s440058/duma_projekt
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
master
duma_projekt/Projekt.ipynb

2227 lines
3.5 MiB
Plaintext
Raw Permalink Normal View History

add project
2021-06-20 18:35:43 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import plotly.graph_objects as go\n",
"from plotly.subplots import make_subplots\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn import metrics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Przygotowanie danych:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 454 entries, 0 to 498\n",
"Data columns (total 11 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 gender 454 non-null object \n",
" 1 age 454 non-null float64\n",
" 2 hypertension 454 non-null int64 \n",
" 3 heart_disease 454 non-null int64 \n",
" 4 ever_married 454 non-null object \n",
" 5 work_type 454 non-null object \n",
" 6 Residence_type 454 non-null object \n",
" 7 avg_glucose_level 454 non-null float64\n",
" 8 bmi 454 non-null float64\n",
" 9 smoking_status 454 non-null object \n",
" 10 stroke 454 non-null int64 \n",
"dtypes: float64(3), int64(3), object(5)\n",
"memory usage: 42.6+ KB\n"
]
},
{
"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>gender</th>\n",
" <th>age</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>ever_married</th>\n",
" <th>work_type</th>\n",
" <th>Residence_type</th>\n",
" <th>avg_glucose_level</th>\n",
" <th>bmi</th>\n",
" <th>smoking_status</th>\n",
" <th>stroke</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Male</td>\n",
" <td>67.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>228.69</td>\n",
" <td>36.6</td>\n",
" <td>formerly smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Male</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Rural</td>\n",
" <td>105.92</td>\n",
" <td>32.5</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Female</td>\n",
" <td>49.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>171.23</td>\n",
" <td>34.4</td>\n",
" <td>smokes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Female</td>\n",
" <td>79.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Self-employed</td>\n",
" <td>Rural</td>\n",
" <td>174.12</td>\n",
" <td>24.0</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Male</td>\n",
" <td>81.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>186.21</td>\n",
" <td>29.0</td>\n",
" <td>formerly smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>494</th>\n",
" <td>Female</td>\n",
" <td>55.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Rural</td>\n",
" <td>111.19</td>\n",
" <td>39.7</td>\n",
" <td>formerly smoked</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495</th>\n",
" <td>Female</td>\n",
" <td>71.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>93.28</td>\n",
" <td>34.7</td>\n",
" <td>never smoked</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496</th>\n",
" <td>Male</td>\n",
" <td>5.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>children</td>\n",
" <td>Rural</td>\n",
" <td>122.19</td>\n",
" <td>35.0</td>\n",
" <td>Unknown</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>497</th>\n",
" <td>Female</td>\n",
" <td>14.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>children</td>\n",
" <td>Rural</td>\n",
" <td>129.53</td>\n",
" <td>21.3</td>\n",
" <td>never smoked</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>498</th>\n",
" <td>Female</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>children</td>\n",
" <td>Rural</td>\n",
" <td>114.53</td>\n",
" <td>29.1</td>\n",
" <td>Unknown</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>454 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" gender age hypertension heart_disease ever_married work_type \\\n",
"0 Male 67.0 0 1 Yes Private \n",
"2 Male 80.0 0 1 Yes Private \n",
"3 Female 49.0 0 0 Yes Private \n",
"4 Female 79.0 1 0 Yes Self-employed \n",
"5 Male 81.0 0 0 Yes Private \n",
".. ... ... ... ... ... ... \n",
"494 Female 55.0 0 0 Yes Private \n",
"495 Female 71.0 0 0 Yes Private \n",
"496 Male 5.0 0 0 No children \n",
"497 Female 14.0 0 0 No children \n",
"498 Female 15.0 0 0 No children \n",
"\n",
" Residence_type avg_glucose_level bmi smoking_status stroke \n",
"0 Urban 228.69 36.6 formerly smoked 1 \n",
"2 Rural 105.92 32.5 never smoked 1 \n",
"3 Urban 171.23 34.4 smokes 1 \n",
"4 Rural 174.12 24.0 never smoked 1 \n",
"5 Urban 186.21 29.0 formerly smoked 1 \n",
".. ... ... ... ... ... \n",
"494 Rural 111.19 39.7 formerly smoked 0 \n",
"495 Urban 93.28 34.7 never smoked 0 \n",
"496 Rural 122.19 35.0 Unknown 0 \n",
"497 Rural 129.53 21.3 never smoked 0 \n",
"498 Rural 114.53 29.1 Unknown 0 \n",
"\n",
"[454 rows x 11 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('data.csv')\n",
"del df['id']\n",
"df = df[df['gender'].map(lambda x: str(x) != \"Other\")]\n",
"df = df.dropna()\n",
"df.info()\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>avg_glucose_level</th>\n",
" <th>bmi</th>\n",
" <th>stroke</th>\n",
" <th>gender_Male</th>\n",
" <th>ever_married_Yes</th>\n",
" <th>work_type_Never_worked</th>\n",
" <th>work_type_Private</th>\n",
" <th>work_type_Self-employed</th>\n",
" <th>work_type_children</th>\n",
" <th>Residence_type_Urban</th>\n",
" <th>smoking_status_formerly smoked</th>\n",
" <th>smoking_status_never smoked</th>\n",
" <th>smoking_status_smokes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>67.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>228.69</td>\n",
" <td>36.6</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>105.92</td>\n",
" <td>32.5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</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>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>49.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>171.23</td>\n",
" <td>34.4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>79.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>174.12</td>\n",
" <td>24.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</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>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>81.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>186.21</td>\n",
" <td>29.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>494</th>\n",
" <td>55.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111.19</td>\n",
" <td>39.7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</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",
" </tr>\n",
" <tr>\n",
" <th>495</th>\n",
" <td>71.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>93.28</td>\n",
" <td>34.7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496</th>\n",
" <td>5.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>122.19</td>\n",
" <td>35.0</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>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>497</th>\n",
" <td>14.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>129.53</td>\n",
" <td>21.3</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>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>498</th>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>114.53</td>\n",
" <td>29.1</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>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>454 rows × 16 columns</p>\n",
"</div>"
],
"text/plain": [
" age hypertension heart_disease avg_glucose_level bmi stroke \\\n",
"0 67.0 0 1 228.69 36.6 1 \n",
"2 80.0 0 1 105.92 32.5 1 \n",
"3 49.0 0 0 171.23 34.4 1 \n",
"4 79.0 1 0 174.12 24.0 1 \n",
"5 81.0 0 0 186.21 29.0 1 \n",
".. ... ... ... ... ... ... \n",
"494 55.0 0 0 111.19 39.7 0 \n",
"495 71.0 0 0 93.28 34.7 0 \n",
"496 5.0 0 0 122.19 35.0 0 \n",
"497 14.0 0 0 129.53 21.3 0 \n",
"498 15.0 0 0 114.53 29.1 0 \n",
"\n",
" gender_Male ever_married_Yes work_type_Never_worked work_type_Private \\\n",
"0 1 1 0 1 \n",
"2 1 1 0 1 \n",
"3 0 1 0 1 \n",
"4 0 1 0 0 \n",
"5 1 1 0 1 \n",
".. ... ... ... ... \n",
"494 0 1 0 1 \n",
"495 0 1 0 1 \n",
"496 1 0 0 0 \n",
"497 0 0 0 0 \n",
"498 0 0 0 0 \n",
"\n",
" work_type_Self-employed work_type_children Residence_type_Urban \\\n",
"0 0 0 1 \n",
"2 0 0 0 \n",
"3 0 0 1 \n",
"4 1 0 0 \n",
"5 0 0 1 \n",
".. ... ... ... \n",
"494 0 0 0 \n",
"495 0 0 1 \n",
"496 0 1 0 \n",
"497 0 1 0 \n",
"498 0 1 0 \n",
"\n",
" smoking_status_formerly smoked smoking_status_never smoked \\\n",
"0 1 0 \n",
"2 0 1 \n",
"3 0 0 \n",
"4 0 1 \n",
"5 1 0 \n",
".. ... ... \n",
"494 1 0 \n",
"495 0 1 \n",
"496 0 0 \n",
"497 0 1 \n",
"498 0 0 \n",
"\n",
" smoking_status_smokes \n",
"0 0 \n",
"2 0 \n",
"3 1 \n",
"4 0 \n",
"5 0 \n",
".. ... \n",
"494 0 \n",
"495 0 \n",
"496 0 \n",
"497 0 \n",
"498 0 \n",
"\n",
"[454 rows x 16 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.get_dummies(df, columns=['gender'], drop_first=True)\n",
"df = pd.get_dummies(df, columns=['ever_married'], drop_first=True)\n",
"df = pd.get_dummies(df, columns=['work_type'], drop_first=True)\n",
"df = pd.get_dummies(df, columns=['Residence_type'], drop_first=True)\n",
"df = pd.get_dummies(df, columns=['smoking_status'], drop_first=True)\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df_train, df_test = train_test_split(df, test_size=0.2, stratify=df['stroke'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 196\n",
"1 167\n",
"Name: stroke, dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train['stroke'].value_counts(dropna=False)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 49\n",
"1 42\n",
"Name: stroke, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test['stroke'].value_counts(dropna=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Wizualizacja"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x19196a188e0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 705.6x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.pairplot(df, x_vars=['avg_glucose_level','bmi'], y_vars='age', height=7, aspect=0.7, diag_kind = None)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" define('plotly', function(require, exports, module) {\n",
" /**\n",
"* plotly.js v1.58.4\n",
"* Copyright 2012-2020, Plotly, Inc.\n",
"* All rights reserved.\n",
"* Licensed under the MIT license\n",
"*/\n",
"!function(t){if(\"object\"==typeof exports&&\"undefined\"!=typeof module)module.exports=t();else if(\"function\"==typeof define&&define.amd)define([],t);else{(\"undefined\"!=typeof window?window:\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:this).Plotly=t()}}((function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l=\"function\"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error(\"Cannot find module '\"+o+\"'\");throw c.code=\"MODULE_NOT_FOUND\",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,(function(t){return i(e[o][1][t]||t)}),u,u.exports,t,e,r,n)}return r[o].exports}for(var a=\"function\"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}({1:[function(t,e,r){\"use strict\";var n=t(\"../src/lib\"),i={\"X,X div\":\"direction:ltr;font-family:'Open Sans', verdana, arial, sans-serif;margin:0;padding:0;\",\"X input,X button\":\"font-family:'Open Sans', verdana, arial, sans-serif;\",\"X input:focus,X button:focus\":\"outline:none;\",\"X a\":\"text-decoration:none;\",\"X a:hover\":\"text-decoration:none;\",\"X .crisp\":\"shape-rendering:crispEdges;\",\"X .user-select-none\":\"-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;user-select:none;\",\"X svg\":\"overflow:hidden;\",\"X svg a\":\"fill:#447adb;\",\"X svg a:hover\":\"fill:#3c6dc5;\",\"X .main-svg\":\"position:absolute;top:0;left:0;pointer-events:none;\",\"X .main-svg .draglayer\":\"pointer-events:all;\",\"X .cursor-default\":\"cursor:default;\",\"X .cursor-pointer\":\"cursor:pointer;\",\"X .cursor-crosshair\":\"cursor:crosshair;\",\"X .cursor-move\":\"cursor:move;\",\"X .cursor-col-resize\":\"cursor:col-resize;\",\"X .cursor-row-resize\":\"cursor:row-resize;\",\"X .cursor-ns-resize\":\"cursor:ns-resize;\",\"X .cursor-ew-resize\":\"cursor:ew-resize;\",\"X .cursor-sw-resize\":\"cursor:sw-resize;\",\"X .cursor-s-resize\":\"cursor:s-resize;\",\"X .cursor-se-resize\":\"cursor:se-resize;\",\"X .cursor-w-resize\":\"cursor:w-resize;\",\"X .cursor-e-resize\":\"cursor:e-resize;\",\"X .cursor-nw-resize\":\"cursor:nw-resize;\",\"X .cursor-n-resize\":\"cursor:n-resize;\",\"X .cursor-ne-resize\":\"cursor:ne-resize;\",\"X .cursor-grab\":\"cursor:-webkit-grab;cursor:grab;\",\"X .modebar\":\"position:absolute;top:2px;right:2px;\",\"X .ease-bg\":\"-webkit-transition:background-color 0.3s ease 0s;-moz-transition:background-color 0.3s ease 0s;-ms-transition:background-color 0.3s ease 0s;-o-transition:background-color 0.3s ease 0s;transition:background-color 0.3s ease 0s;\",\"X .modebar--hover>:not(.watermark)\":\"opacity:0;-webkit-transition:opacity 0.3s ease 0s;-moz-transition:opacity 0.3s ease 0s;-ms-transition:opacity 0.3s ease 0s;-o-transition:opacity 0.3s ease 0s;transition:opacity 0.3s ease 0s;\",\"X:hover .modebar--hover .modebar-group\":\"opacity:1;\",\"X .modebar-group\":\"float:left;display:inline-block;box-sizing:border-box;padding-left:8px;position:relative;vertical-align:middle;white-space:nowrap;\",\"X .modebar-btn\":\"position:relative;font-size:16px;padding:3px 4px;height:22px;cursor:pointer;line-height:normal;box-sizing:border-box;\",\"X .modebar-btn svg\":\"position:relative;top:2px;\",\"X .modebar.vertical\":\"display:flex;flex-direction:column;flex-wrap:wrap;align-content:flex-end;max-height:100%;\",\"X .modebar.vertical svg\":\"top:-1px;\",\"X .modebar.vertical .modebar-group\":\"display:block;float:none;padding-left:0px;padding-bottom:8px;\",\"X .modebar.vertical .modebar-group .modebar-btn\":\"display:block;text-align:center;\",\"X [data-title]:before,X [data-title]:after\":\"position:absolute;-webkit-transform:translate3d(0, 0, 0);-moz-transform:translate3d(0, 0, 0);-ms-transform:translate3d(0, 0, 0);-o-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0);display:none;opacity:0;z-index:1001;pointer-events:none;top:110%;right:50%;\",\"X [data-title]:hover:before,X [data-title]:hover:after\":\"display:block;opacity:1;\",\"X [data-title]:before\":\"content:'';position:absolute;background:transparent;border:6px solid transpa
"/*!\n",
" * The buffer module from node.js, for the browser.\n",
" *\n",
" * @author Feross Aboukhadijeh <feross@feross.org> <http://feross.org>\n",
" * @license MIT\n",
" */function i(t,e){if(t===e)return 0;for(var r=t.length,n=e.length,i=0,a=Math.min(r,n);i<a;++i)if(t[i]!==e[i]){r=t[i],n=e[i];break}return r<n?-1:n<r?1:0}function a(t){return r.Buffer&&\"function\"==typeof r.Buffer.isBuffer?r.Buffer.isBuffer(t):!(null==t||!t._isBuffer)}var o=t(\"util/\"),s=Object.prototype.hasOwnProperty,l=Array.prototype.slice,c=\"foo\"===function(){}.name;function u(t){return Object.prototype.toString.call(t)}function f(t){return!a(t)&&(\"function\"==typeof r.ArrayBuffer&&(\"function\"==typeof ArrayBuffer.isView?ArrayBuffer.isView(t):!!t&&(t instanceof DataView||!!(t.buffer&&t.buffer instanceof ArrayBuffer))))}var h=e.exports=y,p=/\\s*function\\s+([^\\(\\s]*)\\s*/;function d(t){if(o.isFunction(t)){if(c)return t.name;var e=t.toString().match(p);return e&&e[1]}}function g(t,e){return\"string\"==typeof t?t.length<e?t:t.slice(0,e):t}function m(t){if(c||!o.isFunction(t))return o.inspect(t);var e=d(t);return\"[Function\"+(e?\": \"+e:\"\")+\"]\"}function v(t,e,r,n,i){throw new h.AssertionError({message:r,actual:t,expected:e,operator:n,stackStartFunction:i})}function y(t,e){t||v(t,!0,e,\"==\",h.ok)}function x(t,e,r,n){if(t===e)return!0;if(a(t)&&a(e))return 0===i(t,e);if(o.isDate(t)&&o.isDate(e))return t.getTime()===e.getTime();if(o.isRegExp(t)&&o.isRegExp(e))return t.source===e.source&&t.global===e.global&&t.multiline===e.multiline&&t.lastIndex===e.lastIndex&&t.ignoreCase===e.ignoreCase;if(null!==t&&\"object\"==typeof t||null!==e&&\"object\"==typeof e){if(f(t)&&f(e)&&u(t)===u(e)&&!(t instanceof Float32Array||t instanceof Float64Array))return 0===i(new Uint8Array(t.buffer),new Uint8Array(e.buffer));if(a(t)!==a(e))return!1;var s=(n=n||{actual:[],expected:[]}).actual.indexOf(t);return-1!==s&&s===n.expected.indexOf(e)||(n.actual.push(t),n.expected.push(e),function(t,e,r,n){if(null==t||null==e)return!1;if(o.isPrimitive(t)||o.isPrimitive(e))return t===e;if(r&&Object.getPrototypeOf(t)!==Object.getPrototypeOf(e))return!1;var i=b(t),a=b(e);if(i&&!a||!i&&a)return!1;if(i)return t=l.call(t),e=l.call(e),x(t,e,r);var s,c,u=T(t),f=T(e);if(u.length!==f.length)return!1;for(u.sort(),f.sort(),c=u.length-1;c>=0;c--)if(u[c]!==f[c])return!1;for(c=u.length-1;c>=0;c--)if(s=u[c],!x(t[s],e[s],r,n))return!1;return!0}(t,e,r,n))}return r?t===e:t==e}function b(t){return\"[object Arguments]\"==Object.prototype.toString.call(t)}function _(t,e){if(!t||!e)return!1;if(\"[object RegExp]\"==Object.prototype.toString.call(e))return e.test(t);try{if(t instanceof e)return!0}catch(t){}return!Error.isPrototypeOf(e)&&!0===e.call({},t)}function w(t,e,r,n){var i;if(\"function\"!=typeof e)throw new TypeError('\"block\" argument must be a function');\"string\"==typeof r&&(n=r,r=null),i=function(t){var e;try{t()}catch(t){e=t}return e}(e),n=(r&&r.name?\" (\"+r.name+\").\":\".\")+(n?\" \"+n:\".\"),t&&!i&&v(i,r,\"Missing expected exception\"+n);var a=\"string\"==typeof n,s=!t&&i&&!r;if((!t&&o.isError(i)&&a&&_(i,r)||s)&&v(i,r,\"Got unwanted exception\"+n),t&&i&&r&&!_(i,r)||!t&&i)throw i}h.AssertionError=function(t){this.name=\"AssertionError\",this.actual=t.actual,this.expected=t.expected,this.operator=t.operator,t.message?(this.message=t.message,this.generatedMessage=!1):(this.message=function(t){return g(m(t.actual),128)+\" \"+t.operator+\" \"+g(m(t.expected),128)}(this),this.generatedMessage=!0);var e=t.stackStartFunction||v;if(Error.captureStackTrace)Error.captureStackTrace(this,e);else{var r=new Error;if(r.stack){var n=r.stack,i=d(e),a=n.indexOf(\"\\n\"+i);if(a>=0){var o=n.indexOf(\"\\n\",a+1);n=n.substring(o+1)}this.stack=n}}},o.inherits(h.AssertionError,Error),h.fail=v,h.ok=y,h.equal=function(t,e,r){t!=e&&v(t,e,r,\"==\",h.equal)},h.notEqual=function(t,e,r){t==e&&v(t,e,r,\"!=\",h.notEqual)},h.deepEqual=function(t,e,r){x(t,e,!1)||v(t,e,r,\"deepEqual\",h.deepEqual)},h.deepStrictEqual=function(t,e,r){x(t,e,!0)||v(t,e,r,\"deepStrictEqual\",h.deepStrictEqual)},h.notDeepEqual=function(t,e,r){x(t,e,!1)&&v(t,e,r,\"notDeepEqual\",h.notDeepEqual)},h.notDeepStrictEqual=function t(e,r,n){x(e,r,!0)&&v(e,r,n,\"notDeepStrictEqual\",t)},h.strictEqual=function(t,e
"/*!\n",
" * The buffer module from node.js, for the browser.\n",
" *\n",
" * @author Feross Aboukhadijeh <https://feross.org>\n",
" * @license MIT\n",
" */\n",
"\"use strict\";var e=t(\"base64-js\"),n=t(\"ieee754\");r.Buffer=a,r.SlowBuffer=function(t){+t!=t&&(t=0);return a.alloc(+t)},r.INSPECT_MAX_BYTES=50;function i(t){if(t>2147483647)throw new RangeError('The value \"'+t+'\" is invalid for option \"size\"');var e=new Uint8Array(t);return e.__proto__=a.prototype,e}function a(t,e,r){if(\"number\"==typeof t){if(\"string\"==typeof e)throw new TypeError('The \"string\" argument must be of type string. Received type number');return l(t)}return o(t,e,r)}function o(t,e,r){if(\"string\"==typeof t)return function(t,e){\"string\"==typeof e&&\"\"!==e||(e=\"utf8\");if(!a.isEncoding(e))throw new TypeError(\"Unknown encoding: \"+e);var r=0|f(t,e),n=i(r),o=n.write(t,e);o!==r&&(n=n.slice(0,o));return n}(t,e);if(ArrayBuffer.isView(t))return c(t);if(null==t)throw TypeError(\"The first argument must be one of type string, Buffer, ArrayBuffer, Array, or Array-like Object. Received type \"+typeof t);if(B(t,ArrayBuffer)||t&&B(t.buffer,ArrayBuffer))return function(t,e,r){if(e<0||t.byteLength<e)throw new RangeError('\"offset\" is outside of buffer bounds');if(t.byteLength<e+(r||0))throw new RangeError('\"length\" is outside of buffer bounds');var n;n=void 0===e&&void 0===r?new Uint8Array(t):void 0===r?new Uint8Array(t,e):new Uint8Array(t,e,r);return n.__proto__=a.prototype,n}(t,e,r);if(\"number\"==typeof t)throw new TypeError('The \"value\" argument must not be of type number. Received type number');var n=t.valueOf&&t.valueOf();if(null!=n&&n!==t)return a.from(n,e,r);var o=function(t){if(a.isBuffer(t)){var e=0|u(t.length),r=i(e);return 0===r.length||t.copy(r,0,0,e),r}if(void 0!==t.length)return\"number\"!=typeof t.length||N(t.length)?i(0):c(t);if(\"Buffer\"===t.type&&Array.isArray(t.data))return c(t.data)}(t);if(o)return o;if(\"undefined\"!=typeof Symbol&&null!=Symbol.toPrimitive&&\"function\"==typeof t[Symbol.toPrimitive])return a.from(t[Symbol.toPrimitive](\"string\"),e,r);throw new TypeError(\"The first argument must be one of type string, Buffer, ArrayBuffer, Array, or Array-like Object. Received type \"+typeof t)}function s(t){if(\"number\"!=typeof t)throw new TypeError('\"size\" argument must be of type number');if(t<0)throw new RangeError('The value \"'+t+'\" is invalid for option \"size\"')}function l(t){return s(t),i(t<0?0:0|u(t))}function c(t){for(var e=t.length<0?0:0|u(t.length),r=i(e),n=0;n<e;n+=1)r[n]=255&t[n];return r}function u(t){if(t>=2147483647)throw new RangeError(\"Attempt to allocate Buffer larger than maximum size: 0x\"+2147483647..toString(16)+\" bytes\");return 0|t}function f(t,e){if(a.isBuffer(t))return t.length;if(ArrayBuffer.isView(t)||B(t,ArrayBuffer))return t.byteLength;if(\"string\"!=typeof t)throw new TypeError('The \"string\" argument must be one of type string, Buffer, or ArrayBuffer. Received type '+typeof t);var r=t.length,n=arguments.length>2&&!0===arguments[2];if(!n&&0===r)return 0;for(var i=!1;;)switch(e){case\"ascii\":case\"latin1\":case\"binary\":return r;case\"utf8\":case\"utf-8\":return D(t).length;case\"ucs2\":case\"ucs-2\":case\"utf16le\":case\"utf-16le\":return 2*r;case\"hex\":return r>>>1;case\"base64\":return R(t).length;default:if(i)return n?-1:D(t).length;e=(\"\"+e).toLowerCase(),i=!0}}function h(t,e,r){var n=!1;if((void 0===e||e<0)&&(e=0),e>this.length)return\"\";if((void 0===r||r>this.length)&&(r=this.length),r<=0)return\"\";if((r>>>=0)<=(e>>>=0))return\"\";for(t||(t=\"utf8\");;)switch(t){case\"hex\":return A(this,e,r);case\"utf8\":case\"utf-8\":return T(this,e,r);case\"ascii\":return k(this,e,r);case\"latin1\":case\"binary\":return M(this,e,r);case\"base64\":return w(this,e,r);case\"ucs2\":case\"ucs-2\":case\"utf16le\":case\"utf-16le\":return S(this,e,r);default:if(n)throw new TypeError(\"Unknown encoding: \"+t);t=(t+\"\").toLowerCase(),n=!0}}function p(t,e,r){var n=t[e];t[e]=t[r],t[r]=n}function d(t,e,r,n,i){if(0===t.length)return-1;if(\"string\"==typeof r?(n=r,r=0):r>2147483647?r=2147483647:r<-2147483648&&(r=-2147483648),N(r=+r)&&(r=i?0:t.length-1),r<0&&(r=t.length+r),r>=t.length){if(i)return-1;r=t.length-1}else if(r<0){if(!i)return-1;r=0}i
"/*!\n",
" * @overview es6-promise - a tiny implementation of Promises/A+.\n",
" * @copyright Copyright (c) 2014 Yehuda Katz, Tom Dale, Stefan Penner and contributors (Conversion to ES6 API by Jake Archibald)\n",
" * @license Licensed under MIT license\n",
" * See https://raw.githubusercontent.com/stefanpenner/es6-promise/master/LICENSE\n",
" * @version v4.2.8+1e68dce6\n",
" */\n",
"!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof e?e.exports=n():t.ES6Promise=n()}(this,(function(){\"use strict\";function e(t){return\"function\"==typeof t}var r=Array.isArray?Array.isArray:function(t){return\"[object Array]\"===Object.prototype.toString.call(t)},a=0,o=void 0,s=void 0,l=function(t,e){g[a]=t,g[a+1]=e,2===(a+=2)&&(s?s(m):_())};var c=\"undefined\"!=typeof window?window:void 0,u=c||{},f=u.MutationObserver||u.WebKitMutationObserver,h=\"undefined\"==typeof self&&\"undefined\"!=typeof n&&\"[object process]\"==={}.toString.call(n),p=\"undefined\"!=typeof Uint8ClampedArray&&\"undefined\"!=typeof importScripts&&\"undefined\"!=typeof MessageChannel;function d(){var t=setTimeout;return function(){return t(m,1)}}var g=new Array(1e3);function m(){for(var t=0;t<a;t+=2){(0,g[t])(g[t+1]),g[t]=void 0,g[t+1]=void 0}a=0}var v,y,x,b,_=void 0;function w(t,e){var r=this,n=new this.constructor(M);void 0===n[k]&&D(n);var i=r._state;if(i){var a=arguments[i-1];l((function(){return z(i,n,a,r._result)}))}else I(r,n,t,e);return n}function T(t){if(t&&\"object\"==typeof t&&t.constructor===this)return t;var e=new this(M);return S(e,t),e}h?_=function(){return n.nextTick(m)}:f?(y=0,x=new f(m),b=document.createTextNode(\"\"),x.observe(b,{characterData:!0}),_=function(){b.data=y=++y%2}):p?((v=new MessageChannel).port1.onmessage=m,_=function(){return v.port2.postMessage(0)}):_=void 0===c&&\"function\"==typeof t?function(){try{var t=Function(\"return this\")().require(\"vertx\");return\"undefined\"!=typeof(o=t.runOnLoop||t.runOnContext)?function(){o(m)}:d()}catch(t){return d()}}():d();var k=Math.random().toString(36).substring(2);function M(){}function A(t,r,n){r.constructor===t.constructor&&n===w&&r.constructor.resolve===T?function(t,e){1===e._state?C(t,e._result):2===e._state?L(t,e._result):I(e,void 0,(function(e){return S(t,e)}),(function(e){return L(t,e)}))}(t,r):void 0===n?C(t,r):e(n)?function(t,e,r){l((function(t){var n=!1,i=function(t,e,r,n){try{t.call(e,r,n)}catch(t){return t}}(r,e,(function(r){n||(n=!0,e!==r?S(t,r):C(t,r))}),(function(e){n||(n=!0,L(t,e))}),t._label);!n&&i&&(n=!0,L(t,i))}),t)}(t,r,n):C(t,r)}function S(t,e){if(t===e)L(t,new TypeError(\"You cannot resolve a promise with itself\"));else if(i=typeof(n=e),null===n||\"object\"!==i&&\"function\"!==i)C(t,e);else{var r=void 0;try{r=e.then}catch(e){return void L(t,e)}A(t,e,r)}var n,i}function E(t){t._onerror&&t._onerror(t._result),P(t)}function C(t,e){void 0===t._state&&(t._result=e,t._state=1,0!==t._subscribers.length&&l(P,t))}function L(t,e){void 0===t._state&&(t._state=2,t._result=e,l(E,t))}function I(t,e,r,n){var i=t._subscribers,a=i.length;t._onerror=null,i[a]=e,i[a+1]=r,i[a+2]=n,0===a&&t._state&&l(P,t)}function P(t){var e=t._subscribers,r=t._state;if(0!==e.length){for(var n=void 0,i=void 0,a=t._result,o=0;o<e.length;o+=3)n=e[o],i=e[o+r],n?z(r,n,i,a):i(a);t._subscribers.length=0}}function z(t,r,n,i){var a=e(n),o=void 0,s=void 0,l=!0;if(a){try{o=n(i)}catch(t){l=!1,s=t}if(r===o)return void L(r,new TypeError(\"A promises callback cannot return that same promise.\"))}else o=i;void 0!==r._state||(a&&l?S(r,o):!1===l?L(r,s):1===t?C(r,o):2===t&&L(r,o))}var O=0;function D(t){t[k]=O++,t._state=void 0,t._result=void 0,t._subscribers=[]}var R=function(){function t(t,e){this._instanceConstructor=t,this.promise=new t(M),this.promise[k]||D(this.promise),r(e)?(this.length=e.length,this._remaining=e.length,this._result=new Array(this.length),0===this.length?C(this.promise,this._result):(this.length=this.length||0,this._enumerate(e),0===this._remaining&&C(this.promise,this._result))):L(this.promise,new Error(\"Array Methods must be provided an Array\"))}return t.prototype._enumerate=function(t){for(var e=0;void 0===this._state&&e<t.length;e++)this._eachEntry(t[e],e)},t.prototype._eachEntry=function(t,e){var r=this._instanceConstructor,n=r.resolve;if(n===T){var i=void 0,a=void 0,o=!1;try{i=t.then}catch(t){o=!0,a=t}if(i===w&&void 0!==t._state)this._settledAt(t._state,e,t._result);else if(\"function\"!=typeof i)this._remaining--,this._result[e]=t;else if(r===F){var s=new r
"/*!\n",
" * Determine if an object is a Buffer\n",
" *\n",
" * @author Feross Aboukhadijeh <https://feross.org>\n",
" * @license MIT\n",
" */\n",
"e.exports=function(t){return null!=t&&(n(t)||function(t){return\"function\"==typeof t.readFloatLE&&\"function\"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],466:[function(t,e,r){\"use strict\";e.exports=\"undefined\"!=typeof navigator&&(/MSIE/.test(navigator.userAgent)||/Trident\\//.test(navigator.appVersion))},{}],467:[function(t,e,r){\"use strict\";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||\"undefined\"==typeof navigator||(e=navigator.userAgent),e&&e.headers&&\"string\"==typeof e.headers[\"user-agent\"]&&(e=e.headers[\"user-agent\"]),\"string\"!=typeof e)return!1;var r=t.tablet?i.test(e):n.test(e);return!r&&t.tablet&&t.featureDetect&&navigator&&navigator.maxTouchPoints>1&&-1!==e.indexOf(\"Macintosh\")&&-1!==e.indexOf(\"Safari\")&&(r=!0),r}},{}],468:[function(t,e,r){\"use strict\";e.exports=function(t){var e=typeof t;return null!==t&&(\"object\"===e||\"function\"===e)}},{}],469:[function(t,e,r){\"use strict\";var n=Object.prototype.toString;e.exports=function(t){var e;return\"[object Object]\"===n.call(t)&&(null===(e=Object.getPrototypeOf(t))||e===Object.getPrototypeOf({}))}},{}],470:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e,r=t.length,n=0;n<r;n++)if(((e=t.charCodeAt(n))<9||e>13)&&32!==e&&133!==e&&160!==e&&5760!==e&&6158!==e&&(e<8192||e>8205)&&8232!==e&&8233!==e&&8239!==e&&8287!==e&&8288!==e&&12288!==e&&65279!==e)return!1;return!0}},{}],471:[function(t,e,r){\"use strict\";e.exports=function(t){return\"string\"==typeof t&&(t=t.trim(),!!(/^[mzlhvcsqta]\\s*[-+.0-9][^mlhvzcsqta]+/i.test(t)&&/[\\dz]$/i.test(t)&&t.length>4))}},{}],472:[function(t,e,r){e.exports=function(t,e,r){return t*(1-r)+e*r}},{}],473:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof e?e.exports=n():(t=t||self).mapboxgl=n()}(this,(function(){\"use strict\";var t,e,r;function n(n,i){if(t)if(e){var a=\"var sharedChunk = {}; (\"+t+\")(sharedChunk); (\"+e+\")(sharedChunk);\",o={};t(o),(r=i(o)).workerUrl=window.URL.createObjectURL(new Blob([a],{type:\"text/javascript\"}))}else e=i;else t=i}return n(0,(function(t){function e(t,e){return t(e={exports:{}},e.exports),e.exports}var r=n;function n(t,e,r,n){this.cx=3*t,this.bx=3*(r-t)-this.cx,this.ax=1-this.cx-this.bx,this.cy=3*e,this.by=3*(n-e)-this.cy,this.ay=1-this.cy-this.by,this.p1x=t,this.p1y=n,this.p2x=r,this.p2y=n}n.prototype.sampleCurveX=function(t){return((this.ax*t+this.bx)*t+this.cx)*t},n.prototype.sampleCurveY=function(t){return((this.ay*t+this.by)*t+this.cy)*t},n.prototype.sampleCurveDerivativeX=function(t){return(3*this.ax*t+2*this.bx)*t+this.cx},n.prototype.solveCurveX=function(t,e){var r,n,i,a,o;for(void 0===e&&(e=1e-6),i=t,o=0;o<8;o++){if(a=this.sampleCurveX(i)-t,Math.abs(a)<e)return i;var s=this.sampleCurveDerivativeX(i);if(Math.abs(s)<1e-6)break;i-=a/s}if((i=t)<(r=0))return r;if(i>(n=1))return n;for(;r<n;){if(a=this.sampleCurveX(i),Math.abs(a-t)<e)return i;t>a?r=i:n=i,i=.5*(n-r)+r}return i},n.prototype.solve=function(t,e){return this.sampleCurveY(this.solveCurveX(t,e))};var i=a;function a(t,e){this.x=t,this.y=e}function o(t,e,n,i){var a=new r(t,e,n,i);return function(t){return a.solve(t)}}a.prototype={clone:function(){return new a(this.x,this.y)},add:function(t){return this.clone()._add(t)},sub:function(t){return this.clone()._sub(t)},multByPoint:function(t){return this.clone()._multByPoint(t)},divByPoi
"/*\n",
"object-assign\n",
"(c) Sindre Sorhus\n",
"@license MIT\n",
"*/\n",
"\"use strict\";var n=Object.getOwnPropertySymbols,i=Object.prototype.hasOwnProperty,a=Object.prototype.propertyIsEnumerable;function o(t){if(null==t)throw new TypeError(\"Object.assign cannot be called with null or undefined\");return Object(t)}e.exports=function(){try{if(!Object.assign)return!1;var t=new String(\"abc\");if(t[5]=\"de\",\"5\"===Object.getOwnPropertyNames(t)[0])return!1;for(var e={},r=0;r<10;r++)e[\"_\"+String.fromCharCode(r)]=r;if(\"0123456789\"!==Object.getOwnPropertyNames(e).map((function(t){return e[t]})).join(\"\"))return!1;var n={};return\"abcdefghijklmnopqrst\".split(\"\").forEach((function(t){n[t]=t})),\"abcdefghijklmnopqrst\"===Object.keys(Object.assign({},n)).join(\"\")}catch(t){return!1}}()?Object.assign:function(t,e){for(var r,s,l=o(t),c=1;c<arguments.length;c++){for(var u in r=Object(arguments[c]))i.call(r,u)&&(l[u]=r[u]);if(n){s=n(r);for(var f=0;f<s.length;f++)a.call(r,s[f])&&(l[s[f]]=r[s[f]])}}return l}},{}],500:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i,a,o,s,l,c){var u=e+a+c;if(f>0){var f=Math.sqrt(u+1);t[0]=.5*(o-l)/f,t[1]=.5*(s-n)/f,t[2]=.5*(r-a)/f,t[3]=.5*f}else{var h=Math.max(e,a,c);f=Math.sqrt(2*h-u+1);e>=h?(t[0]=.5*f,t[1]=.5*(i+r)/f,t[2]=.5*(s+n)/f,t[3]=.5*(o-l)/f):a>=h?(t[0]=.5*(r+i)/f,t[1]=.5*f,t[2]=.5*(l+o)/f,t[3]=.5*(s-n)/f):(t[0]=.5*(n+s)/f,t[1]=.5*(o+l)/f,t[2]=.5*f,t[3]=.5*(r-i)/f)}return t}},{}],501:[function(t,e,r){\"use strict\";e.exports=function(t){var e=(t=t||{}).center||[0,0,0],r=t.rotation||[0,0,0,1],n=t.radius||1;e=[].slice.call(e,0,3),u(r=[].slice.call(r,0,4),r);var i=new f(r,e,Math.log(n));i.setDistanceLimits(t.zoomMin,t.zoomMax),(\"eye\"in t||\"up\"in t)&&i.lookAt(0,t.eye,t.center,t.up);return i};var n=t(\"filtered-vector\"),i=t(\"gl-mat4/lookAt\"),a=t(\"gl-mat4/fromQuat\"),o=t(\"gl-mat4/invert\"),s=t(\"./lib/quatFromFrame\");function l(t,e,r){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2))}function c(t,e,r,n){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2)+Math.pow(n,2))}function u(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=c(r,n,i,a);o>1e-6?(t[0]=r/o,t[1]=n/o,t[2]=i/o,t[3]=a/o):(t[0]=t[1]=t[2]=0,t[3]=1)}function f(t,e,r){this.radius=n([r]),this.center=n(e),this.rotation=n(t),this.computedRadius=this.radius.curve(0),this.computedCenter=this.center.curve(0),this.computedRotation=this.rotation.curve(0),this.computedUp=[.1,0,0],this.computedEye=[.1,0,0],this.computedMatrix=[.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],this.recalcMatrix(0)}var h=f.prototype;h.lastT=function(){return Math.max(this.radius.lastT(),this.center.lastT(),this.rotation.lastT())},h.recalcMatrix=function(t){this.radius.curve(t),this.center.curve(t),this.rotation.curve(t);var e=this.computedRotation;u(e,e);var r=this.computedMatrix;a(r,e);var n=this.computedCenter,i=this.computedEye,o=this.computedUp,s=Math.exp(this.computedRadius[0]);i[0]=n[0]+s*r[2],i[1]=n[1]+s*r[6],i[2]=n[2]+s*r[10],o[0]=r[1],o[1]=r[5],o[2]=r[9];for(var l=0;l<3;++l){for(var c=0,f=0;f<3;++f)c+=r[l+4*f]*i[f];r[12+l]=-c}},h.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r},h.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},h.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},h.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=i[1],o=i[5],s=i[9],c=l(a,o,s);a/=c,o/=c,s/=c;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=l(u-=a*p,f-=o*p,h-=s*p);u/=d,f/=d,h/=d;var g=i[2],m=i[6],v=i[10],y=g*a+m*o+v*s,x=g*u+m*f+v*h,b=l(g-=y*a+x*u,m-=y*o+x*f,v-=y*s+x*h);g/=b,m/=b,v/=b;var _=u*e+a*r,w=f*e+o*r,T=h*e+s*r;this.center.move(t,_,w,T);var k=Math.exp(this.computedRadius[0]);k=Math.max(1e-4,k+n),this.radius.set(t,Math.log(k))},h.rotate=function(t,e,r,n){this.recalcMatrix(t),e=e||0,r=r||0;var i=this.computedMatrix,a=i[0],o=i[4],s=i[8],u=i[1],f=i[5],h=i[9],p=i[2],d=i[6],g=i[10],m=e*a+r*u,v=e*o+r*f,y=e*s+r*h,x=-(d*y-g*v),b=-(g*m-p*y),_=-(p*v-d*m),w=Math.sqrt(Math.max(0,1-Math.pow(x,2)-Math.pow(b,2)-Math.pow(_,2))),T=c(x,b,_,w);T>1e-6?(x/=T,b/=
"/*!\n",
" * pad-left <https://github.com/jonschlinkert/pad-left>\n",
" *\n",
" * Copyright (c) 2014-2015, Jon Schlinkert.\n",
" * Licensed under the MIT license.\n",
" */\n",
"\"use strict\";var n=t(\"repeat-string\");e.exports=function(t,e,r){return n(r=\"undefined\"!=typeof r?r+\"\":\" \",e)+t}},{\"repeat-string\":541}],503:[function(t,e,r){\"use strict\";function n(t,e){if(\"string\"!=typeof t)return[t];var r=[t];\"string\"==typeof e||Array.isArray(e)?e={brackets:e}:e||(e={});var n=e.brackets?Array.isArray(e.brackets)?e.brackets:[e.brackets]:[\"{}\",\"[]\",\"()\"],i=e.escape||\"___\",a=!!e.flat;n.forEach((function(t){var e=new RegExp([\"\\\\\",t[0],\"[^\\\\\",t[0],\"\\\\\",t[1],\"]*\\\\\",t[1]].join(\"\")),n=[];function a(e,a,o){var s=r.push(e.slice(t[0].length,-t[1].length))-1;return n.push(s),i+s+i}r.forEach((function(t,n){for(var i,o=0;t!=i;)if(i=t,t=t.replace(e,a),o++>1e4)throw Error(\"References have circular dependency. Please, check them.\");r[n]=t})),n=n.reverse(),r=r.map((function(e){return n.forEach((function(r){e=e.replace(new RegExp(\"(\\\\\"+i+r+\"\\\\\"+i+\")\",\"g\"),t[0]+\"$1\"+t[1])})),e}))}));var o=new RegExp(\"\\\\\"+i+\"([0-9]+)\\\\\"+i);return a?r:function t(e,r,n){for(var i,a=[],s=0;i=o.exec(e);){if(s++>1e4)throw Error(\"Circular references in parenthesis\");a.push(e.slice(0,i.index)),a.push(t(r[i[1]],r)),e=e.slice(i.index+i[0].length)}return a.push(e),a}(r[0],r)}function i(t,e){if(e&&e.flat){var r,n=e&&e.escape||\"___\",i=t[0];if(!i)return\"\";for(var a=new RegExp(\"\\\\\"+n+\"([0-9]+)\\\\\"+n),o=0;i!=r;){if(o++>1e4)throw Error(\"Circular references in \"+t);r=i,i=i.replace(a,s)}return i}return t.reduce((function t(e,r){return Array.isArray(r)&&(r=r.reduce(t,\"\")),e+r}),\"\");function s(e,r){if(null==t[r])throw Error(\"Reference \"+r+\"is undefined\");return t[r]}}function a(t,e){return Array.isArray(t)?i(t,e):n(t,e)}a.parse=n,a.stringify=i,e.exports=a},{}],504:[function(t,e,r){\"use strict\";var n=t(\"pick-by-alias\");e.exports=function(t){var e;arguments.length>1&&(t=arguments);\"string\"==typeof t?t=t.split(/\\s/).map(parseFloat):\"number\"==typeof t&&(t=[t]);t.length&&\"number\"==typeof t[0]?e=1===t.length?{width:t[0],height:t[0],x:0,y:0}:2===t.length?{width:t[0],height:t[1],x:0,y:0}:{x:t[0],y:t[1],width:t[2]-t[0]||0,height:t[3]-t[1]||0}:t&&(t=n(t,{left:\"x l left Left\",top:\"y t top Top\",width:\"w width W Width\",height:\"h height W Width\",bottom:\"b bottom Bottom\",right:\"r right Right\"}),e={x:t.left||0,y:t.top||0},null==t.width?t.right?e.width=t.right-e.x:e.width=0:e.width=t.width,null==t.height?t.bottom?e.height=t.bottom-e.y:e.height=0:e.height=t.height);return e}},{\"pick-by-alias\":511}],505:[function(t,e,r){e.exports=function(t){var e=[];return t.replace(i,(function(t,r,i){var o=r.toLowerCase();for(i=function(t){var e=t.match(a);return e?e.map(Number):[]}(i),\"m\"==o&&i.length>2&&(e.push([r].concat(i.splice(0,2))),o=\"l\",r=\"m\"==r?\"l\":\"L\");;){if(i.length==n[o])return i.unshift(r),e.push(i);if(i.length<n[o])throw new Error(\"malformed path data\");e.push([r].concat(i.splice(0,n[o])))}})),e};var n={a:7,c:6,h:1,l:2,m:2,q:4,s:4,t:2,v:1,z:0},i=/([astvzqmhlc])([^astvzqmhlc]*)/gi;var a=/-?[0-9]*\\.?[0-9]+(?:e[-+]?\\d+)?/gi},{}],506:[function(t,e,r){e.exports=function(t,e){e||(e=[0,\"\"]),t=String(t);var r=parseFloat(t,10);return e[0]=r,e[1]=t.match(/[\\d.\\-\\+]*\\s*(.*)/)[1]||\"\",e}},{}],507:[function(t,e,r){(function(t){(function(){\"use strict\";function r(t){if(\"string\"!=typeof t)throw new TypeError(\"Path must be a string. Received \"+JSON.stringify(t))}function n(t,e){for(var r,n=\"\",i=0,a=-1,o=0,s=0;s<=t.length;++s){if(s<t.length)r=t.charCodeAt(s);else{if(47===r)break;r=47}if(47===r){if(a===s-1||1===o);else if(a!==s-1&&2===o){if(n.length<2||2!==i||46!==n.charCodeAt(n.length-1)||46!==n.charCodeAt(n.length-2))if(n.length>2){var l=n.lastIndexOf(\"/\");if(l!==n.length-1){-1===l?(n=\"\",i=0):i=(n=n.slice(0,l)).length-1-n.lastIndexOf(\"/\"),a=s,o=0;continue}}else if(2===n.length||1===n.length){n=\"\",i=0,a=s,o=0;continue}e&&(n.length>0?n+=\"/..\":n=\"..\",i=2)}else n.length>0?n+=\"/\"+t.slice(a+1,s):n=t.slice(a+1,s),i=s-a-1;a=s,o=0}else 46===r&&-1!==o?++o:o=-1}return n}var i={resolve:function(){for(var e,i=\"\",a=!1,o=arguments.length-1;o
"/*\n",
" * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
" * @license MIT\n",
" * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
" */\n",
"var n,i=t(\"./lib/build-log\"),a=t(\"./lib/epsilon\"),o=t(\"./lib/intersecter\"),s=t(\"./lib/segment-chainer\"),l=t(\"./lib/segment-selector\"),c=t(\"./lib/geojson\"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return t.regions.forEach(e.addRegion),{segments:e.calculate(t.inverted),inverted:t.inverted}},combine:function(t,e){return{combined:o(!1,f,u).calculate(t.segments,t.inverted,e.segments,e.inverted),inverted1:t.inverted,inverted2:e.inverted}},selectUnion:function(t){return{segments:l.union(t.combined,u),inverted:t.inverted1||t.inverted2}},selectIntersect:function(t){return{segments:l.intersect(t.combined,u),inverted:t.inverted1&&t.inverted2}},selectDifference:function(t){return{segments:l.difference(t.combined,u),inverted:t.inverted1&&!t.inverted2}},selectDifferenceRev:function(t){return{segments:l.differenceRev(t.combined,u),inverted:!t.inverted1&&t.inverted2}},selectXor:function(t){return{segments:l.xor(t.combined,u),inverted:t.inverted1!==t.inverted2}},polygon:function(t){return{regions:s(t.segments,f,u),inverted:t.inverted}},polygonFromGeoJSON:function(t){return c.toPolygon(n,t)},polygonToGeoJSON:function(t){return c.fromPolygon(n,f,t)},union:function(t,e){return h(t,e,n.selectUnion)},intersect:function(t,e){return h(t,e,n.selectIntersect)},difference:function(t,e){return h(t,e,n.selectDifference)},differenceRev:function(t,e){return h(t,e,n.selectDifferenceRev)},xor:function(t,e){return h(t,e,n.selectXor)}},\"object\"==typeof window&&(window.PolyBool=n),e.exports=n},{\"./lib/build-log\":518,\"./lib/epsilon\":519,\"./lib/geojson\":520,\"./lib/intersecter\":521,\"./lib/segment-chainer\":523,\"./lib/segment-selector\":524}],518:[function(t,e,r){e.exports=function(){var t,e=0,r=!1;function n(e,r){return t.list.push({type:e,data:r?JSON.parse(JSON.stringify(r)):void 0}),t}return t={list:[],segmentId:function(){return e++},checkIntersection:function(t,e){return n(\"check\",{seg1:t,seg2:e})},segmentChop:function(t,e){return n(\"div_seg\",{seg:t,pt:e}),n(\"chop\",{seg:t,pt:e})},statusRemove:function(t){return n(\"pop_seg\",{seg:t})},segmentUpdate:function(t){return n(\"seg_update\",{seg:t})},segmentNew:function(t,e){return n(\"new_seg\",{seg:t,primary:e})},segmentRemove:function(t){return n(\"rem_seg\",{seg:t})},tempStatus:function(t,e,r){return n(\"temp_status\",{seg:t,above:e,below:r})},rewind:function(t){return n(\"rewind\",{seg:t})},status:function(t,e,r){return n(\"status\",{seg:t,above:e,below:r})},vert:function(e){return e===r?t:(r=e,n(\"vert\",{x:e}))},log:function(t){return\"string\"!=typeof t&&(t=JSON.stringify(t,!1,\" \")),n(\"log\",{txt:t})},reset:function(){return n(\"reset\")},selected:function(t){return n(\"selected\",{segs:t})},chainStart:function(t){return n(\"chain_start\",{seg:t})},chainRemoveHead:function(t,e){return n(\"chain_rem_head\",{index:t,pt:e})},chainRemoveTail:function(t,e){return n(\"chain_rem_tail\",{index:t,pt:e})},chainNew:function(t,e){return n(\"chain_new\",{pt1:t,pt2:e})},chainMatch:function(t){return n(\"chain_match\",{index:t})},chainClose:function(t){return n(\"chain_close\",{index:t})},chainAddHead:function(t,e){return n(\"chain_add_head\",{index:t,pt:e})},chainAddTail:function(t,e){return n(\"chain_add_tail\",{index:t,pt:e})},chainConnect:function(t,e){return n(\"chain_con\",{index1:t,index2:e})},chainReverse:function(t){return n(\"chain_rev\",{index:t})},chainJoin:function(t,e){return n(\"chain_join\",{index1:t,index2:e})},done:function(){return n(\"done\")}}}},{}],519:[function(t,e,r){e.exports=function(t){\"number\"!=typeof t&&(t=1e-10);var e={epsilon:function(e){return\"number\"==typeof e&&(t=e),t},pointAboveOrOnLine:function(e,r,n){var i=r[0],a=r[1],o=n[0],s=n[1],l=e[0];return(o-i)*(e[1]-a)-(s-a)*(l-i)>=-t},pointBetween:function(e,r,n){var i=e[1]-r[1],a=n[0]-r[0],o=e[0]-r[0],s=n[1]-r[1],l=o*a+i*s;return!(l<t)&&!(l-(a*a+s*s)>-t)},pointsSameX:function(e,r){r
"/*!\n",
" * repeat-string <https://github.com/jonschlinkert/repeat-string>\n",
" *\n",
" * Copyright (c) 2014-2015, Jon Schlinkert.\n",
" * Licensed under the MIT License.\n",
" */\n",
"\"use strict\";var n,i=\"\";e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"expected a string\");if(1===e)return t;if(2===e)return t+t;var r=t.length*e;if(n!==t||\"undefined\"==typeof n)n=t,i=\"\";else if(i.length>=r)return i.substr(0,r);for(;r>i.length&&e>1;)1&e&&(i+=t),e>>=1,t+=t;return i=(i+=t).substr(0,r)}},{}],542:[function(t,e,r){(function(t){(function(){e.exports=t.performance&&t.performance.now?function(){return performance.now()}:Date.now||function(){return+new Date}}).call(this)}).call(this,\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{}],543:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.length,r=t[t.length-1],n=e,i=e-2;i>=0;--i){var a=r,o=t[i];(l=o-((r=a+o)-a))&&(t[--n]=r,r=l)}var s=0;for(i=n;i<e;++i){var l;a=t[i];(l=(o=r)-((r=a+o)-a))&&(t[s++]=l)}return t[s++]=r,t.length=s,t}},{}],544:[function(t,e,r){\"use strict\";var n=t(\"two-product\"),i=t(\"robust-sum\"),a=t(\"robust-scale\"),o=t(\"robust-compress\");function s(t,e){for(var r=new Array(t.length-1),n=1;n<t.length;++n)for(var i=r[n-1]=new Array(t.length-1),a=0,o=0;a<t.length;++a)a!==e&&(i[o++]=t[n][a]);return r}function l(t){for(var e=new Array(t),r=0;r<t;++r){e[r]=new Array(t);for(var n=0;n<t;++n)e[r][n]=[\"m[\",r,\"][\",n,\"]\"].join(\"\")}return e}function c(t){if(2===t.length)return[\"sum(prod(\",t[0][0],\",\",t[1][1],\"),prod(-\",t[0][1],\",\",t[1][0],\"))\"].join(\"\");for(var e=[],r=0;r<t.length;++r)e.push([\"scale(\",c(s(t,r)),\",\",(n=r,1&n?\"-\":\"\"),t[0][r],\")\"].join(\"\"));return function t(e){if(1===e.length)return e[0];if(2===e.length)return[\"sum(\",e[0],\",\",e[1],\")\"].join(\"\");var r=e.length>>1;return[\"sum(\",t(e.slice(0,r)),\",\",t(e.slice(r)),\")\"].join(\"\")}(e);var n}function u(t){return new Function(\"sum\",\"scale\",\"prod\",\"compress\",[\"function robustDeterminant\",t,\"(m){return compress(\",c(l(t)),\")};return robustDeterminant\",t].join(\"\"))(i,a,n,o)}var f=[function(){return[0]},function(t){return[t[0][0]]}];!function(){for(;f.length<6;)f.push(u(f.length));for(var t=[],r=[\"function robustDeterminant(m){switch(m.length){\"],n=0;n<6;++n)t.push(\"det\"+n),r.push(\"case \",n,\":return det\",n,\"(m);\");r.push(\"}var det=CACHE[m.length];if(!det)det=CACHE[m.length]=gen(m.length);return det(m);}return robustDeterminant\"),t.push(\"CACHE\",\"gen\",r.join(\"\"));var i=Function.apply(void 0,t);for(e.exports=i.apply(void 0,f.concat([f,u])),n=0;n<f.length;++n)e.exports[n]=f[n]}()},{\"robust-compress\":543,\"robust-scale\":550,\"robust-sum\":553,\"two-product\":582}],545:[function(t,e,r){\"use strict\";var n=t(\"two-product\"),i=t(\"robust-sum\");e.exports=function(t,e){for(var r=n(t[0],e[0]),a=1;a<t.length;++a)r=i(r,n(t[a],e[a]));return r}},{\"robust-sum\":553,\"two-product\":582}],546:[function(t,e,r){\"use strict\";var n=t(\"two-product\"),i=t(\"robust-sum\"),a=t(\"robust-subtract\"),o=t(\"robust-scale\");function s(t,e){for(var r=new Array(t.length-1),n=1;n<t.length;++n)for(var i=r[n-1]=new Array(t.length-1),a=0,o=0;a<t.length;++a)a!==e&&(i[o++]=t[n][a]);return r}function l(t){if(1===t.length)return t[0];if(2===t.length)return[\"sum(\",t[0],\",\",t[1],\")\"].join(\"\");var e=t.length>>1;return[\"sum(\",l(t.slice(0,e)),\",\",l(t.slice(e)),\")\"].join(\"\")}function c(t,e){if(\"m\"===t.charAt(0)){if(\"w\"===e.charAt(0)){var r=t.split(\"[\");return[\"w\",e.substr(1),\"m\",r[0].substr(1)].join(\"\")}return[\"prod(\",t,\",\",e,\")\"].join(\"\")}return c(e,t)}function u(t){if(2===t.length)return[[\"diff(\",c(t[0][0],t[1][1]),\",\",c(t[1][0],t[0][1]),\")\"].join(\"\")];for(var e=[],r=0;r<t.length;++r)e.push([\"scale(\",l(u(s(t,r))),\",\",(n=r,!0&n?\"-\":\"\"),t[0][r],\")\"].join(\"\"));return e;var n}function f(t,e){for(var r=[],n=0;n<e-2;++n)r.push([\"prod(m\",t,\"[\",n,\"],m\",t,\"[\",n,\"])\"].join(\"\"));return l(r)}function h(t){for(var e=[],r=[],c=function(t){for(var e=new Array(t),r=0;r<t;++r){e[r]=new Array(t);for(var n=0;n<t;++n)e[r][n]=[\"m\",n,\"[\",t-r-2,\"]\"].join(\"\")}return e}(t),h=0;h<t;++h)c[0][h]=\
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"name": "Smoker",
"type": "bar",
"x": [
"False",
"True"
],
"xaxis": "x",
"y": [
77,
377
],
"yaxis": "y"
},
{
"name": "Male",
"type": "bar",
"x": [
"False",
"True"
],
"xaxis": "x2",
"y": [
179,
275
],
"yaxis": "y2"
},
{
"name": "Urban",
"type": "bar",
"x": [
"False",
"True"
],
"xaxis": "x3",
"y": [
217,
237
],
"yaxis": "y3"
}
],
"layout": {
"annotations": [
{
"font": {
"size": 16
},
"showarrow": false,
"text": "Smoker",
"x": 0.14444444444444446,
"xanchor": "center",
"xref": "paper",
"y": 1,
"yanchor": "bottom",
"yref": "paper"
},
{
"font": {
"size": 16
},
"showarrow": false,
"text": "Male",
"x": 0.5,
"xanchor": "center",
"xref": "paper",
"y": 1,
"yanchor": "bottom",
"yref": "paper"
},
{
"font": {
"size": 16
},
"showarrow": false,
"text": "Urban",
"x": 0.8555555555555556,
"xanchor": "center",
"xref": "paper",
"y": 1,
"yanchor": "bottom",
"yref": "paper"
}
],
"bargap": 0.05,
"barmode": "relative",
"height": 400,
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"width": 700,
"xaxis": {
"anchor": "y",
"domain": [
0,
0.2888888888888889
]
},
"xaxis2": {
"anchor": "y2",
"domain": [
0.35555555555555557,
0.6444444444444445
]
},
"xaxis3": {
"anchor": "y3",
"domain": [
0.7111111111111111,
1
]
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
]
},
"yaxis2": {
"anchor": "x2",
"domain": [
0,
1
]
},
"yaxis3": {
"anchor": "x3",
"domain": [
0,
1
]
}
}
},
"text/html": [
"<div> <div id=\"844ad0ee-17b5-4575-8042-3c40ec60debb\" class=\"plotly-graph-div\" style=\"height:400px; width:700px;\"></div> <script type=\"text/javascript\"> require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById(\"844ad0ee-17b5-4575-8042-3c40ec60debb\")) { Plotly.newPlot( \"844ad0ee-17b5-4575-8042-3c40ec60debb\", [{\"name\": \"Smoker\", \"type\": \"bar\", \"x\": [\"False\", \"True\"], \"xaxis\": \"x\", \"y\": [77, 377], \"yaxis\": \"y\"}, {\"name\": \"Male\", \"type\": \"bar\", \"x\": [\"False\", \"True\"], \"xaxis\": \"x2\", \"y\": [179, 275], \"yaxis\": \"y2\"}, {\"name\": \"Urban\", \"type\": \"bar\", \"x\": [\"False\", \"True\"], \"xaxis\": \"x3\", \"y\": [217, 237], \"yaxis\": \"y3\"}], {\"annotations\": [{\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Smoker\", \"x\": 0.14444444444444446, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Male\", \"x\": 0.5, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}, {\"font\": {\"size\": 16}, \"showarrow\": false, \"text\": \"Urban\", \"x\": 0.8555555555555556, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}], \"bargap\": 0.05, \"barmode\": \"relative\", \"height\": 400, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201
" \n",
"var gd = document.getElementById('844ad0ee-17b5-4575-8042-3c40ec60debb');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"features = {'Smoker': df['smoking_status_smokes'].values,\n",
" 'Male': df['gender_Male'].values,\n",
" 'Urban': df['Residence_type_Urban']}\n",
"df_vis = pd.DataFrame(features)\n",
"\n",
"fig = make_subplots(rows=1, cols=3, subplot_titles=('Smoker', 'Male', 'Urban'))\n",
"L= len(df_vis)\n",
"\n",
"cnames = list(df_vis.columns)\n",
"for k, name in enumerate(cnames):\n",
" n_true = df_vis[name].sum()\n",
" fig.add_trace(go.Bar(x=['False', 'True'], y=[n_true, L-n_true], name=name ), 1,k+1)\n",
"fig.update_layout(barmode='relative', bargap=0.05, width=700, height=400)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regresja liniowa"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"X_cols = list(set(df_train.columns) - {'stroke'})\n",
"y_cols = 'stroke'\n",
"\n",
"X_train_lin = df_train[X_cols]\n",
"y_train_lin = df_train[y_cols]\n",
"\n",
"X_test_lin = df_test[X_cols]\n",
"y_test_lin = df_test[y_cols]\n",
"\n",
"clf_lin = LinearRegression().fit(X_train_lin, y_train_lin)\n",
"test_pred_lin = clf_lin.predict(X_test_lin)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def regression_results(y_true, y_pred):\n",
"\n",
" # Regression metrics\n",
" explained_variance=metrics.explained_variance_score(y_true, y_pred)\n",
" mean_absolute_error=metrics.mean_absolute_error(y_true, y_pred) \n",
" mse=metrics.mean_squared_error(y_true, y_pred) \n",
" mean_squared_log_error=metrics.mean_squared_log_error(y_true, y_pred)\n",
" median_absolute_error=metrics.median_absolute_error(y_true, y_pred)\n",
" r2=metrics.r2_score(y_true, y_pred)\n",
"\n",
" print('explained_variance: ', round(explained_variance,4)) \n",
" print('mean_squared_log_error: ', round(mean_squared_log_error,4))\n",
" print('r2: ', round(r2,4))\n",
" print('MAE: ', round(mean_absolute_error,4))\n",
" print('MSE: ', round(mse,4))\n",
" print('RMSE: ', round(np.sqrt(mse),4))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"explained_variance: 0.0292\n",
"mean_squared_log_error: 0.1162\n",
"r2: 0.0272\n",
"MAE: 0.2418\n",
"MSE: 0.2418\n",
"RMSE: 0.4917\n"
]
}
],
"source": [
"test_pred_lin = np.round(np.clip(test_pred_lin, 0, 1))\n",
"regression_results(y_test_lin, test_pred_lin)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.79 0.76 0.77 49\n",
" 1 0.73 0.76 0.74 42\n",
"\n",
" accuracy 0.76 91\n",
" macro avg 0.76 0.76 0.76 91\n",
"weighted avg 0.76 0.76 0.76 91\n",
"\n"
]
}
],
"source": [
"print(metrics.classification_report(y_test_lin, test_pred_lin))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 1.,\n",
" 0., 0., 1., 0., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 1., 1.,\n",
" 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 1., 0., 0., 1., 1.,\n",
" 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 1.,\n",
" 0., 1., 0., 0., 1., 0.])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_pred_lin"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
" 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
" 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
" 0, 1, 0], dtype=int64)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test_lin.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regresja logistyczna"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"num_cols = ['age', 'avg_glucose_level', 'bmi']\n",
"cat_cols = list(set(df.columns) - {'stroke', 'age', 'avg_glucose_level', 'bmi'})\n",
"\n",
"scaler = StandardScaler()\n",
"scaler.fit(df_train[num_cols])\n",
"\n",
"X_num_train = scaler.transform(df_train[num_cols])\n",
"X_cat_train = df_train[cat_cols].to_numpy()\n",
"X_train = np.hstack((X_num_train, X_cat_train))\n",
"y_train = df_train['stroke']\n",
"\n",
"X_num_test = scaler.transform(df_test[num_cols])\n",
"X_cat_test = df_test[cat_cols].to_numpy()\n",
"X_test = np.hstack((X_num_test, X_cat_test))\n",
"y_test = df_test['stroke']"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"explained_variance: -0.0165\n",
"mean_squared_log_error: 0.1214\n",
"r2: -0.017\n",
"MAE: 0.2527\n",
"MSE: 0.2527\n",
"RMSE: 0.5027\n"
]
}
],
"source": [
"clf_log = LogisticRegression()\n",
"\n",
"clf_log.fit(X_train, y_train)\n",
"\n",
"test_pred_log = clf_log.predict(X_test)\n",
"\n",
"regression_results(y_test, test_pred_log)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Classification Report\n",
" precision recall f1-score support\n",
"\n",
" 0 0.77 0.76 0.76 49\n",
" 1 0.72 0.74 0.73 42\n",
"\n",
" accuracy 0.75 91\n",
" macro avg 0.75 0.75 0.75 91\n",
"weighted avg 0.75 0.75 0.75 91\n",
"\n"
]
}
],
"source": [
"print('\\nClassification Report')\n",
"print(metrics.classification_report(y_test, test_pred_log))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1,\n",
" 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
" 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
" 0, 1, 0], dtype=int64)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_pred_log"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
" 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
" 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
" 0, 1, 0], dtype=int64)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Naiwny Bayes"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"explained_variance: 0.2187\n",
"mean_squared_log_error: 0.1003\n",
"r2: 0.1599\n",
"MAE: 0.2088\n",
"MSE: 0.2088\n",
"RMSE: 0.4569\n"
]
}
],
"source": [
"gnb = GaussianNB()\n",
"\n",
"gnb = gnb.fit(X_train, y_train)\n",
"\n",
"test_pred_bay = gnb.predict(X_test)\n",
"\n",
"regression_results(y_test, test_pred_bay)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Classification Report\n",
" precision recall f1-score support\n",
"\n",
" 0 0.89 0.69 0.78 49\n",
" 1 0.72 0.90 0.80 42\n",
"\n",
" accuracy 0.79 91\n",
" macro avg 0.81 0.80 0.79 91\n",
"weighted avg 0.81 0.79 0.79 91\n",
"\n"
]
}
],
"source": [
"print('\\nClassification Report')\n",
"print(metrics.classification_report(y_test, test_pred_bay))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
" 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n",
" 0, 1, 0], dtype=int64)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_pred_bay"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
" 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
" 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
" 0, 1, 0], dtype=int64)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test.values"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Reference in New Issue Copy Permalink