data-toc-modified-id=\"Identify-missing-3.1.1\"><span class=\"toc-item-num\">3.1.1 </span>Identify missing</a></span></li><li><span><a href=\"#Replace-?-with-Nan\" data-toc-modified-id=\"Replace-?-with-Nan-3.1.2\"><span class=\"toc-item-num\">3.1.2 </span>Replace <code>?</code> with <code>Nan</code></a></span></li></ul></li><li><span><a href=\"#Remove-columns-with-duplicate-information\" data-toc-modified-id=\"Remove-columns-with-duplicate-information-3.2\"><span class=\"toc-item-num\">3.2 </span>Remove columns with duplicate information</a></span></li><li><span><a href=\"#Feature-Engineering\" data-toc-modified-id=\"Feature-Engineering-3.3\"><span class=\"toc-item-num\">3.3 </span>Feature Engineering</a></span><ul class=\"toc-item\"><li><span><a href=\"#Convert-sex-column-to-numerical\" data-toc-modified-id=\"Convert-sex-column-to-numerical-3.3.1\"><span class=\"toc-item-num\">3.3.1 </span>Convert <code>sex</code> column to numerical</a></span></li><li><span><a href=\"#Convert-columns-with-binary-categories-to-numerical\" data-toc-modified-id=\"Convert-columns-with-binary-categories-to-numerical-3.3.2\"><span class=\"toc-item-num\">3.3.2 </span>Convert columns with binary categories to numerical</a></span></li><li><span><a href=\"#One-Hot-encode-referral-source\" data-toc-modified-id=\"One-Hot-encode-referral-source-3.3.3\"><span class=\"toc-item-num\">3.3.3 </span>One-Hot encode <code>referral source</code></a></span></li><li><span><a href=\"#Label-encode-Class\" data-toc-modified-id=\"Label-encode-Class-3.3.4\"><span class=\"toc-item-num\">3.3.4 </span>Label encode <code>Class</code></a></span></li></ul></li><li><span><a href=\"#Impute-Missing\" data-toc-modified-id=\"Impute-Missing-3.4\"><span class=\"toc-item-num\">3.4 </span>Impute Missing</a></span></li><li><span><a href=\"#Handle-Outliers\" data-toc-modified-id=\"Handle-Outliers-3.5\"><span class=\"toc-item-num\">3.5 </span>Handle Outliers</a></span><ul class=\"toc-item\"><li><span><a href=\"#Remove-recrods-with--age->-100-and-TSH->-150\" data-toc-modified-id=\"Remove-recrods-with--age->-100-and-TSH->-150-3.5.1\"><span class=\"toc-item-num\">3.5.1 </span>Remove recrods with <code>age > 100</code> and <code>TSH > 150</code></a></span></li></ul></li><li><span><a href=\"#Data-Transformation\" data-toc-modified-id=\"Data-Transformation-3.6\"><span class=\"toc-item-num\">3.6 </span>Data Transformation</a></span><ul class=\"toc-item\"><li><span><a href=\"#Apply-log-transformation\" data-toc-modified-id=\"Apply-log-transformation-3.6.1\"><span class=\"toc-item-num\">3.6.1 </span>Apply log transformation</a></span></li><li><span><a href=\"#Apply-Box-cox-tranformation\" data-toc-modified-id=\"Apply-Box-cox-tranformation-3.6.2\"><span class=\"toc-item-num\">3.6.2 </span>Apply Box-cox tranformation</a></span></li></ul></li><li><span><a href=\"#Distribution-of-Class\" data-toc-modified-id=\"Distribution-of-Class-3.7\"><span class=\"toc-item-num\">3.7 </span>Distribution of <code>Class</code></a></span><ul class=\"toc-item\"><li><span><a href=\"#Oversample-using-RandomOverSampler()\" data-toc-modified-id=\"Oversample-using-RandomOverSampler()-3.7.1\"><span class=\"toc-item-num\">3.7.1 </span>Oversample using <code>RandomOverSampler()</code></a></span></li></ul></li></ul></li><li><span><a href=\"#Clustering\" data-toc-modified-id=\"Clustering-4\"><span class=\"toc-item-num\">4 </span>Clustering</a></span></li><li><span><a href=\"#Modeling\" data-toc-modified-id=\"Modeling-5\"><span class=\"toc-item-num\">5 </span>Modeling</a></span><ul class=\"toc-item\"><li><span><a href=\"#XGB\" data-toc-modified-id=\"XGB-5.1\"><span class=\"toc-item-num\">5.1 </span>XGB</a></span></li><li><span><a href=\"#SVM\" data-toc-modified-id=\"SVM-5.2\"><span class=\"toc-item-num\">5.2 </span>SVM</a></span></li></ul></li></ul></div>"
]
},
{
"cell_type": "markdown",
"id": "42addc0e",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"id": "1be17173",
"metadata": {},
"source": [
"This notebook performs various exploratory data analysis tasks needed for thyroid classification project.\n",
"\n",
"__Problem Statement__: To build a classification methodology to predict the type of Thyroid a person has, based on the below features.\n",
"\n",
"| Column | Description | Column | Description |\n",
"|---|---|---|---|\n",
"| age | Age of the person | TSH_measured | true or false |\n",
"| sex | Male or Female | TSH | thyroid stimulating hormone floating value |\n",
"| on_thyroxine | true or false | T3_measured | true or false |\n",
"| on_antithyroid_medication | true or false | T3 | triiodothyronine value |\n",
"| sick | true or false | TT4_measured | true or false |\n",
"| pregnant | true or false | TT4 | Thyroxine value |\n",
"| thyroid_surgery | true or false | T4U_measured | true or false |\n",
"| I131_treatment | true or false | T4U | numerical value |\n",
"| query_hypothyroid | true or false | FTI_measured | true or false |\n",
"| query_hyperthyroid | true or false | FTI | Free Thyroxine Index |\n",
"| lithium | true or false | TBG_measured | true or false |\n",
"| goitre | true or false | TBG | Thyroid Binding Globulin value |\n",
"| tumor | true or false | referral_source | different sources of referals |\n",
"| hypopituitary | true or false | Class | different types of thyroid |\n",
"| psych | true or false | | |"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "faa5acef",
"metadata": {},
"outputs": [],
"source": [
"# General imports\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.impute import KNNImputer\n",
"from scipy import stats\n",
"from imblearn.over_sampling import RandomOverSampler\n",
"from sklearn.cluster import KMeans\n",
"\n",
"pd.set_option('display.max_rows', 500)\n",
"pd.set_option('display.max_columns', 500)\n",
"pd.set_option('display.width', 1000)"
]
},
{
"cell_type": "markdown",
"id": "193e8a84",
"metadata": {},
"source": [
"## Read the data"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "eb5adf8f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3972, 30)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read clean data exported from DB\n",
"df = pd.read_csv('Training/TrainingFile_FromDB/InputFile.csv')\n",
"df.shape"
]
},
{
"cell_type": "markdown",
"id": "7d971e2d",
"metadata": {},
"source": [
"> Data exported from database has 3972 records and 30 columns."
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "4c270b6b",
"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>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>query_hyperthyroid</th>\n",
" <th>lithium</th>\n",
" <th>goitre</th>\n",
" <th>tumor</th>\n",
" <th>hypopituitary</th>\n",
" <th>psych</th>\n",
" <th>TSH_measured</th>\n",
" <th>TSH</th>\n",
" <th>T3_measured</th>\n",
" <th>T3</th>\n",
" <th>TT4_measured</th>\n",
" <th>TT4</th>\n",
" <th>T4U_measured</th>\n",
" <th>T4U</th>\n",
" <th>FTI_measured</th>\n",
" <th>FTI</th>\n",
" <th>TBG_measured</th>\n",
" <th>TBG</th>\n",
" <th>referral_source</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>49</td>\n",
" <td>F</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>1.0</td>\n",
" <td>t</td>\n",
" <td>1.6</td>\n",
" <td>t</td>\n",
" <td>93.0</td>\n",
" <td>t</td>\n",
" <td>0.94</td>\n",
" <td>t</td>\n",
" <td>99.0</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>46</td>\n",
" <td>M</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>1.1</td>\n",
" <td>t</td>\n",
" <td>2.9</td>\n",
" <td>t</td>\n",
" <td>125.0</td>\n",
" <td>t</td>\n",
" <td>1.02</td>\n",
" <td>t</td>\n",
" <td>122.0</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>SVHC</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80</td>\n",
" <td>F</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>2.2</td>\n",
" <td>t</td>\n",
" <td>1.7</td>\n",
" <td>t</td>\n",
" <td>83.0</td>\n",
" <td>t</td>\n",
" <td>1.11</td>\n",
" <td>t</td>\n",
" <td>76.0</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80</td>\n",
" <td>F</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>1.1</td>\n",
" <td>t</td>\n",
" <td>1.8</td>\n",
" <td>t</td>\n",
" <td>94.0</td>\n",
" <td>t</td>\n",
" <td>0.94</td>\n",
" <td>t</td>\n",
" <td>100.0</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>SVHC</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>26</td>\n",
" <td>F</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>68</td>\n",
" <td>M</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>1.9</td>\n",
" <td>t</td>\n",
" <td>2.9</td>\n",
" <td>t</td>\n",
" <td>90.0</td>\n",
" <td>t</td>\n",
" <td>1.0</td>\n",
" <td>t</td>\n",
" <td>90.0</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>SVI</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>24</td>\n",
" <td>M</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>0.6</td>\n",
" <td>t</td>\n",
" <td>3.0</td>\n",
" <td>t</td>\n",
" <td>119.0</td>\n",
" <td>t</td>\n",
" <td>1.03</td>\n",
" <td>t</td>\n",
" <td>115.0</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>21</td>\n",
" <td>F</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>38</td>\n",
" <td>M</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>t</td>\n",
" <td>78.0</td>\n",
" <td>t</td>\n",
" <td>0.87</td>\n",
" <td>t</td>\n",
" <td>90.0</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>55</td>\n",
" <td>F</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>t</td>\n",
" <td>0.05</td>\n",
" <td>t</td>\n",
" <td>2.8</td>\n",
" <td>t</td>\n",
" <td>189.0</td>\n",
" <td>t</td>\n",
" <td>1.66</td>\n",
" <td>t</td>\n",
" <td>114.0</td>\n",
" <td>f</td>\n",
" <td>?</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick pregnant thyroid_surgery I131_treatment query_hypothyroid query_hyperthyroid lithium goitre tumor hypopituitary psych TSH_measured TSH T3_measured T3 TT4_measured TT4 T4U_measured T4U FTI_measured FTI TBG_measured TBG referral_source Class\n",
"0 49 F f f f t f f f f f f f f f f t 1.0 t 1.6 t 93.0 t 0.94 t 99.0 f ? other negative\n",
"1 46 M f f f f f f f f f f f f f t t 1.1 t 2.9 t 125.0 t 1.02 t 122.0 f ? SVHC negative\n",
"2 80 F f f t f f f f f f f f f f f t 2.2 t 1.7 t 83.0 t 1.11 t 76.0 f ? other negative\n",
"3 80 F f f f f f f f f f f f f f t t 1.1 t 1.8 t 94.0 t 0.94 t 100.0 f ? SVHC negative\n",
"4 26 F f f f f f f f f f f f f f f f ? f ? f ? f ? f ? f ? other negative\n",
"5 68 M f f f f f f f f f f f f f f t 1.9 t 2.9 t 90.0 t 1.0 t 90.0 f ? SVI negative\n",
"6 24 M f f f f f f f f f f f f f f t 0.6 t 3.0 t 119.0 t 1.03 t 115.0 f ? other negative\n",
"7 21 F f f f f f f f f t f f f f f f ? f ? f ? f ? f ? f ? other negative\n",
"8 38 M f f f f f f f f f f f f f f f ? f ? t 78.0 t 0.87 t 90.0 f ? other negative\n",
"9 55 F t f f f f f f f f f f f f f t 0.05 t 2.8 t 189.0 t 1.66 t 114.0 f ? other negative"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check head\n",
"df.head(10)"
]
},
{
"cell_type": "markdown",
"id": "394d71b7",
"metadata": {},
"source": [
"> Observations: \n",
" - The data does not show any missing values. However, looking closely, we can see that missing values are indicated by `?`.<br>\n",
" - Some columns such as `FTI_measured` with t/f values are just indicating whether the next column `FTI` has a value or not. \n",
"\n",
"Let's count the number of missing and remove additional columns that do not add any value."
]
},
{
"cell_type": "markdown",
"id": "061b4a38",
"metadata": {},
"source": [
"## Data Exploration"
]
},
{
"cell_type": "markdown",
"id": "98651be1",
"metadata": {},
"source": [
"### Missing Values"
]
},
{
"cell_type": "markdown",
"id": "6ee0e7fc",
"metadata": {},
"source": [
"#### Identify missing"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "b13ca0f1",
"metadata": {},
"outputs": [],
"source": [
"# Define function to count missing values represented by '?'\n",
"def calc_missing(df):\n",
" print(f'Column \\t No. of missing')\n",
" for col in df.columns:\n",
" count = df[col][df[col]=='?'].count()\n",
" if count != 0:\n",
" print(f'{col} \\t {count}')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "7f349497",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column \t No. of missing\n",
"age \t 1\n",
"sex \t 157\n",
"TSH \t 390\n",
"T3 \t 812\n",
"TT4 \t 247\n",
"T4U \t 409\n",
"FTI \t 407\n",
"TBG \t 3972\n"
]
}
],
"source": [
"# Count missing\n",
"calc_missing(df)"
]
},
{
"cell_type": "markdown",
"id": "99e9ed19",
"metadata": {},
"source": [
"> All records in the TBG column are missing. We will drop this column later."
]
},
{
"cell_type": "markdown",
"id": "aa4e1635",
"metadata": {},
"source": [
"#### Replace `?` with `Nan`"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "de1cfa79",
"metadata": {},
"outputs": [],
"source": [
"# Define function\n",
"def replace_missing(df):\n",
" for col in df.columns:\n",
" count = df[col][df[col]=='?'].count()\n",
" if count != 0:\n",
" df[col] = df[col].replace('?', np.nan)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "22805709",
"metadata": {},
"outputs": [],
"source": [
"# Replace ? with Nan\n",
"replace_missing(df)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "384e423e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"age 1\n",
"sex 157\n",
"on_thyroxine 0\n",
"query_on_thyroxine 0\n",
"on_antithyroid_medication 0\n",
"sick 0\n",
"pregnant 0\n",
"thyroid_surgery 0\n",
"I131_treatment 0\n",
"query_hypothyroid 0\n",
"query_hyperthyroid 0\n",
"lithium 0\n",
"goitre 0\n",
"tumor 0\n",
"hypopituitary 0\n",
"psych 0\n",
"TSH_measured 0\n",
"TSH 390\n",
"T3_measured 0\n",
"T3 812\n",
"TT4_measured 0\n",
"TT4 247\n",
"T4U_measured 0\n",
"T4U 409\n",
"FTI_measured 0\n",
"FTI 407\n",
"TBG_measured 0\n",
"TBG 3972\n",
"referral_source 0\n",
"Class 0\n",
"dtype: int64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check missing\n",
"df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"id": "704ac1d1",
"metadata": {},
"source": [
"### Remove columns with duplicate information\n",
"\n",
"Columns with `_measured` in the column name contain t/f values which are just indicators of whether the next column FTI has a value or not. Since these columns do not add any information to the data, we will remove these columns."
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "1e2aa86b",
"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>TSH</th>\n",
" <th>T3_measured</th>\n",
" <th>T3</th>\n",
" <th>TT4_measured</th>\n",
" <th>TT4</th>\n",
" <th>T4U_measured</th>\n",
" <th>T4U</th>\n",
" <th>FTI_measured</th>\n",
" <th>FTI</th>\n",
" <th>TBG_measured</th>\n",
" <th>TBG</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>t</td>\n",
" <td>1.6</td>\n",
" <td>t</td>\n",
" <td>93.0</td>\n",
" <td>t</td>\n",
" <td>0.94</td>\n",
" <td>t</td>\n",
" <td>99.0</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.1</td>\n",
" <td>t</td>\n",
" <td>2.9</td>\n",
" <td>t</td>\n",
" <td>125.0</td>\n",
" <td>t</td>\n",
" <td>1.02</td>\n",
" <td>t</td>\n",
" <td>122.0</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.2</td>\n",
" <td>t</td>\n",
" <td>1.7</td>\n",
" <td>t</td>\n",
" <td>83.0</td>\n",
" <td>t</td>\n",
" <td>1.11</td>\n",
" <td>t</td>\n",
" <td>76.0</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.1</td>\n",
" <td>t</td>\n",
" <td>1.8</td>\n",
" <td>t</td>\n",
" <td>94.0</td>\n",
" <td>t</td>\n",
" <td>0.94</td>\n",
" <td>t</td>\n",
" <td>100.0</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" <td>f</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TSH T3_measured T3 TT4_measured TT4 T4U_measured T4U FTI_measured FTI TBG_measured TBG\n",
"0 1.0 t 1.6 t 93.0 t 0.94 t 99.0 f NaN\n",
"1 1.1 t 2.9 t 125.0 t 1.02 t 122.0 f NaN\n",
"2 2.2 t 1.7 t 83.0 t 1.11 t 76.0 f NaN\n",
"3 1.1 t 1.8 t 94.0 t 0.94 t 100.0 f NaN\n",
"4 NaN f NaN f NaN f NaN f NaN f NaN"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Look at columns with duplicate information\n",
"df.iloc[:, -13:-2].head()"
]
},
{
"cell_type": "markdown",
"id": "629a92a1",
"metadata": {},
"source": [
"> `t` in a column with `_measured` indicates that the next column has a value and `f` indicates a missing value in the next column.\n",
"\n",
"We will drop the columns with `_measured`."
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "a663020a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['TSH_measured',\n",
" 'T3_measured',\n",
" 'TT4_measured',\n",
" 'T4U_measured',\n",
" 'FTI_measured',\n",
" 'TBG_measured']"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Identify columns\n",
"cols_to_drop = [col for col in df.columns if '_measured' in col]\n",
"cols_to_drop"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "bceb3199",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3972"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['TBG'].isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "60a36b98",
"metadata": {},
"outputs": [],
"source": [
"# Identify columns that do not have any variation in data as they do not add any value\n",
"for col in df.columns:\n",
" if df[col].isna().sum() == df.shape[0]:\n",
" cols_to_drop.append(col)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "7d34db4f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['TSH_measured',\n",
" 'T3_measured',\n",
" 'TT4_measured',\n",
" 'T4U_measured',\n",
" 'FTI_measured',\n",
" 'TBG_measured',\n",
" 'TBG']"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols_to_drop"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "8d65c6a0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3972, 23)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop columns\n",
"df.drop(columns=cols_to_drop, axis=1, inplace=True)\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "ea9880e7",
"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>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>query_hyperthyroid</th>\n",
" <th>lithium</th>\n",
" <th>goitre</th>\n",
" <th>tumor</th>\n",
" <th>hypopituitary</th>\n",
" <th>psych</th>\n",
" <th>TSH</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>referral_source</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3971</td>\n",
" <td>3815</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" <td>3582</td>\n",
" <td>3160</td>\n",
" <td>3725</td>\n",
" <td>3563</td>\n",
" <td>3565</td>\n",
" <td>3972</td>\n",
" <td>3972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>93</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>287</td>\n",
" <td>69</td>\n",
" <td>241</td>\n",
" <td>146</td>\n",
" <td>234</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>59</td>\n",
" <td>F</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>0.2</td>\n",
" <td>2.0</td>\n",
" <td>101.0</td>\n",
" <td>0.99</td>\n",
" <td>100.0</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>102</td>\n",
" <td>2608</td>\n",
" <td>3484</td>\n",
" <td>3916</td>\n",
" <td>3926</td>\n",
" <td>3816</td>\n",
" <td>3917</td>\n",
" <td>3917</td>\n",
" <td>3906</td>\n",
" <td>3733</td>\n",
" <td>3726</td>\n",
" <td>3952</td>\n",
" <td>3937</td>\n",
" <td>3872</td>\n",
" <td>3971</td>\n",
" <td>3780</td>\n",
" <td>119</td>\n",
" <td>251</td>\n",
" <td>72</td>\n",
" <td>101</td>\n",
" <td>77</td>\n",
" <td>2317</td>\n",
" <td>3668</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick pregnant thyroid_surgery I131_treatment query_hypothyroid query_hyperthyroid lithium goitre tumor hypopituitary psych TSH T3 TT4 T4U FTI referral_source Class\n",
"count 3971 3815 3972 3972 3972 3972 3972 3972 3972 3972 3972 3972 3972 3972 3972 3972 3582 3160 3725 3563 3565 3972 3972\n",
"unique 93 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 287 69 241 146 234 5 4\n",
"top 59 F f f f f f f f f f f f f f f 0.2 2.0 101.0 0.99 100.0 other negative\n",
"freq 102 2608 3484 3916 3926 3816 3917 3917 3906 3733 3726 3952 3937 3872 3971 3780 119 251 72 101 77 2317 3668"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check data\n",
"df.describe()"
]
},
{
"cell_type": "markdown",
"id": "71426a46",
"metadata": {},
"source": [
"> Observation: \n",
" - Majority of the data is categorical with two categories. \n",
" - Referral source column has 5 and the Class column has 4 categories.\n",
" \n",
"Before we impute the missing data, let's map the binary categories and encode the multi-categories to nurical data as needed."
]
},
{
"cell_type": "markdown",
"id": "d5b85b1b",
"metadata": {},
"source": [
"### Feature Engineering\n",
"\n",
"In this section, we will map the features with binary categories and encode the features with multi-categories to numerical data. "
]
},
{
"cell_type": "markdown",
"id": "64f63372",
"metadata": {},
"source": [
"#### Convert `sex` column to numerical"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "c515a693",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"object\n",
"F 2608\n",
"M 1207\n",
"Name: sex, dtype: int64\n"
]
}
],
"source": [
"# Check data type and categories\n",
"print(df['sex'].dtype)\n",
"print(df['sex'].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "edfc7a8d",
"metadata": {},
"outputs": [],
"source": [
"# Convert to numerical\n",
"df['sex'] = df['sex'].map({'F':0, 'M':1})"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "d45a0581",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('float64')"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check data type\n",
"df['sex'].dtype"
]
},
{
"cell_type": "markdown",
"id": "570b990a",
"metadata": {},
"source": [
"#### Convert columns with binary categories to numerical\n",
"\n",
"There are many columns with two unqiue `t` and `f` categories. Let's map these to numerical 0 and 1."
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "67d2a954",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"age \t 94\n",
"sex \t 3\n",
"on_thyroxine \t 2\n",
"query_on_thyroxine \t 2\n",
"on_antithyroid_medication \t 2\n",
"sick \t 2\n",
"pregnant \t 2\n",
"thyroid_surgery \t 2\n",
"I131_treatment \t 2\n",
"query_hypothyroid \t 2\n",
"query_hyperthyroid \t 2\n",
"lithium \t 2\n",
"goitre \t 2\n",
"tumor \t 2\n",
"hypopituitary \t 2\n",
"psych \t 2\n",
"TSH \t 288\n",
"T3 \t 70\n",
"TT4 \t 242\n",
"T4U \t 147\n",
"FTI \t 235\n",
"referral_source \t 5\n",
"Class \t 4\n"
]
}
],
"source": [
"for col in df.columns:\n",
" print(f'{col} \\t {len(df[col].unique())}')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "00565b6e",
"metadata": {},
"outputs": [],
"source": [
"# Map categories\n",
"for col in df.columns:\n",
" if len(df[col].unique())==2:\n",
" df[col] = df[col].map({'f':0, 't':1})"
]
},
{
"cell_type": "code",
"execution_count": 107,
"id": "c4586fa0",
"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>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>query_hyperthyroid</th>\n",
" <th>lithium</th>\n",
" <th>goitre</th>\n",
" <th>tumor</th>\n",
" <th>hypopituitary</th>\n",
" <th>psych</th>\n",
" <th>TSH</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>referral_source</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>49</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>1.6</td>\n",
" <td>93.0</td>\n",
" <td>0.94</td>\n",
" <td>99.0</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>46</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.1</td>\n",
" <td>2.9</td>\n",
" <td>125.0</td>\n",
" <td>1.02</td>\n",
" <td>122.0</td>\n",
" <td>SVHC</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2.2</td>\n",
" <td>1.7</td>\n",
" <td>83.0</td>\n",
" <td>1.11</td>\n",
" <td>76.0</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.1</td>\n",
" <td>1.8</td>\n",
" <td>94.0</td>\n",
" <td>0.94</td>\n",
" <td>100.0</td>\n",
" <td>SVHC</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>26</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>other</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick pregnant thyroid_surgery I131_treatment query_hypothyroid query_hyperthyroid lithium goitre tumor hypopituitary psych TSH T3 TT4 T4U FTI referral_source Class\n",
"0 49 0.0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1.0 1.6 93.0 0.94 99.0 other negative\n",
"1 46 1.0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1.1 2.9 125.0 1.02 122.0 SVHC negative\n",
"2 80 0.0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2.2 1.7 83.0 1.11 76.0 other negative\n",
"3 80 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1.1 1.8 94.0 0.94 100.0 SVHC negative\n",
"4 26 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN other negative"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check head\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "f031d827",
"metadata": {},
"source": [
"#### One-Hot encode `referral source`\n",
"\n",
"The column has multiple categories. Let's create dummy variables for these categories"
]
},
{
"cell_type": "code",
"execution_count": 108,
"id": "b68cca55",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['other', 'SVHC', 'SVI', 'STMW', 'SVHD'], dtype=object)"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check categories\n",
"df['referral_source'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"id": "aaa3d1c4",
"metadata": {},
"outputs": [],
"source": [
"# Create dummy variables\n",
"# drop_first=True to avoid dummy variable trap\n",
"df = pd.get_dummies(data=df, columns=['referral_source'], drop_first=True)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"id": "9211b933",
"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>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>query_hyperthyroid</th>\n",
" <th>lithium</th>\n",
" <th>goitre</th>\n",
" <th>tumor</th>\n",
" <th>hypopituitary</th>\n",
" <th>psych</th>\n",
" <th>TSH</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>Class</th>\n",
" <th>referral_source_SVHC</th>\n",
" <th>referral_source_SVHD</th>\n",
" <th>referral_source_SVI</th>\n",
" <th>referral_source_other</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>49</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>1.6</td>\n",
" <td>93.0</td>\n",
" <td>0.94</td>\n",
" <td>99.0</td>\n",
" <td>negative</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>46</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.1</td>\n",
" <td>2.9</td>\n",
" <td>125.0</td>\n",
" <td>1.02</td>\n",
" <td>122.0</td>\n",
" <td>negative</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick pregnant thyroid_surgery I131_treatment query_hypothyroid query_hyperthyroid lithium goitre tumor hypopituitary psych TSH T3 TT4 T4U FTI Class referral_source_SVHC referral_source_SVHD referral_source_SVI referral_source_other\n",
"0 49 0.0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1.0 1.6 93.0 0.94 99.0 negative 0 0 0 1\n",
"1 46 1.0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1.1 2.9 125.0 1.02 122.0 negative 1 0 0 0"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check head\n",
"df.head(2)"
]
},
{
"cell_type": "markdown",
"id": "b5f71fee",
"metadata": {},
"source": [
"#### Label encode `Class`\n",
"\n",
"The coulmn has multiple categories. Since `Class` represents various severities of thyroid, we will label encode the feature."
]
},
{
"cell_type": "code",
"execution_count": 111,
"id": "3290e935",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['negative', 'compensated_hypothyroid', 'primary_hypothyroid',\n",
" 'secondary_hypothyroid'], dtype=object)"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check categories\n",
"df['Class'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 112,
"id": "018ba462",
"metadata": {},
"outputs": [],
"source": [
"# Encode\n",
"encoder = LabelEncoder()\n",
"df['Class'] = encoder.fit_transform(df['Class'])"
]
},
{
"cell_type": "code",
"execution_count": 113,
"id": "541c22d7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 2, 3])"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check categories\n",
"df['Class'].unique()"
]
},
{
"cell_type": "markdown",
"id": "df20c118",
"metadata": {},
"source": [
"### Impute Missing\n",
"\n",
"The data has a lot of missing values and we will use `KNNImputer` to impute the missing."
]
},
{
"cell_type": "code",
"execution_count": 114,
"id": "57bcf6ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"age 1\n",
"sex 157\n",
"on_thyroxine 0\n",
"query_on_thyroxine 0\n",
"on_antithyroid_medication 0\n",
"sick 0\n",
"pregnant 0\n",
"thyroid_surgery 0\n",
"I131_treatment 0\n",
"query_hypothyroid 0\n",
"query_hyperthyroid 0\n",
"lithium 0\n",
"goitre 0\n",
"tumor 0\n",
"hypopituitary 0\n",
"psych 0\n",
"TSH 390\n",
"T3 812\n",
"TT4 247\n",
"T4U 409\n",
"FTI 407\n",
"Class 0\n",
"referral_source_SVHC 0\n",
"referral_source_SVHD 0\n",
"referral_source_SVI 0\n",
"referral_source_other 0\n",
"dtype: int64"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check missing\n",
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"id": "08054188",
"metadata": {},
"outputs": [],
"source": [
"# Impute data\n",
"imputer = KNNImputer(n_neighbors=3, weights='uniform', missing_values=np.nan)\n",
"new_array = imputer.fit_transform(df)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"id": "7fa01760",
"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>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>query_hyperthyroid</th>\n",
" <th>lithium</th>\n",
" <th>goitre</th>\n",
" <th>tumor</th>\n",
" <th>hypopituitary</th>\n",
" <th>psych</th>\n",
" <th>TSH</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>Class</th>\n",
" <th>referral_source_SVHC</th>\n",
" <th>referral_source_SVHD</th>\n",
" <th>referral_source_SVI</th>\n",
" <th>referral_source_other</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>49.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>93.0</td>\n",
" <td>1.0</td>\n",
" <td>99.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>46.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>125.0</td>\n",
" <td>1.0</td>\n",
" <td>122.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>83.0</td>\n",
" <td>1.0</td>\n",
" <td>76.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>94.0</td>\n",
" <td>1.0</td>\n",
" <td>100.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>26.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>108.0</td>\n",
" <td>1.0</td>\n",
" <td>90.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick pregnant thyroid_surgery I131_treatment query_hypothyroid query_hyperthyroid lithium goitre tumor hypopituitary psych TSH T3 TT4 T4U FTI Class referral_source_SVHC referral_source_SVHD referral_source_SVI referral_source_other\n",
"0 49.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 93.0 1.0 99.0 1.0 0.0 0.0 0.0 1.0\n",
"1 46.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 3.0 125.0 1.0 122.0 1.0 1.0 0.0 0.0 0.0\n",
"2 80.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 83.0 1.0 76.0 1.0 0.0 0.0 0.0 1.0\n",
"3 80.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 2.0 94.0 1.0 100.0 1.0 1.0 0.0 0.0 0.0\n",
"4 26.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 3.0 108.0 1.0 90.0 1.0 0.0 0.0 0.0 1.0"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert array to df\n",
"new_df = pd.DataFrame(np.round(new_array), columns=df.columns)\n",
"new_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 117,
"id": "633c4895",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"age 0\n",
"sex 0\n",
"on_thyroxine 0\n",
"query_on_thyroxine 0\n",
"on_antithyroid_medication 0\n",
"sick 0\n",
"pregnant 0\n",
"thyroid_surgery 0\n",
"I131_treatment 0\n",
"query_hypothyroid 0\n",
"query_hyperthyroid 0\n",
"lithium 0\n",
"goitre 0\n",
"tumor 0\n",
"hypopituitary 0\n",
"psych 0\n",
"TSH 0\n",
"T3 0\n",
"TT4 0\n",
"T4U 0\n",
"FTI 0\n",
"Class 0\n",
"referral_source_SVHC 0\n",
"referral_source_SVHD 0\n",
"referral_source_SVI 0\n",
"referral_source_other 0\n",
"dtype: int64"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check missing\n",
"new_df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "38c506a8",
"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>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>query_hyperthyroid</th>\n",
" <th>lithium</th>\n",
" <th>goitre</th>\n",
" <th>tumor</th>\n",
" <th>hypopituitary</th>\n",
" <th>psych</th>\n",
" <th>TSH</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>Class</th>\n",
" <th>referral_source_SVHC</th>\n",
" <th>referral_source_SVHD</th>\n",
" <th>referral_source_SVI</th>\n",
" <th>referral_source_other</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" <td>3972.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>51.681017</td>\n",
" <td>0.309416</td>\n",
" <td>0.122860</td>\n",
" <td>0.014099</td>\n",
" <td>0.011581</td>\n",
" <td>0.039275</td>\n",
" <td>0.013847</td>\n",
" <td>0.013847</td>\n",
" <td>0.016616</td>\n",
" <td>0.060171</td>\n",
" <td>0.061934</td>\n",
" <td>0.005035</td>\n",
" <td>0.008812</td>\n",
" <td>0.025176</td>\n",
" <td>0.000252</td>\n",
" <td>0.048338</td>\n",
" <td>4.816717</td>\n",
" <td>2.035247</td>\n",
" <td>108.469789</td>\n",
" <td>1.020393</td>\n",
" <td>110.100453</td>\n",
" <td>0.974824</td>\n",
" <td>0.103474</td>\n",
" <td>0.010070</td>\n",
" <td>0.273414</td>\n",
" <td>0.583333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>21.079318</td>\n",
" <td>0.462311</td>\n",
" <td>0.328318</td>\n",
" <td>0.117913</td>\n",
" <td>0.107004</td>\n",
" <td>0.194273</td>\n",
" <td>0.116870</td>\n",
" <td>0.116870</td>\n",
" <td>0.127845</td>\n",
" <td>0.237834</td>\n",
" <td>0.241065</td>\n",
" <td>0.070790</td>\n",
" <td>0.093468</td>\n",
" <td>0.156680</td>\n",
" <td>0.015867</td>\n",
" <td>0.214507</td>\n",
" <td>23.011349</td>\n",
" <td>0.780502</td>\n",
" <td>34.717478</td>\n",
" <td>0.162878</td>\n",
" <td>31.999075</td>\n",
" <td>0.278266</td>\n",
" <td>0.304616</td>\n",
" <td>0.099858</td>\n",
" <td>0.445768</td>\n",
" <td>0.493069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>36.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>88.000000</td>\n",
" <td>1.000000</td>\n",
" <td>93.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>54.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>104.000000</td>\n",
" <td>1.000000</td>\n",
" <td>107.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>67.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>124.000000</td>\n",
" <td>1.000000</td>\n",
" <td>124.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>455.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>530.000000</td>\n",
" <td>11.000000</td>\n",
" <td>430.000000</td>\n",
" <td>2.000000</td>\n",
" <td>395.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick pregnant thyroid_surgery I131_treatment query_hypothyroid query_hyperthyroid lithium goitre tumor hypopituitary psych TSH T3 TT4 T4U FTI Class referral_source_SVHC referral_source_SVHD referral_source_SVI referral_source_other\n",
"count 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000 3972.000000\n",
"mean 51.681017 0.309416 0.122860 0.014099 0.011581 0.039275 0.013847 0.013847 0.016616 0.060171 0.061934 0.005035 0.008812 0.025176 0.000252 0.048338 4.816717 2.035247 108.469789 1.020393 110.100453 0.974824 0.103474 0.010070 0.273414 0.583333\n",
"std 21.079318 0.462311 0.328318 0.117913 0.107004 0.194273 0.116870 0.116870 0.127845 0.237834 0.241065 0.070790 0.093468 0.156680 0.015867 0.214507 23.011349 0.780502 34.717478 0.162878 31.999075 0.278266 0.304616 0.099858 0.445768 0.493069\n",
"min 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000\n",
"25% 36.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 2.000000 88.000000 1.000000 93.000000 1.000000 0.000000 0.000000 0.000000 0.000000\n",
"50% 54.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 2.000000 104.000000 1.000000 107.000000 1.000000 0.000000 0.000000 0.000000 1.000000\n",
"75% 67.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.000000 2.000000 124.000000 1.000000 124.000000 1.000000 0.000000 0.000000 1.000000 1.000000\n",
"max 455.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 530.000000 11.000000 430.000000 2.000000 395.000000 3.000000 1.000000 1.000000 1.000000 1.000000"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check data\n",
"new_df.describe()"
]
},
{
"cell_type": "markdown",
"id": "9d094f6b",
"metadata": {},
"source": [
"### Handle Outliers\n",
"\n",
"Let's check the distribution of some numeric features."
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "dac2edee",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x720 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create histograms\n",
"cols = ['age','TSH','T3','TT4','T4U','FTI']\n",
"\n",
"plt.figure(figsize=(15,10),facecolor='white')\n",
"plotnumber = 1\n",
"\n",
"for col in cols:\n",
" ax = plt.subplot(2,3,plotnumber)\n",
" sns.histplot(new_df[col], kde=True, ax=ax)\n",
"# sns.boxplot(x=new_df[col], ax=ax)\n",
" plt.xlabel(col, fontsize=10)\n",
" plotnumber+=1"
]
},
{
"cell_type": "markdown",
"id": "a13c3642",
"metadata": {},
"source": [
"> Observation: \n",
" - The distributions of some features look skewed which could be due to the presence of outliers. \n",
" \n",
"Let's create box-plots to determine outliers."
]
},
{
"cell_type": "code",
"execution_count": 120,
"id": "f828c223",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x720 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create boxplots\n",
"cols = ['age','TSH','T3','TT4','T4U','FTI']\n",
"\n",
"plt.figure(figsize=(15,10),facecolor='white')\n",
"plotnumber = 1\n",
"\n",
"for col in cols:\n",
" ax = plt.subplot(2,3,plotnumber)\n",
" sns.boxplot(x=new_df[col])\n",
" plt.xlabel(col, fontsize=10)\n",
" plotnumber+=1"
]
},
{
"cell_type": "markdown",
"id": "e3f0e09e",
"metadata": {},
"source": [
"> Observations: \n",
" - Box plots show outliers in most features. \n",
" - Age cannot be > 400, so we will remove the record. \n",
" - TSH normally range from 0.1-15 however there is no upper limit. A quick google search shows that most labs can measure TSH upto 150 mlU/ml. We will remove records will TSH > 150. \n"
]
},
{
"cell_type": "markdown",
"id": "155a5815",
"metadata": {},
"source": [
"#### Remove recrods with `age > 100` and `TSH > 150`"
]
},
{
"cell_type": "code",
"execution_count": 121,
"id": "82c023c3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Recrods with age > 100\n",
"len(new_df[new_df['age']>100])"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "7d2c55fb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"19"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Recrods with TSH > 150\n",
"len(new_df[new_df['TSH']>150]['TSH'])"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "f67297ed",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(new_df[new_df['age']>100])"
]
},
{
"cell_type": "code",
"execution_count": 124,
"id": "d68b43db",
"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>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>query_hyperthyroid</th>\n",
" <th>lithium</th>\n",
" <th>goitre</th>\n",
" <th>tumor</th>\n",
" <th>hypopituitary</th>\n",
" <th>psych</th>\n",
" <th>TSH</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>Class</th>\n",
" <th>referral_source_SVHC</th>\n",
" <th>referral_source_SVHD</th>\n",
" <th>referral_source_SVI</th>\n",
" <th>referral_source_other</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>288</th>\n",
" <td>39.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>160.0</td>\n",
" <td>0.0</td>\n",
" <td>11.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>316</th>\n",
" <td>50.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>151.0</td>\n",
" <td>1.0</td>\n",
" <td>32.0</td>\n",
" <td>1.0</td>\n",
" <td>28.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>639</th>\n",
" <td>33.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>160.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>1.0</td>\n",
" <td>13.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>937</th>\n",
" <td>53.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>165.0</td>\n",
" <td>0.0</td>\n",
" <td>17.0</td>\n",
" <td>1.0</td>\n",
" <td>14.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1018</th>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>151.0</td>\n",
" <td>1.0</td>\n",
" <td>42.0</td>\n",
" <td>1.0</td>\n",
" <td>39.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1307</th>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>472.0</td>\n",
" <td>2.0</td>\n",
" <td>34.0</td>\n",
" <td>1.0</td>\n",
" <td>29.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1564</th>\n",
" <td>455.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>118.0</td>\n",
" <td>1.0</td>\n",
" <td>104.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1575</th>\n",
" <td>53.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>183.0</td>\n",
" <td>1.0</td>\n",
" <td>14.0</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1902</th>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>183.0</td>\n",
" <td>1.0</td>\n",
" <td>45.0</td>\n",
" <td>1.0</td>\n",
" <td>46.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1972</th>\n",
" <td>25.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>468.0</td>\n",
" <td>1.0</td>\n",
" <td>21.0</td>\n",
" <td>1.0</td>\n",
" <td>19.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2027</th>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>440.0</td>\n",
" <td>0.0</td>\n",
" <td>24.0</td>\n",
" <td>1.0</td>\n",
" <td>18.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2049</th>\n",
" <td>59.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>530.0</td>\n",
" <td>2.0</td>\n",
" <td>10.0</td>\n",
" <td>1.0</td>\n",
" <td>8.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2239</th>\n",
" <td>44.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>199.0</td>\n",
" <td>1.0</td>\n",
" <td>10.0</td>\n",
" <td>1.0</td>\n",
" <td>10.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2259</th>\n",
" <td>31.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>188.0</td>\n",
" <td>1.0</td>\n",
" <td>63.0</td>\n",
" <td>1.0</td>\n",
" <td>53.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2777</th>\n",
" <td>25.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>236.0</td>\n",
" <td>0.0</td>\n",
" <td>16.0</td>\n",
" <td>1.0</td>\n",
" <td>17.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3039</th>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>400.0</td>\n",
" <td>0.0</td>\n",
" <td>11.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3054</th>\n",
" <td>35.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>230.0</td>\n",
" <td>2.0</td>\n",
" <td>36.0</td>\n",
" <td>1.0</td>\n",
" <td>27.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3337</th>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>478.0</td>\n",
" <td>3.0</td>\n",
" <td>45.0</td>\n",
" <td>1.0</td>\n",
" <td>34.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3736</th>\n",
" <td>455.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>118.0</td>\n",
" <td>1.0</td>\n",
" <td>104.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3747</th>\n",
" <td>53.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>183.0</td>\n",
" <td>1.0</td>\n",
" <td>14.0</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3787</th>\n",
" <td>48.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>178.0</td>\n",
" <td>1.0</td>\n",
" <td>63.0</td>\n",
" <td>1.0</td>\n",
" <td>59.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick pregnant thyroid_surgery I131_treatment query_hypothyroid query_hyperthyroid lithium goitre tumor hypopituitary psych TSH T3 TT4 T4U FTI Class referral_source_SVHC referral_source_SVHD referral_source_SVI referral_source_other\n",
"288 39.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 160.0 0.0 11.0 1.0 9.0 2.0 0.0 0.0 0.0 1.0\n",
"316 50.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 151.0 1.0 32.0 1.0 28.0 2.0 0.0 0.0 0.0 1.0\n",
"639 33.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 160.0 0.0 10.0 1.0 13.0 2.0 0.0 0.0 1.0 0.0\n",
"937 53.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 165.0 0.0 17.0 1.0 14.0 2.0 0.0 0.0 0.0 1.0\n",
"1018 60.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 151.0 1.0 42.0 1.0 39.0 2.0 0.0 0.0 0.0 1.0\n",
"1307 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 472.0 2.0 34.0 1.0 29.0 2.0 0.0 0.0 0.0 0.0\n",
"1564 455.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 118.0 1.0 104.0 1.0 0.0 0.0 1.0 0.0\n",
"1575 53.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 183.0 1.0 14.0 1.0 11.0 2.0 0.0 0.0 0.0 1.0\n",
"1902 60.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 183.0 1.0 45.0 1.0 46.0 2.0 0.0 0.0 0.0 1.0\n",
"1972 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 468.0 1.0 21.0 1.0 19.0 2.0 0.0 0.0 0.0 1.0\n",
"2027 18.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 440.0 0.0 24.0 1.0 18.0 2.0 0.0 0.0 0.0 1.0\n",
"2049 59.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 530.0 2.0 10.0 1.0 8.0 2.0 0.0 0.0 0.0 1.0\n",
"2239 44.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 199.0 1.0 10.0 1.0 10.0 2.0 0.0 0.0 1.0 0.0\n",
"2259 31.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 188.0 1.0 63.0 1.0 53.0 2.0 0.0 0.0 0.0 1.0\n",
"2777 25.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 236.0 0.0 16.0 1.0 17.0 2.0 0.0 0.0 0.0 1.0\n",
"3039 60.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 400.0 0.0 11.0 1.0 9.0 2.0 0.0 0.0 0.0 1.0\n",
"3054 35.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 230.0 2.0 36.0 1.0 27.0 2.0 0.0 0.0 0.0 1.0\n",
"3337 18.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 478.0 3.0 45.0 1.0 34.0 2.0 0.0 0.0 0.0 1.0\n",
"3736 455.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 118.0 1.0 104.0 1.0 0.0 0.0 1.0 0.0\n",
"3747 53.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 183.0 1.0 14.0 1.0 11.0 2.0 0.0 0.0 0.0 1.0\n",
"3787 48.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 178.0 1.0 63.0 1.0 59.0 2.0 0.0 0.0 0.0 1.0"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df[(new_df['age']>100) | (new_df['TSH']>150)]"
]
},
{
"cell_type": "code",
"execution_count": 125,
"id": "b4dd1a89",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3951, 26)"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Subset data\n",
"df2 = new_df[(new_df['age']<=100) & (new_df['TSH']<=150)]\n",
"df2.shape"
]
},
{
"cell_type": "code",
"execution_count": 126,
"id": "80160a9e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x720 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create histograms\n",
"cols = ['age','TSH','T3','TT4','T4U','FTI']\n",
"\n",
"plt.figure(figsize=(15,10),facecolor='white')\n",
"plotnumber = 1\n",
"\n",
"for col in cols:\n",
" ax = plt.subplot(2,3,plotnumber)\n",
" sns.histplot(df2[col])\n",
" plt.xlabel(col, fontsize=10)\n",
" plotnumber+=1"
]
},
{
"cell_type": "markdown",
"id": "8cf8a2a3",
"metadata": {},
"source": [
"> Observations: \n",
" - Removing outlier from `age` has significantly improved the distribution and it looks normal. \n",
" - Removing outlier from `TSH` has also improved the distribution a little but it is still heavilty skewed to the right. \n",
" - Similarly, other features also seem to be skewed to the right.\n",
" \n",
"Let's apply some transforations to the data to see if the data can be normally distributed."
]
},
{
"cell_type": "markdown",
"id": "de7f4dda",
"metadata": {},
"source": [
"### Data Transformation\n",
"\n",
"Apply some transforations to the data to see if the data can be normally distributed."
]
},
{
"cell_type": "markdown",
"id": "28bd9b60",
"metadata": {},
"source": [
"#### Apply log transformation"
]
},
{
"cell_type": "code",
"execution_count": 127,
"id": "50708b01",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x720 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create histograms\n",
"cols = ['age','TSH','T3','TT4','T4U','FTI']\n",
"\n",
"plt.figure(figsize=(15,10),facecolor='white')\n",
"plotnumber = 1\n",
"\n",
"for col in cols:\n",
" ax = plt.subplot(2,3,plotnumber)\n",
" sns.histplot(np.log(df2[col]+1e-8))\n",
" plt.xlabel(col, fontsize=10)\n",
" plotnumber+=1"
]
},
{
"cell_type": "markdown",
"id": "5bd6578a",
"metadata": {},
"source": [
"> The data seems to be skewed to the left after applyting log transformation."
]
},
{
"cell_type": "markdown",
"id": "794ea9f4",
"metadata": {},
"source": [
"#### Apply Box-cox tranformation"
]
},
{
"cell_type": "code",
"execution_count": 128,
"id": "ea90b467",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x720 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create histograms\n",
"cols = ['age','TSH','T3','TT4','T4U','FTI']\n",
"\n",
"plt.figure(figsize=(15,10),facecolor='white')\n",
"plotnumber = 1\n",
"param=[]\n",
"for col in cols:\n",
" ax = plt.subplot(2,3,plotnumber)\n",
" col_bx,p = stats.boxcox(df2[col]+1e-8)\n",
" sns.histplot(col_bx)\n",
" param.append(p)\n",
" plt.xlabel(col, fontsize=10)\n",
" plotnumber+=1"
]
},
{
"cell_type": "code",
"execution_count": 129,
"id": "15b27f01",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1.0550222277705679,\n",
" 0.1537593101418859,\n",
" 0.62709777401318,\n",
" 0.6048130750519881,\n",
" 0.7376933654702804,\n",
" 0.656094585046508]"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Transformation parameters for eahc feature\n",
"param"
]
},
{
"cell_type": "markdown",
"id": "3682c66e",
"metadata": {},
"source": [
"> Box Cox transformation does a good job of bringing the features close to a Normal distribution."
]
},
{
"cell_type": "markdown",
"id": "2f854b4c",
"metadata": {},
"source": [
"### Distribution of `Class`"
]
},
{
"cell_type": "markdown",
"id": "1290b78e",
"metadata": {},
"source": [
"In this section, we will check the distribution of our dependent variable `Class`. We will aso use oversampling technqiue to handle imbalanced class."
]
},
{
"cell_type": "code",
"execution_count": 130,
"id": "846d55b8",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Check distribution of cateogircal `Class` feature\n",
"sns.countplot(x='Class', data=df2);"
]
},
{
"cell_type": "markdown",
"id": "8be15e0e",
"metadata": {},
"source": [
"> The data is highly imbalanced with majority of data represented by Class = 1. \n",
"\n",
"Let's oversample the data."
]
},
{
"cell_type": "markdown",
"id": "ab4b8b3f",
"metadata": {},
"source": [
"#### Oversample using `RandomOverSampler()`"
]
},
{
"cell_type": "code",
"execution_count": 131,
"id": "108a5edd",
"metadata": {},
"outputs": [],
"source": [
"x = df2.drop(['Class'], axis=1)\n",
"y = df2['Class']\n",
"\n",
"sampler = RandomOverSampler()\n",
"x_sampled, y_sampled = sampler.fit_resample(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"id": "9be7e980",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(14664, 25)"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check shape\n",
"x_sampled.shape"
]
},
{
"cell_type": "code",
"execution_count": 133,
"id": "ceef8fcd",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" FutureWarning\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Check data for each category\n",
"sns.countplot(y_sampled);"
]
},
{
"cell_type": "code",
"execution_count": 134,
"id": "330de3e9",
"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>age</th>\n",
" <th>sex</th>\n",
" <th>on_thyroxine</th>\n",
" <th>query_on_thyroxine</th>\n",
" <th>on_antithyroid_medication</th>\n",
" <th>sick</th>\n",
" <th>pregnant</th>\n",
" <th>thyroid_surgery</th>\n",
" <th>I131_treatment</th>\n",
" <th>query_hypothyroid</th>\n",
" <th>query_hyperthyroid</th>\n",
" <th>lithium</th>\n",
" <th>goitre</th>\n",
" <th>tumor</th>\n",
" <th>hypopituitary</th>\n",
" <th>psych</th>\n",
" <th>TSH</th>\n",
" <th>T3</th>\n",
" <th>TT4</th>\n",
" <th>T4U</th>\n",
" <th>FTI</th>\n",
" <th>referral_source_SVHC</th>\n",
" <th>referral_source_SVHD</th>\n",
" <th>referral_source_SVI</th>\n",
" <th>referral_source_other</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>49.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>93.0</td>\n",
" <td>1.0</td>\n",
" <td>99.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>46.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>125.0</td>\n",
" <td>1.0</td>\n",
" <td>122.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>83.0</td>\n",
" <td>1.0</td>\n",
" <td>76.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>94.0</td>\n",
" <td>1.0</td>\n",
" <td>100.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>26.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>108.0</td>\n",
" <td>1.0</td>\n",
" <td>90.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick pregnant thyroid_surgery I131_treatment query_hypothyroid query_hyperthyroid lithium goitre tumor hypopituitary psych TSH T3 TT4 T4U FTI referral_source_SVHC referral_source_SVHD referral_source_SVI referral_source_other\n",
"0 49.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 93.0 1.0 99.0 0.0 0.0 0.0 1.0\n",
"1 46.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 3.0 125.0 1.0 122.0 1.0 0.0 0.0 0.0\n",
"2 80.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 83.0 1.0 76.0 0.0 0.0 0.0 1.0\n",
"3 80.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 2.0 94.0 1.0 100.0 1.0 0.0 0.0 0.0\n",
"4 26.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 3.0 108.0 1.0 90.0 0.0 0.0 0.0 1.0"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create df\n",
"x_sampled = pd.DataFrame(x_sampled, columns=x.columns)\n",
"x_sampled.head()"
]
},
{
"cell_type": "markdown",
"id": "49c4c76a",
"metadata": {},
"source": [
"## Clustering"
]
},
{
"cell_type": "markdown",
"id": "144e3f22",
"metadata": {},
"source": [
"Here, we determine the best number of clusters in which the data can be split into."
]
},
{
"cell_type": "code",
"execution_count": 137,
"id": "084444c3",
"metadata": {},
"outputs": [],
"source": [
"wcss = []\n",
"for i in range(1,10):\n",
" kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
" pred = kmeans.fit(x_sampled)\n",
" wcss.append(pred.inertia_)"
]
},
{
"cell_type": "code",
"execution_count": 140,
"id": "7c841029",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fe968e19898>]"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(range(1,10), wcss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8847dd77",
"metadata": {},
"outputs": [],
"source": [
"kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
"pred = kmeans.fit(x_sampled)"
]
},
{
"cell_type": "markdown",
"id": "e942543f",
"metadata": {},
"source": [
"## Modeling"
]
},
{
"cell_type": "markdown",
"id": "5e89b915",
"metadata": {},
"source": [
"This section shows 2/4 modelling techniques used to model the training data. Refer to the application for full details on various models and hypter-parameter tuning performed."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5a552678",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6127, 28)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tdf = pd.read_csv('test_cluster')\n",
"tdf.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1fd292fd",
"metadata": {},
"outputs": [],
"source": [
"tdf.drop(['Cluster','Unnamed: 0'], inplace=True, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b6019c72",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['age', 'sex', 'on_thyroxine', 'query_on_thyroxine', 'on_antithyroid_medication', 'sick', 'pregnant', 'thyroid_surgery', 'I131_treatment', 'query_hypothyroid', 'query_hyperthyroid', 'lithium', 'goitre', 'tumor', 'hypopituitary', 'psych', 'TSH', 'T3', 'TT4', 'T4U', 'FTI', 'referral_source_SVHC', 'referral_source_SVHD', 'referral_source_SVI', 'referral_source_other', 'Label'], dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tdf.columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5e08142f",
"metadata": {},
"outputs": [],
"source": [
"X = tdf.drop('Label', axis=1)\n",
"Y = tdf['Label']\n",
"x_train, x_test, y_train, y_test = train_test_split(X, Y,\n",
" test_size=1/3, random_state=101)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8c0ff050",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4084,)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b7272f34",
"metadata": {},
"outputs": [],
"source": [
"from xgboost import XGBClassifier\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.metrics import roc_auc_score"
]
},
{
"cell_type": "markdown",
"id": "167c8fc6",
"metadata": {},
"source": [
"### XGB"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d7002903",
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 64 candidates, totalling 320 fits\n",
"[18:19:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.991 total time= 0.1s\n",
"[18:19:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.990 total time= 0.1s\n",
"[18:19:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:19:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:19:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.996 total time= 0.0s\n",
"[18:19:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.994 total time= 0.1s\n",
"[18:19:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.993 total time= 0.1s\n",
"[18:19:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.995 total time= 0.1s\n",
"[18:19:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.994 total time= 0.1s\n",
"[18:19:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.998 total time= 0.1s\n",
"[18:19:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.995 total time= 0.2s\n",
"[18:19:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.991 total time= 0.2s\n",
"[18:19:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.995 total time= 0.2s\n",
"[18:19:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.994 total time= 0.2s\n",
"[18:19:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.998 total time= 0.2s\n",
"[18:19:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.995 total time= 0.3s\n",
"[18:19:43] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.991 total time= 0.3s\n",
"[18:19:43] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.995 total time= 0.4s\n",
"[18:19:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.994 total time= 0.4s\n",
"[18:19:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.996 total time= 0.3s\n",
"[18:19:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:19:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.990 total time= 0.0s\n",
"[18:19:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:19:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:19:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.996 total time= 0.0s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:19:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:19:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.991 total time= 0.1s\n",
"[18:19:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.994 total time= 0.1s\n",
"[18:19:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.994 total time= 0.1s\n",
"[18:19:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.998 total time= 0.1s\n",
"[18:19:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.994 total time= 0.2s\n",
"[18:19:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.991 total time= 0.2s\n",
"[18:19:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.993 total time= 0.2s\n",
"[18:19:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.994 total time= 0.2s\n",
"[18:19:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.998 total time= 0.3s\n",
"[18:19:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.994 total time= 0.3s\n",
"[18:19:47] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.991 total time= 0.4s\n",
"[18:19:47] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.993 total time= 0.4s\n",
"[18:19:47] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.994 total time= 0.4s\n",
"[18:19:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.996 total time= 0.3s\n",
"[18:19:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:19:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.991 total time= 0.0s\n",
"[18:19:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:19:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:19:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.999 total time= 0.1s\n",
"[18:19:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.994 total time= 0.1s\n",
"[18:19:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.991 total time= 0.1s\n",
"[18:19:49] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.995 total time= 0.1s\n",
"[18:19:49] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.995 total time= 0.1s\n",
"[18:19:49] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.999 total time= 0.1s\n",
"[18:19:49] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.994 total time= 0.2s\n",
"[18:19:49] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.990 total time= 0.2s\n",
"[18:19:49] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.995 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:19:50] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.995 total time= 0.2s\n",
"[18:19:50] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.999 total time= 0.2s\n",
"[18:19:50] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.994 total time= 0.3s\n",
"[18:19:50] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.991 total time= 0.3s\n",
"[18:19:51] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.995 total time= 0.4s\n",
"[18:19:51] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.995 total time= 0.4s\n",
"[18:19:51] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.999 total time= 0.3s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.991 total time= 0.0s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.995 total time= 0.0s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.999 total time= 0.0s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.994 total time= 0.1s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.991 total time= 0.1s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.995 total time= 0.1s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.995 total time= 0.1s\n",
"[18:19:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.999 total time= 0.1s\n",
"[18:19:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.994 total time= 0.2s\n",
"[18:19:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.990 total time= 0.2s\n",
"[18:19:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.995 total time= 0.2s\n",
"[18:19:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.995 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:19:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.999 total time= 0.2s\n",
"[18:19:54] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.994 total time= 0.4s\n",
"[18:19:54] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.990 total time= 0.3s\n",
"[18:19:54] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.995 total time= 0.4s\n",
"[18:19:55] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.995 total time= 0.4s\n",
"[18:19:55] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.999 total time= 0.4s\n",
"[18:19:55] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=0.987 total time= 0.0s\n",
"[18:19:55] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=0.980 total time= 0.0s\n",
"[18:19:55] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=0.987 total time= 0.0s\n",
"[18:19:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=0.987 total time= 0.0s\n",
"[18:19:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:19:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.991 total time= 0.1s\n",
"[18:19:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.990 total time= 0.1s\n",
"[18:19:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.994 total time= 0.1s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:19:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.993 total time= 0.1s\n",
"[18:19:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.996 total time= 0.1s\n",
"[18:19:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.991 total time= 0.2s\n",
"[18:19:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.991 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:19:57] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.993 total time= 0.2s\n",
"[18:19:57] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.993 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:19:57] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.996 total time= 0.2s\n",
"[18:19:57] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.993 total time= 0.4s\n",
"[18:19:58] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.991 total time= 0.4s\n",
"[18:19:58] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.994 total time= 0.4s\n",
"[18:19:58] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.994 total time= 0.4s\n",
"[18:19:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.998 total time= 0.4s\n",
"[18:19:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=0.991 total time= 0.0s\n",
"[18:19:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:19:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:19:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:19:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=0.996 total time= 0.0s\n",
"[18:19:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.991 total time= 0.2s\n",
"[18:19:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.991 total time= 0.1s\n",
"[18:20:00] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.994 total time= 0.2s\n",
"[18:20:00] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.993 total time= 0.1s\n",
"[18:20:00] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.996 total time= 0.2s\n",
"[18:20:00] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.993 total time= 0.3s\n",
"[18:20:00] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.991 total time= 0.3s\n",
"[18:20:01] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.994 total time= 0.3s\n",
"[18:20:01] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.994 total time= 0.3s\n",
"[18:20:01] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.996 total time= 0.3s\n",
"[18:20:01] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.993 total time= 0.5s\n",
"[18:20:02] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.991 total time= 0.5s\n",
"[18:20:02] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.994 total time= 0.5s\n",
"[18:20:03] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.994 total time= 0.5s\n",
"[18:20:03] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.998 total time= 0.5s\n",
"[18:20:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:20:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=0.996 total time= 0.0s\n",
"[18:20:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.991 total time= 0.2s\n",
"[18:20:05] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.994 total time= 0.2s\n",
"[18:20:05] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.994 total time= 0.2s\n",
"[18:20:05] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.998 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:20:05] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.993 total time= 0.3s\n",
"[18:20:05] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.991 total time= 0.3s\n",
"[18:20:06] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.995 total time= 0.3s\n",
"[18:20:06] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.994 total time= 0.4s\n",
"[18:20:07] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.999 total time= 0.3s\n",
"[18:20:07] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.994 total time= 0.5s\n",
"[18:20:07] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.993 total time= 0.5s\n",
"[18:20:08] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.995 total time= 0.6s\n",
"[18:20:09] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.995 total time= 0.6s\n",
"[18:20:09] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.999 total time= 0.6s\n",
"[18:20:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=0.993 total time= 0.1s\n",
"[18:20:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:20:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=0.996 total time= 0.1s\n",
"[18:20:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.994 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:20:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:10] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.994 total time= 0.2s\n",
"[18:20:11] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.994 total time= 0.2s\n",
"[18:20:11] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.999 total time= 0.3s\n",
"[18:20:11] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.994 total time= 0.4s\n",
"[18:20:11] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.990 total time= 0.4s\n",
"[18:20:12] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.995 total time= 0.4s\n",
"[18:20:12] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.994 total time= 0.4s\n",
"[18:20:13] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.999 total time= 0.4s\n",
"[18:20:13] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.994 total time= 0.5s\n",
"[18:20:13] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.991 total time= 0.6s\n",
"[18:20:14] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.995 total time= 0.6s\n",
"[18:20:15] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.994 total time= 0.6s\n",
"[18:20:15] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.999 total time= 0.6s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=0.985 total time= 0.0s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=0.980 total time= 0.0s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=0.985 total time= 0.0s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=0.984 total time= 0.0s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.985 total time= 0.1s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.980 total time= 0.1s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.985 total time= 0.1s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.984 total time= 0.1s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.993 total time= 0.1s\n",
"[18:20:16] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.987 total time= 0.2s\n",
"[18:20:17] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.980 total time= 0.2s\n",
"[18:20:17] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.987 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:20:17] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.987 total time= 0.2s\n",
"[18:20:17] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.993 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:20:17] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.991 total time= 0.5s\n",
"[18:20:18] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.990 total time= 0.4s\n",
"[18:20:18] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.994 total time= 0.4s\n",
"[18:20:19] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.993 total time= 0.4s\n",
"[18:20:19] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.996 total time= 0.4s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=0.991 total time= 0.0s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=0.987 total time= 0.0s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=0.996 total time= 0.0s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.991 total time= 0.2s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.993 total time= 0.1s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.994 total time= 0.2s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.993 total time= 0.1s\n",
"[18:20:20] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.996 total time= 0.2s\n",
"[18:20:21] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.991 total time= 0.3s\n",
"[18:20:21] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.993 total time= 0.3s\n",
"[18:20:21] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.994 total time= 0.3s\n",
"[18:20:21] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.993 total time= 0.3s\n",
"[18:20:22] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.996 total time= 0.3s\n",
"[18:20:22] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.991 total time= 0.5s\n",
"[18:20:23] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.993 total time= 0.6s\n",
"[18:20:23] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.994 total time= 0.5s\n",
"[18:20:24] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.993 total time= 0.6s\n",
"[18:20:24] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.996 total time= 0.5s\n",
"[18:20:25] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=0.991 total time= 0.0s\n",
"[18:20:25] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=0.993 total time= 0.1s\n",
"[18:20:25] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:20:25] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:25] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=0.996 total time= 0.0s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:20:25] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:25] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:25] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=0.994 total time= 0.2s\n",
"[18:20:26] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:26] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=0.996 total time= 0.2s\n",
"[18:20:26] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=0.993 total time= 0.3s\n",
"[18:20:26] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=0.993 total time= 0.4s\n",
"[18:20:27] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=0.994 total time= 0.4s\n",
"[18:20:27] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=0.993 total time= 0.4s\n",
"[18:20:27] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=0.996 total time= 0.4s\n",
"[18:20:28] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=0.993 total time= 0.8s\n",
"[18:20:28] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=0.993 total time= 0.8s\n",
"[18:20:29] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=0.994 total time= 0.9s\n",
"[18:20:30] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=0.994 total time= 0.9s\n",
"[18:20:31] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=0.996 total time= 0.9s\n",
"[18:20:32] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=0.991 total time= 0.0s\n",
"[18:20:32] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:32] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:20:32] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:32] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=0.996 total time= 0.0s\n",
"[18:20:32] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:32] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:33] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=0.994 total time= 0.2s\n",
"[18:20:33] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:33] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=0.996 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:20:33] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=0.993 total time= 0.4s\n",
"[18:20:34] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=0.993 total time= 0.5s\n",
"[18:20:34] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=0.994 total time= 0.4s\n",
"[18:20:34] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=0.993 total time= 0.7s\n",
"[18:20:35] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=0.996 total time= 0.6s\n",
"[18:20:36] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=0.994 total time= 0.9s\n",
"[18:20:37] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=0.993 total time= 1.0s\n",
"[18:20:38] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=0.994 total time= 0.9s\n",
"[18:20:38] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=0.994 total time= 1.0s\n",
"[18:20:39] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=0.996 total time= 1.0s\n",
"[18:20:40] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=0.985 total time= 0.0s\n",
"[18:20:40] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=0.980 total time= 0.0s\n",
"[18:20:40] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=0.985 total time= 0.0s\n",
"[18:20:40] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=0.984 total time= 0.0s\n",
"[18:20:40] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.985 total time= 0.1s\n",
"[18:20:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.980 total time= 0.1s\n",
"[18:20:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.985 total time= 0.1s\n",
"[18:20:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.984 total time= 0.1s\n",
"[18:20:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.993 total time= 0.1s\n",
"[18:20:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.985 total time= 0.3s\n",
"[18:20:41] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.980 total time= 0.3s\n",
"[18:20:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.985 total time= 0.2s\n",
"[18:20:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.984 total time= 0.3s\n",
"[18:20:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.993 total time= 0.3s\n",
"[18:20:42] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=0.985 total time= 0.5s\n",
"[18:20:43] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=0.980 total time= 0.5s\n",
"[18:20:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=0.985 total time= 0.6s\n",
"[18:20:44] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=0.984 total time= 0.6s\n",
"[18:20:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=0.993 total time= 0.5s\n",
"[18:20:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=0.991 total time= 0.0s\n",
"[18:20:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=0.987 total time= 0.0s\n",
"[18:20:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:45] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=0.996 total time= 0.1s\n",
"[18:20:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.991 total time= 0.2s\n",
"[18:20:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.987 total time= 0.1s\n",
"[18:20:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.996 total time= 0.2s\n",
"[18:20:46] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.991 total time= 0.3s\n",
"[18:20:47] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.993 total time= 0.3s\n",
"[18:20:47] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.987 total time= 0.3s\n",
"[18:20:47] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.993 total time= 0.3s\n",
"[18:20:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.996 total time= 0.3s\n",
"[18:20:48] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=0.991 total time= 0.7s\n",
"[18:20:49] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=0.993 total time= 0.7s\n",
"[18:20:49] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=0.987 total time= 0.7s\n",
"[18:20:50] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=0.993 total time= 0.7s\n",
"[18:20:51] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=0.996 total time= 0.8s\n",
"[18:20:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=0.991 total time= 0.0s\n",
"[18:20:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=0.993 total time= 0.1s\n",
"[18:20:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=0.994 total time= 0.1s\n",
"[18:20:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=0.993 total time= 0.1s\n",
"[18:20:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=0.996 total time= 0.1s\n",
"[18:20:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=0.991 total time= 0.2s\n",
"[18:20:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:52] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=0.994 total time= 0.2s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:20:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=0.996 total time= 0.2s\n",
"[18:20:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=0.991 total time= 0.4s\n",
"[18:20:53] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=0.993 total time= 0.5s\n",
"[18:20:54] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=0.994 total time= 0.4s\n",
"[18:20:54] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=0.993 total time= 0.4s\n",
"[18:20:55] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=0.996 total time= 0.5s\n",
"[18:20:55] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=0.991 total time= 0.7s\n",
"[18:20:56] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=0.993 total time= 0.9s\n",
"[18:20:57] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=0.994 total time= 0.7s\n",
"[18:20:57] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=0.993 total time= 0.6s\n",
"[18:20:58] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=0.996 total time= 0.7s\n",
"[18:20:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=0.991 total time= 0.0s\n",
"[18:20:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 2/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=0.994 total time= 0.0s\n",
"[18:20:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 4/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=0.993 total time= 0.0s\n",
"[18:20:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=0.996 total time= 0.0s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:20:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 1/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=0.991 total time= 0.2s\n",
"[18:20:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:20:59] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 3/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=0.994 total time= 0.2s\n",
"[18:21:00] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=0.993 total time= 0.2s\n",
"[18:21:00] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
"[CV 5/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=0.996 total time= 0.2s\n",
"[18:21:00] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=0.991 total time= 0.3s\n",
"[18:21:00] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=0.993 total time= 0.4s\n",
"[18:21:01] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=0.994 total time= 0.3s\n",
"[18:21:01] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=0.993 total time= 0.3s\n",
"[18:21:01] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=0.996 total time= 0.4s\n",
"[18:21:02] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 1/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=0.991 total time= 0.6s\n",
"[18:21:02] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 2/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=0.993 total time= 0.7s\n",
"[18:21:03] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 3/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=0.994 total time= 0.6s\n",
"[18:21:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 4/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=0.993 total time= 0.6s\n",
"[18:21:04] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV 5/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=0.996 total time= 0.7s\n",
"[18:21:05] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mohi9282/opt/anaconda3/envs/pytorchenv/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
" warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 15min 33s, sys: 15.8 s, total: 15min 49s\n",
"Wall time: 1min 24s\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(cv=5,\n",
" estimator=XGBClassifier(base_score=None, booster=None,\n",
" colsample_bylevel=None,\n",
" colsample_bynode=None,\n",
" colsample_bytree=None, gamma=None,\n",
" gpu_id=None, importance_type='gain',\n",
" interaction_constraints=None,\n",
" learning_rate=None, max_delta_step=None,\n",
" max_depth=None, min_child_weight=None,\n",
" missing=nan, monotone_constraints=None,\n",
" n_estimators=100, n_jobs=None,\n",
" num_parallel_tree=None,\n",
" objective='multi:softmax',\n",
" random_state=None, reg_alpha=None,\n",
" reg_lambda=None, scale_pos_weight=None,\n",
" subsample=None, tree_method=None,\n",
" validate_parameters=None, verbosity=None),\n",
" param_grid={'learning_rate': [0.5, 0.1, 0.01, 0.001],\n",
" 'max_depth': [3, 5, 10, 20],\n",
" 'n_estimators': [10, 50, 100, 200]},\n",
" verbose=3)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"xgb = XGBClassifier(objective='multi:softmax')\n",
"param_grid = {'learning_rate': [0.5, 0.1, 0.01, 0.001],\n",
" 'max_depth': [3, 5, 10, 20],\n",
" 'n_estimators': [10, 50, 100, 200]}\n",
"\n",
"grid_search = GridSearchCV(estimator=xgb, param_grid=param_grid,\n",
" cv=5, verbose=3)\n",
"grid_search.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "2c3b0bb0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'learning_rate': 0.1, 'max_depth': 10, 'n_estimators': 200}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_search.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "7fb8ee90",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:22:38] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
]
},
{
"data": {
"text/plain": [
"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
" colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
" importance_type='gain', interaction_constraints='',\n",
" learning_rate=0.1, max_delta_step=0, max_depth=10,\n",
" min_child_weight=1, missing=nan, monotone_constraints='()',\n",
" n_estimators=200, n_jobs=12, num_parallel_tree=1,\n",
" objective='multi:softprob', random_state=0, reg_alpha=0,\n",
" reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
" tree_method='exact', validate_parameters=1, verbosity=None)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xgb_model = XGBClassifier(max_depth=10, learning_rate=0.1,\n",
" n_estimators=200)\n",
"xgb_model.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "9f2381b1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9999967572787911"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred_xgb = xgb_model.predict_proba(x_test)\n",
"roc_auc_score(y_test, pred_xgb, multi_class='ovr')"
]
},
{
"cell_type": "markdown",
"id": "f9a00f25",
"metadata": {},
"source": [
"### SVM"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "644b55bb",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.svm import SVC"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "89268ccf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
"[CV 1/5] END ..................C=0.1, gamma=0.1;, score=0.869 total time= 4.2s\n",
"[CV 2/5] END ..................C=0.1, gamma=0.1;, score=0.849 total time= 4.1s\n",
"[CV 3/5] END ..................C=0.1, gamma=0.1;, score=0.846 total time= 4.0s\n",
"[CV 4/5] END ..................C=0.1, gamma=0.1;, score=0.865 total time= 4.2s\n",
"[CV 5/5] END ..................C=0.1, gamma=0.1;, score=0.884 total time= 4.2s\n",
"[CV 1/5] END .................C=0.1, gamma=0.01;, score=0.971 total time= 2.8s\n",
"[CV 2/5] END .................C=0.1, gamma=0.01;, score=0.977 total time= 2.9s\n",
"[CV 3/5] END .................C=0.1, gamma=0.01;, score=0.977 total time= 2.7s\n",
"[CV 4/5] END .................C=0.1, gamma=0.01;, score=0.976 total time= 2.7s\n",
"[CV 5/5] END .................C=0.1, gamma=0.01;, score=0.982 total time= 2.9s\n",
"[CV 1/5] END ................C=0.1, gamma=0.001;, score=0.955 total time= 2.0s\n",
"[CV 2/5] END ................C=0.1, gamma=0.001;, score=0.957 total time= 1.9s\n",
"[CV 3/5] END ................C=0.1, gamma=0.001;, score=0.963 total time= 1.8s\n",
"[CV 4/5] END ................C=0.1, gamma=0.001;, score=0.944 total time= 1.8s\n",
"[CV 5/5] END ................C=0.1, gamma=0.001;, score=0.962 total time= 1.8s\n",
"[CV 1/5] END ....................C=1, gamma=0.1;, score=1.000 total time= 3.0s\n",
"[CV 2/5] END ....................C=1, gamma=0.1;, score=1.000 total time= 3.0s\n",
"[CV 3/5] END ....................C=1, gamma=0.1;, score=1.000 total time= 3.0s\n",
"[CV 4/5] END ....................C=1, gamma=0.1;, score=1.000 total time= 3.0s\n",
"[CV 5/5] END ....................C=1, gamma=0.1;, score=1.000 total time= 3.0s\n",
"[CV 1/5] END ...................C=1, gamma=0.01;, score=0.993 total time= 1.5s\n",
"[CV 2/5] END ...................C=1, gamma=0.01;, score=0.996 total time= 1.5s\n",
"[CV 3/5] END ...................C=1, gamma=0.01;, score=0.982 total time= 1.4s\n",
"[CV 4/5] END ...................C=1, gamma=0.01;, score=0.989 total time= 1.4s\n",
"[CV 5/5] END ...................C=1, gamma=0.01;, score=0.979 total time= 1.5s\n",
"[CV 1/5] END ..................C=1, gamma=0.001;, score=0.972 total time= 0.9s\n",
"[CV 2/5] END ..................C=1, gamma=0.001;, score=0.976 total time= 0.9s\n",
"[CV 3/5] END ..................C=1, gamma=0.001;, score=0.972 total time= 0.9s\n",
"[CV 4/5] END ..................C=1, gamma=0.001;, score=0.966 total time= 0.8s\n",
"[CV 5/5] END ..................C=1, gamma=0.001;, score=0.971 total time= 0.9s\n",
"[CV 1/5] END ...................C=10, gamma=0.1;, score=1.000 total time= 2.8s\n",
"[CV 2/5] END ...................C=10, gamma=0.1;, score=1.000 total time= 3.2s\n",
"[CV 3/5] END ...................C=10, gamma=0.1;, score=1.000 total time= 2.9s\n",
"[CV 4/5] END ...................C=10, gamma=0.1;, score=1.000 total time= 2.8s\n",
"[CV 5/5] END ...................C=10, gamma=0.1;, score=1.000 total time= 2.8s\n",
"[CV 1/5] END ..................C=10, gamma=0.01;, score=0.991 total time= 1.2s\n",
"[CV 2/5] END ..................C=10, gamma=0.01;, score=0.998 total time= 1.2s\n",
"[CV 3/5] END ..................C=10, gamma=0.01;, score=0.990 total time= 1.2s\n",
"[CV 4/5] END ..................C=10, gamma=0.01;, score=0.991 total time= 1.2s\n",
"[CV 5/5] END ..................C=10, gamma=0.01;, score=0.993 total time= 1.2s\n",
"[CV 1/5] END .................C=10, gamma=0.001;, score=0.979 total time= 0.5s\n",
"[CV 2/5] END .................C=10, gamma=0.001;, score=0.988 total time= 0.5s\n",
"[CV 3/5] END .................C=10, gamma=0.001;, score=0.978 total time= 0.5s\n",
"[CV 4/5] END .................C=10, gamma=0.001;, score=0.973 total time= 0.5s\n",
"[CV 5/5] END .................C=10, gamma=0.001;, score=0.973 total time= 0.5s\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, estimator=SVC(probability=True),\n",
" param_grid={'C': [0.1, 1, 10], 'gamma': [0.1, 0.01, 0.001]},\n",
" verbose=3)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svc = SVC(kernel='rbf', probability=True)\n",
"param_grid = {'C': [0.1,1, 10], 'gamma': [0.1,0.01,0.001]}\n",
"# param_grid = {'C': [0.1], 'gamma': [0.1],'kernel': ['rbf', 'poly']}\n",
"grid_search = GridSearchCV(estimator=svc, param_grid=param_grid,\n",
" cv=5, verbose=3)\n",
"grid_search.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "5daa90a8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'C': 1, 'gamma': 0.1}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_search.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "24be7002",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=1, gamma=0.1, probability=True)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svm_model = SVC(kernel='rbf', probability=True,\n",
" C=1, gamma=0.1)\n",
"svm_model.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "3e5bfeeb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred_svm = svm_model.predict_proba(x_test)\n",
"roc_auc_score(y_test, pred_svm, multi_class='ovr')"
]
},
{
"cell_type": "markdown",
"id": "18912a7f",
"metadata": {},
"source": [
"-- End of Exploration --"
]
}
],
"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": "3.7.3"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": true,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": true
}
},
"nbformat": 4,
"nbformat_minor": 5
}