Datasets
Models
Downloads: 1
40521 lines (40520 with data), 2.0 MB
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"This project focuses on analyzing the **Stroke Prediction Dataset** to develop a _machine learning model_ that can predict a patient's likelihood of suffering a stroke. According to the World Health Organization, strokes are the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. This makes early prediction and prevention of strokes critically important.\n",
"\n",
"The dataset contains various attributes about patients including:\n",
"\n",
"- Demographics\n",
" - Age\n",
" - Gender\n",
"- Health factors\n",
" - Hypertension\n",
" - Heart disease\n",
" - Average glucose levels\n",
" - BMI\n",
"- Lifestyle\n",
" - Smoking status\n",
" - Work type\n",
"- Location\n",
" - Urban vs rural residence\n",
"\n",
"The target variable is whether the patient suffered a `stroke`.\n",
"\n",
"As a data analyst working with **The Johns Hopkins Hospital**, our objective is to thoroughly explore this data to uncover patterns and insights that can inform the development of a robust predictive model. This will enable doctors to identify high-risk patients and advise them and their families on precautionary measures.\n",
"\n",
"The analysis will progress through the following key steps:\n",
"\n",
"1. **Exploratory Data Analysis** - Examining the distributions, ranges, and relationships between the features and target variable through statistical summaries and visualizations. Checking data quality.\n",
"\n",
"2. **Statistical Inference** - Formulating and testing hypotheses about stroke risk factors and quantifying uncertainty through confidence intervals.\n",
"\n",
"3. **Machine Learning Modeling** - Applying a range of classification algorithms including logistic regression, decision trees, random forests and more to predict stroke likelihood. Tuning hyperparameters and building ensembles to optimize predictive performance.\n",
"\n",
"4. **Model Deployment** - Selecting the top performing model and deploying it to enable real-time stroke risk prediction, potentially as a web app or containerized microservice.\n",
"\n",
"Throughout this notebook, detailed commentary will be provided on the analytical approach, key findings, model results and ideas for further enhancement. The goal is to demonstrate a thoughtful, thorough analysis while documenting reproducible steps from data intake through model deployment.\n",
"\n",
"By predicting stroke risk, this project aims to arm healthcare providers with a powerful tool to identify and engage high-risk patients, ultimately reducing the devastating impact of this condition. _Let's begin the analysis to see what insights the data holds._\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"sys.path.append(\"../src/utils\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import joblib\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import pingouin as pg\n",
"\n",
"from IPython.display import Image\n",
"\n",
"from catboost import CatBoostClassifier\n",
"from scipy import stats\n",
"from sklearn.base import BaseEstimator, ClassifierMixin\n",
"from sklearn.ensemble import VotingClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import (\n",
" StratifiedKFold,\n",
" train_test_split,\n",
")\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler\n",
"from skopt import BayesSearchCV\n",
"from skopt.space import Categorical, Integer, Real\n",
"import xgboost as xgb\n",
"import lightgbm as lgb\n",
"\n",
"import shap\n",
"\n",
"from stroke_risk_utils import (\n",
" plot_combined_histograms,\n",
" plot_combined_bar_charts,\n",
" plot_combined_boxplots,\n",
" plot_correlation_matrix,\n",
" flag_anomalies,\n",
" evaluate_model,\n",
" plot_model_performance,\n",
" plot_combined_confusion_matrices,\n",
" extract_feature_importances,\n",
" plot_feature_importances,\n",
" detect_anomalies_iqr,\n",
" calculate_cramers_v,\n",
")"
]
},
{
"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>id</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>9046</td>\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>1</th>\n",
" <td>51676</td>\n",
" <td>Female</td>\n",
" <td>61.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Self-employed</td>\n",
" <td>Rural</td>\n",
" <td>202.21</td>\n",
" <td>NaN</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>31112</td>\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>60182</td>\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>1665</td>\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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id gender age hypertension heart_disease ever_married \\\n",
"0 9046 Male 67.0 0 1 Yes \n",
"1 51676 Female 61.0 0 0 Yes \n",
"2 31112 Male 80.0 0 1 Yes \n",
"3 60182 Female 49.0 0 0 Yes \n",
"4 1665 Female 79.0 1 0 Yes \n",
"\n",
" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
"0 Private Urban 228.69 36.6 formerly smoked \n",
"1 Self-employed Rural 202.21 NaN never smoked \n",
"2 Private Rural 105.92 32.5 never smoked \n",
"3 Private Urban 171.23 34.4 smokes \n",
"4 Self-employed Rural 174.12 24.0 never smoked \n",
"\n",
" stroke \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stroke_df = pd.read_csv(\"../data/stroke_dataset.csv\")\n",
"stroke_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of duplicate rows: 0\n"
]
}
],
"source": [
"duplicates = stroke_df.duplicated().sum()\n",
"print(f\"Number of duplicate rows: {duplicates}\")\n",
"if duplicates > 0:\n",
" stroke_df = stroke_df.drop_duplicates()\n",
" print(\"Duplicates removed.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great, we can see that `there are no duplicates` in the dataset, therefore we can move forward.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5110 entries, 0 to 5109\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 id 5110 non-null int64 \n",
" 1 gender 5110 non-null object \n",
" 2 age 5110 non-null float64\n",
" 3 hypertension 5110 non-null int64 \n",
" 4 heart_disease 5110 non-null int64 \n",
" 5 ever_married 5110 non-null object \n",
" 6 work_type 5110 non-null object \n",
" 7 Residence_type 5110 non-null object \n",
" 8 avg_glucose_level 5110 non-null float64\n",
" 9 bmi 4909 non-null float64\n",
" 10 smoking_status 5110 non-null object \n",
" 11 stroke 5110 non-null int64 \n",
"dtypes: float64(3), int64(4), object(5)\n",
"memory usage: 479.2+ KB\n"
]
}
],
"source": [
"stroke_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great, we can see that the dataset contains a `mix of integer, float, and object data types`, which are appropriate for the corresponding variables. That being said, we can check for missing values.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id 0\n",
"gender 0\n",
"age 0\n",
"hypertension 0\n",
"heart_disease 0\n",
"ever_married 0\n",
"work_type 0\n",
"Residence_type 0\n",
"avg_glucose_level 0\n",
"bmi 201\n",
"smoking_status 0\n",
"stroke 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(stroke_df.isnull().sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dataset contains 5110 entries and 12 columns related to potential stroke risk factors.\n",
"\n",
"**Quick Facts:**\n",
"\n",
"- **Features:** `id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status`\n",
"- **Target Variable:** stroke (binary: 0 or 1)\n",
"- **Data Types:** Mixture of numerical (int64, float64) and categorical (object) features\n",
"- **Missing Values:** 201 in 'bmi' column (3.93% of dataset)\n",
"\n",
"**Key Observations:**\n",
"\n",
"1. Diverse risk factors: demographic, health conditions, lifestyle, and biometric measurements\n",
"2. Binary target variable (stroke occurrence)\n",
"3. Potential for class imbalance in target variable (to be checked)\n",
"\n",
"**Initial Steps:**\n",
"\n",
"1. Clean data: rename columns, handle missing values.\n",
"2. Explore feature distributions and relationships with target\n",
"3. Conduct statistical tests to validate risk factor relationships\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"stroke_df = stroke_df.rename(columns={\"Residence_type\": \"residence_type\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, we will handle missing values in the `bmi` column by dropping the rows with missing values, as they account for only 3.93% of the dataset.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>id</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>9046</td>\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>31112</td>\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>60182</td>\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>1665</td>\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>56669</td>\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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id gender age hypertension heart_disease ever_married \\\n",
"0 9046 Male 67.0 0 1 Yes \n",
"2 31112 Male 80.0 0 1 Yes \n",
"3 60182 Female 49.0 0 0 Yes \n",
"4 1665 Female 79.0 1 0 Yes \n",
"5 56669 Male 81.0 0 0 Yes \n",
"\n",
" work_type residence_type avg_glucose_level bmi smoking_status \\\n",
"0 Private Urban 228.69 36.6 formerly smoked \n",
"2 Private Rural 105.92 32.5 never smoked \n",
"3 Private Urban 171.23 34.4 smokes \n",
"4 Self-employed Rural 174.12 24.0 never smoked \n",
"5 Private Urban 186.21 29.0 formerly smoked \n",
"\n",
" stroke \n",
"0 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 \n",
"5 1 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stroke_df = stroke_df.dropna(subset=[\"bmi\"])\n",
"stroke_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With missing values in `bmi` handled and features renamed, let's examine the dataset structure.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" count mean std min 25% \\\n",
"id 4909.0 37064.313506 20995.098457 77.00 18605.00 \n",
"age 4909.0 42.865374 22.555115 0.08 25.00 \n",
"hypertension 4909.0 0.091872 0.288875 0.00 0.00 \n",
"heart_disease 4909.0 0.049501 0.216934 0.00 0.00 \n",
"avg_glucose_level 4909.0 105.305150 44.424341 55.12 77.07 \n",
"bmi 4909.0 28.893237 7.854067 10.30 23.50 \n",
"stroke 4909.0 0.042575 0.201917 0.00 0.00 \n",
"\n",
" 50% 75% max \n",
"id 37608.00 55220.00 72940.00 \n",
"age 44.00 60.00 82.00 \n",
"hypertension 0.00 0.00 1.00 \n",
"heart_disease 0.00 0.00 1.00 \n",
"avg_glucose_level 91.68 113.57 271.74 \n",
"bmi 28.10 33.10 97.60 \n",
"stroke 0.00 0.00 1.00 \n"
]
}
],
"source": [
"print(stroke_df.describe().T)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Current Observations:**\n",
"\n",
"- **Numerical Features:**\n",
" - `age`: Average 42.87 years, range 0.08 to 82.\n",
" - `avg_glucose_level`: Average 105.31, large standard deviation (44.42).\n",
" - `bmi`: Average 28.89, range 10.30 to 97.60.\n",
"- **Binary Features:**\n",
" - `hypertension`, `heart_disease`, and `stroke` are binary (0 or 1).\n",
" - Low prevalence of hypertension and heart disease.\n",
" - `stroke` (target variable) has low prevalence (about 4%), indicating class imbalance.\n",
"\n",
"**Next Step: Analyze Distributions of All Variables**\n",
"\n",
"Prior to encoding, it's crucial to comprehensively analyze the distributions of both numerical and categorical variables. This analysis will provide valuable insights into our dataset's characteristics and guide our encoding and preprocessing strategies.\n",
"\n",
"We should proceed as follows:\n",
"\n",
"1. **Numerical Variables:**\n",
"\n",
" - Create histograms and box plots for `age`, `avg_glucose_level`, and `bmi`.\n",
" - Look for outliers, skewness, and any unusual patterns.\n",
" - Consider if any transformations (e.g., log transformation) might be beneficial.\n",
"\n",
"2. **Binary Variables:**\n",
"\n",
" - Create bar plots for `hypertension`, `heart_disease`, and `stroke`.\n",
" - Quantify the exact prevalence of each condition.\n",
" - For `stroke`, our target variable, consider strategies to handle class imbalance.\n",
"\n",
"3. **Categorical Variables:**\n",
" - Create bar plots for `gender`, `ever_married`, `work_type`, `residence_type`, and `smoking_status`.\n",
" - Examine the distribution of categories within each variable.\n",
" - Look for any categories with very low frequency, which might need special handling.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"numerical_features = [\"age\", \"avg_glucose_level\", \"bmi\"]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"marker": {
"color": "#CC7B5C",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "age",
"nbinsx": 30,
"type": "histogram",
"x": [
67,
80,
49,
79,
81,
74,
69,
78,
81,
61,
54,
79,
50,
64,
75,
60,
71,
52,
79,
82,
71,
80,
65,
69,
57,
42,
82,
80,
48,
82,
74,
72,
58,
49,
78,
54,
82,
60,
76,
58,
81,
39,
79,
77,
63,
82,
73,
54,
56,
80,
67,
45,
78,
70,
76,
59,
80,
67,
66,
63,
52,
80,
80,
79,
43,
59,
79,
68,
54,
61,
70,
47,
74,
79,
81,
57,
80,
45,
78,
70,
58,
57,
69,
64,
77,
74,
81,
57,
50,
54,
79,
53,
79,
80,
45,
68,
71,
61,
74,
38,
77,
58,
53,
80,
56,
72,
80,
82,
73,
78,
69,
68,
71,
76,
76,
76,
75,
82,
59,
74,
69,
72,
66,
78,
57,
70,
68,
80,
68,
55,
77,
50,
57,
71,
81,
78,
70,
78,
56,
79,
81,
60,
80,
72,
80,
78,
81,
46,
59,
32,
61,
63,
63,
82,
82,
65,
51,
59,
68,
72,
59,
70,
69,
74,
80,
77,
78,
54,
78,
81,
78,
78,
63,
39,
51,
63,
48,
81,
79,
78,
55,
73,
81,
79,
63,
81,
49,
57,
81,
39,
80,
81,
79,
73,
77,
78,
68,
57,
77,
51,
60,
66,
57,
68,
68,
57,
14,
75,
78,
3,
58,
8,
70,
14,
47,
52,
75,
32,
74,
79,
79,
37,
37,
40,
35,
20,
42,
44,
79,
65,
57,
49,
71,
59,
25,
67,
38,
54,
70,
27,
47,
58,
3,
58,
14,
32,
23,
55,
17,
59,
13,
4,
16,
67,
22,
45,
66,
58,
69,
58,
53,
78,
44,
66,
27,
43,
13,
51,
30,
46,
43,
79,
27,
38,
78,
57,
61,
37,
48,
29,
22,
11,
22,
53,
27,
76,
57,
8,
21,
18,
78,
78,
33,
52,
51,
11,
48,
82,
54,
49,
24,
37,
64,
66,
23,
68,
55,
25,
48,
79,
47,
20,
60,
32,
66,
43,
36,
52,
29,
39,
76,
0.64,
34,
72,
43,
57,
44,
4,
44,
18,
41,
23,
53,
56,
0.88,
25,
14,
53,
4,
39,
53,
5,
4,
37,
70,
24,
55,
70,
34,
26,
39,
38,
20,
17,
60,
75,
31,
58,
5,
13,
22,
52,
61,
78,
57,
21,
22,
80,
7,
64,
64,
38,
12,
33,
3,
37,
22,
24,
75,
12,
63,
52,
68,
5,
56,
43,
40,
53,
52,
31,
69,
64,
64,
62,
2,
53,
65,
22,
26,
53,
20,
63,
13,
21,
65,
65,
42,
72,
13,
29,
59,
42,
13,
75,
33,
43,
11,
7,
16,
44,
78,
25,
69,
61,
43,
38,
68,
26,
18,
43,
43,
32,
81,
25,
66,
11,
53,
78,
9,
48,
31,
20,
21,
42,
35,
53,
60,
74,
23,
55,
71,
5,
14,
15,
66,
56,
60,
57,
29,
76,
52,
42,
71,
64,
39,
46,
51,
31,
22,
41,
28,
44,
68,
49,
10,
31,
67,
70,
38,
5,
54,
30,
23,
31,
72,
23,
25,
51,
20,
12,
57,
65,
16,
43,
30,
29,
54,
57,
42,
18,
9,
26,
40,
77,
15,
76,
52,
55,
5,
55,
36,
46,
5,
39,
12,
67,
1.8,
72,
0.32,
64,
27,
25,
81,
32,
57,
52,
54,
36,
18,
45,
29,
51,
23,
54,
54,
18,
26,
72,
5,
65,
42,
47,
24,
57,
51,
48,
56,
30,
63,
46,
53,
1.08,
15,
59,
24,
53,
39,
23,
31,
5,
44,
1.8,
40,
7,
56,
42,
48,
82,
52,
42,
73,
55,
50,
43,
33,
80,
33,
74,
65,
40,
52,
66,
20,
51,
60,
59,
58,
42,
28,
57,
66,
56,
48,
37,
27,
15,
61,
80,
43,
53,
70,
58,
49,
65,
54,
74,
56,
79,
71,
28,
9,
4,
36,
78,
52,
80,
28,
38,
56,
75,
76,
10,
40,
82,
17,
58,
12,
53,
26,
48,
25,
27,
16,
20,
2,
1.8,
28,
32,
35,
52,
55,
42,
52,
18,
13,
45,
29,
12,
37,
30,
44,
79,
38,
45,
66,
19,
49,
66,
47,
59,
23,
79,
37,
78,
70,
34,
28,
29,
13,
22,
56,
81,
28,
9,
50,
70,
30,
51,
24,
17,
9,
50,
49,
6,
30,
73,
20,
42,
79,
55,
27,
71,
23,
71,
56,
31,
64,
46,
55,
2,
58,
78,
1.16,
35,
64,
18,
19,
47,
54,
39,
37,
17,
20,
31,
56,
31,
82,
33,
42,
20,
22,
61,
31,
31,
17,
30,
69,
63,
19,
7,
51,
38,
2,
58,
69,
64,
31,
59,
54,
24,
19,
17,
29,
42,
31,
29,
23,
25,
14,
66,
77,
79,
52,
41,
31,
39,
51,
40,
48,
55,
41,
39,
41,
5,
38,
40,
12,
20,
53,
21,
70,
61,
70,
38,
59,
36,
31,
65,
61,
2,
52,
33,
5,
32,
54,
51,
39,
40,
19,
9,
53,
23,
19,
1,
48,
71,
44,
53,
70,
1.4,
49,
17,
40,
46,
32,
2,
40,
19,
43,
46,
71,
31,
41,
65,
20,
51,
37,
49,
62,
28,
80,
45,
37,
3,
26,
58,
59,
74,
17,
62,
37,
19,
59,
59,
62,
80,
54,
27,
4,
81,
41,
27,
51,
59,
51,
71,
48,
49,
25,
31,
68,
39,
32,
39,
63,
69,
29,
1.72,
58,
32,
63,
57,
77,
8,
21,
16,
8,
71,
74,
7,
19,
64,
23,
75,
80,
40,
24,
74,
52,
54,
19,
23,
13,
51,
60,
79,
75,
69,
57,
47,
33,
51,
47,
15,
1,
20,
6,
50,
31,
15,
3,
30,
17,
24,
5,
33,
30,
66,
27,
21,
55,
19,
75,
71,
16,
49,
10,
37,
59,
7,
61,
34,
9,
46,
0.88,
50,
19,
21,
2,
37,
20,
50,
61,
48,
82,
64,
50,
1.4,
0.24,
55,
29,
4,
70,
35,
80,
54,
39,
26,
6,
41,
42,
9,
55,
71,
14,
77,
50,
49,
51,
79,
63,
66,
20,
37,
22,
60,
39,
53,
55,
63,
57,
82,
56,
41,
8,
34,
75,
57,
72,
21,
51,
15,
24,
30,
82,
62,
79,
19,
45,
5,
57,
31,
61,
27,
61,
53,
76,
57,
9,
34,
51,
61,
19,
50,
80,
13,
55,
67,
30,
67,
82,
5,
81,
48,
66,
38,
8,
47,
27,
53,
27,
36,
50,
32,
58,
73,
62,
50,
51,
19,
30,
45,
30,
28,
70,
0.32,
23,
18,
41,
52,
77,
34,
67,
1.64,
59,
62,
47,
22,
33,
27,
44,
18,
47,
66,
42,
56,
69,
65,
53,
36,
69,
19,
3,
15,
59,
61,
28,
52,
33,
80,
47,
2,
48,
1.56,
26,
62,
1.72,
38,
28,
67,
57,
40,
10,
55,
7,
56,
37,
69,
45,
28,
52,
46,
45,
21,
4,
45,
71,
46,
3,
79,
71,
74,
52,
49,
32,
62,
78,
48,
18,
80,
34,
40,
59,
11,
35,
29,
23,
59,
44,
62,
32,
2,
79,
2,
43,
43,
62,
73,
31,
42,
20,
55,
23,
33,
14,
79,
64,
76,
46,
80,
21,
33,
32,
0.72,
49,
41,
37,
58,
79,
23,
78,
41,
68,
30,
1.88,
32,
20,
20,
10,
8,
65,
65,
46,
53,
79,
43,
56,
3,
32,
41,
53,
67,
33,
21,
26,
69,
15,
3,
1.24,
80,
2,
50,
30,
15,
71,
4,
2,
11,
45,
22,
63,
50,
25,
49,
39,
43,
67,
48,
38,
14,
63,
37,
20,
60,
13,
34,
22,
46,
53,
28,
62,
11,
66,
80,
8,
53,
63,
31,
43,
79,
8,
38,
80,
49,
50,
13,
73,
60,
44,
79,
5,
51,
5,
65,
81,
46,
44,
25,
49,
80,
22,
61,
3,
50,
45,
55,
21,
0.8,
50,
73,
58,
30,
55,
57,
77,
29,
46,
40,
29,
37,
35,
54,
56,
26,
75,
44,
63,
17,
46,
47,
50,
76,
47,
47,
49,
71,
65,
40,
82,
64,
50,
40,
74,
30,
68,
6,
4,
60,
59,
72,
62,
13,
72,
1.08,
35,
54,
40,
3,
2,
41,
61,
58,
17,
64,
32,
42,
39,
10,
78,
68,
24,
59,
45,
12,
37,
43,
12,
13,
74,
2,
59,
21,
55,
14,
53,
75,
37,
49,
55,
57,
45,
46,
12,
12,
77,
6,
76,
52,
62,
82,
69,
78,
37,
50,
41,
54,
43,
37,
9,
11,
56,
5,
17,
6,
57,
18,
2,
76,
1,
54,
45,
54,
67,
53,
47,
44,
65,
40,
3,
81,
24,
9,
52,
63,
78,
22,
2,
79,
49,
74,
62,
16,
62,
50,
49,
24,
21,
17,
22,
13,
21,
8,
16,
18,
68,
49,
80,
31,
25,
39,
35,
71,
71,
40,
41,
2,
3,
53,
1.88,
60,
5,
30,
49,
68,
23,
2,
24,
41,
63,
64,
38,
1.24,
38,
43,
27,
42,
46,
67,
34,
51,
77,
30,
81,
12,
18,
35,
82,
53,
75,
32,
27,
56,
71,
78,
44,
62,
27,
42,
66,
82,
49,
77,
59,
54,
38,
55,
13,
45,
54,
51,
47,
25,
61,
55,
23,
59,
4,
65,
50,
42,
51,
66,
22,
5,
19,
47,
12,
53,
26,
62,
81,
63,
25,
8,
76,
44,
65,
54,
24,
65,
12,
33,
74,
18,
77,
48,
52,
23,
25,
44,
78,
37,
32,
67,
41,
2,
75,
17,
13,
25,
57,
31,
61,
46,
15,
47,
68,
0.4,
40,
62,
45,
47,
35,
51,
60,
59,
1.24,
18,
81,
15,
73,
0.08,
53,
45,
70,
56,
7,
66,
53,
20,
15,
51,
34,
33,
53,
69,
28,
81,
20,
1.48,
2,
77,
38,
23,
67,
59,
41,
21,
41,
54,
25,
6,
21,
32,
64,
70,
44,
60,
56,
53,
59,
53,
63,
42,
63,
52,
62,
68,
36,
3,
40,
8,
65,
28,
40,
65,
58,
44,
54,
79,
62,
37,
63,
27,
6,
30,
15,
50,
32,
33,
62,
82,
71,
20,
44,
57,
50,
50,
44,
47,
35,
8,
3,
29,
36,
19,
41,
23,
14,
35,
45,
52,
19,
21,
8,
35,
33,
53,
31,
43,
61,
57,
11,
36,
16,
60,
13,
69,
22,
63,
45,
58,
61,
30,
78,
68,
16,
38,
39,
51,
37,
16,
39,
24,
51,
50,
27,
73,
65,
76,
53,
65,
31,
1.72,
45,
56,
62,
35,
16,
31,
5,
26,
10,
22,
82,
50,
47,
31,
22,
12,
63,
78,
4,
76,
81,
39,
59,
2,
37,
78,
75,
34,
48,
13,
54,
13,
24,
76,
1,
57,
46,
26,
56,
34,
25,
60,
22,
35,
48,
38,
12,
22,
0.64,
13,
78,
23,
35,
78,
50,
61,
43,
60,
51,
21,
16,
25,
36,
8,
48,
14,
60,
61,
60,
56,
9,
45,
14,
37,
52,
47,
82,
64,
12,
79,
77,
25,
26,
14,
42,
37,
8,
78,
18,
78,
55,
68,
47,
47,
51,
43,
78,
63,
45,
53,
12,
56,
29,
55,
49,
23,
53,
56,
56,
61,
45,
80,
47,
3,
63,
68,
76,
62,
30,
44,
47,
25,
31,
71,
32,
72,
44,
63,
37,
54,
56,
25,
19,
62,
27,
5,
20,
21,
44,
39,
38,
26,
51,
39,
5,
59,
45,
43,
80,
42,
8,
68,
5,
57,
44,
16,
60,
58,
43,
50,
1.48,
5,
9,
67,
21,
36,
70,
57,
52,
28,
41,
81,
33,
31,
42,
10,
34,
70,
33,
58,
25,
1.4,
47,
14,
82,
52,
72,
52,
61,
15,
1.56,
6,
3,
18,
53,
58,
31,
29,
5,
40,
75,
52,
39,
40,
78,
39,
17,
45,
0.56,
13,
26,
42,
44,
3,
25,
41,
51,
20,
25,
18,
37,
51,
2,
38,
64,
22,
71,
32,
32,
63,
0.24,
54,
25,
80,
31,
53,
35,
31,
60,
0.56,
21,
78,
59,
0.64,
10,
60,
11,
48,
50,
69,
20,
22,
55,
57,
29,
32,
54,
37,
58,
41,
72,
32,
54,
79,
56,
45,
6,
45,
60,
65,
57,
58,
8,
18,
49,
2,
52,
63,
57,
50,
12,
35,
35,
64,
42,
62,
3,
43,
52,
64,
59,
81,
15,
50,
73,
19,
30,
40,
47,
66,
47,
41,
63,
12,
1.32,
46,
8,
74,
74,
4,
26,
63,
48,
24,
47,
70,
41,
42,
57,
51,
15,
11,
27,
53,
52,
68,
65,
23,
43,
55,
52,
21,
78,
49,
27,
52,
22,
77,
57,
13,
14,
29,
49,
1.24,
21,
36,
42,
56,
46,
79,
25,
67,
37,
44,
36,
17,
50,
53,
39,
71,
18,
55,
52,
27,
24,
72,
41,
29,
26,
23,
43,
43,
49,
13,
26,
10,
39,
41,
25,
10,
54,
7,
53,
55,
6,
72,
42,
50,
55,
47,
3,
49,
12,
28,
40,
55,
9,
77,
18,
73,
67,
58,
2,
38,
5,
34,
78,
57,
17,
71,
19,
56,
64,
13,
40,
80,
62,
79,
9,
26,
1.72,
16,
40,
46,
45,
59,
31,
69,
73,
56,
80,
60,
40,
30,
66,
78,
37,
41,
34,
18,
69,
28,
3,
57,
34,
50,
55,
2,
27,
57,
73,
52,
65,
48,
54,
8,
45,
48,
77,
34,
33,
39,
39,
40,
46,
1,
5,
14,
49,
45,
13,
76,
14,
20,
9,
78,
49,
55,
43,
16,
58,
67,
71,
8,
18,
47,
59,
28,
67,
71,
56,
44,
6,
78,
32,
40,
63,
51,
22,
82,
17,
34,
42,
32,
73,
47,
34,
64,
52,
55,
64,
23,
18,
65,
12,
61,
66,
59,
47,
47,
71,
62,
28,
7,
68,
37,
75,
5,
1.08,
34,
49,
8,
50,
2,
1.16,
76,
18,
82,
67,
27,
57,
51,
68,
56,
18,
63,
31,
37,
64,
47,
1.8,
42,
37,
69,
13,
73,
48,
78,
21,
54,
19,
82,
80,
21,
46,
14,
6,
3,
47,
34,
41,
67,
5,
20,
63,
73,
62,
0.88,
50,
71,
30,
80,
64,
7,
36,
79,
35,
28,
72,
12,
25,
27,
62,
58,
30,
56,
28,
47,
16,
29,
26,
26,
55,
29,
29,
33,
19,
10,
3,
60,
16,
35,
41,
19,
79,
41,
1.64,
51,
33,
33,
48,
45,
61,
31,
60,
51,
53,
52,
42,
56,
14,
40,
58,
52,
30,
47,
34,
82,
31,
58,
47,
65,
13,
37,
9,
7,
37,
62,
14,
77,
36,
3,
78,
49,
44,
10,
8,
60,
56,
73,
65,
34,
13,
80,
46,
80,
65,
24,
33,
11,
57,
19,
82,
39,
14,
60,
3,
57,
79,
65,
67,
60,
36,
36,
26,
71,
78,
77,
74,
50,
9,
1.64,
54,
8,
36,
38,
7,
5,
14,
0.88,
30,
39,
17,
14,
52,
78,
2,
32,
0.88,
50,
48,
66,
46,
54,
81,
15,
77,
59,
48,
49,
44,
39,
34,
50,
62,
38,
43,
28,
58,
28,
18,
31,
55,
54,
73,
26,
73,
38,
75,
81,
20,
12,
78,
42,
16,
4,
60,
45,
23,
21,
67,
10,
24,
50,
14,
13,
71,
66,
32,
46,
48,
45,
55,
31,
25,
73,
14,
56,
60,
27,
28,
46,
11,
63,
59,
57,
35,
54,
7,
48,
69,
59,
72,
49,
73,
24,
26,
27,
78,
77,
59,
0.32,
39,
35,
75,
80,
35,
22,
39,
6,
62,
13,
56,
53,
5,
44,
13,
59,
35,
52,
76,
51,
79,
69,
78,
60,
10,
42,
21,
56,
59,
69,
25,
63,
25,
58,
28,
46,
53,
37,
12,
14,
60,
42,
65,
80,
32,
53,
50,
23,
45,
0.8,
49,
70,
82,
23,
79,
74,
78,
50,
34,
47,
52,
67,
62,
18,
75,
42,
29,
52,
18,
67,
66,
5,
18,
52,
44,
46,
36,
61,
7,
15,
61,
20,
2,
47,
25,
66,
2,
63,
53,
33,
23,
23,
81,
55,
69,
58,
70,
48,
15,
31,
2,
80,
69,
52,
80,
54,
2,
39,
38,
76,
45,
1.24,
77,
46,
50,
52,
41,
40,
59,
48,
42,
26,
39,
34,
54,
57,
1.56,
50,
64,
44,
38,
32,
17,
81,
15,
5,
70,
52,
67,
17,
61,
31,
58,
12,
30,
65,
23,
14,
27,
53,
58,
9,
59,
46,
41,
55,
43,
15,
36,
36,
47,
18,
19,
65,
17,
54,
46,
59,
48,
18,
56,
3,
41,
67,
60,
47,
24,
43,
17,
45,
24,
79,
79,
27,
64,
34,
50,
58,
26,
50,
80,
2,
46,
29,
6,
32,
16,
44,
23,
48,
34,
78,
42,
40,
10,
39,
42,
47,
14,
45,
58,
73,
62,
62,
51,
0.8,
57,
8,
69,
16,
56,
41,
1.24,
42,
57,
20,
8,
43,
62,
42,
63,
80,
13,
20,
71,
50,
76,
41,
76,
78,
5,
43,
49,
70,
24,
41,
20,
34,
32,
27,
44,
20,
52,
57,
29,
16,
35,
5,
63,
59,
63,
52,
50,
43,
27,
30,
8,
14,
23,
6,
37,
38,
3,
26,
58,
57,
58,
76,
79,
34,
75,
11,
71,
40,
24,
0.64,
82,
32,
81,
79,
62,
39,
60,
48,
24,
70,
17,
56,
3,
65,
72,
10,
29,
44,
46,
33,
0.24,
55,
56,
50,
78,
63,
31,
65,
51,
60,
69,
23,
46,
16,
26,
44,
23,
38,
18,
63,
23,
32,
8,
77,
41,
34,
25,
35,
15,
1.64,
4,
33,
28,
37,
50,
76,
72,
16,
44,
46,
46,
73,
29,
19,
27,
36,
61,
9,
25,
68,
26,
17,
75,
34,
53,
18,
48,
5,
79,
16,
32,
55,
70,
51,
39,
79,
80,
34,
59,
54,
22,
21,
4,
26,
62,
33,
51,
75,
42,
82,
61,
62,
59,
31,
12,
76,
71,
15,
44,
30,
47,
47,
60,
62,
47,
78,
35,
57,
12,
26,
33,
60,
35,
10,
50,
44,
41,
7,
45,
60,
49,
45,
55,
54,
39,
76,
28,
70,
28,
79,
30,
26,
57,
16,
40,
77,
36,
40,
2,
52,
78,
22,
33,
40,
20,
19,
25,
23,
28,
6,
37,
10,
34,
60,
22,
52,
61,
10,
36,
29,
69,
62,
62,
79,
13,
49,
41,
56,
8,
24,
61,
40,
22,
80,
60,
57,
55,
39,
51,
77,
32,
45,
49,
13,
74,
72,
57,
51,
55,
73,
12,
6,
76,
38,
50,
75,
1.16,
32,
12,
32,
13,
1.32,
63,
29,
53,
49,
81,
66,
61,
52,
8,
82,
43,
4,
45,
62,
42,
72,
26,
9,
31,
20,
22,
23,
57,
52,
64,
26,
54,
32,
66,
13,
77,
18,
11,
16,
73,
36,
10,
54,
42,
52,
32,
43,
28,
79,
11,
75,
55,
78,
48,
73,
56,
54,
55,
65,
69,
29,
64,
16,
75,
17,
24,
50,
52,
82,
56,
56,
18,
33,
57,
30,
52,
3,
44,
33,
24,
24,
54,
72,
37,
36,
22,
68,
24,
35,
32,
52,
21,
82,
33,
62,
57,
14,
63,
50,
12,
80,
74,
46,
66,
26,
70,
62,
15,
67,
63,
42,
49,
78,
61,
41,
73,
64,
65,
81,
47,
40,
46,
2,
56,
54,
26,
45,
57,
78,
2,
76,
51,
54,
36,
57,
25,
7,
76,
45,
66,
45,
79,
25,
53,
35,
78,
32,
0.56,
26,
34,
65,
15,
17,
71,
42,
30,
75,
9,
47,
75,
61,
61,
27,
55,
2,
51,
45,
36,
46,
48,
79,
50,
79,
32,
36,
27,
22,
5,
9,
31,
26,
43,
33,
34,
66,
44,
56,
67,
18,
55,
61,
0.08,
48,
44,
68,
52,
15,
2,
60,
37,
5,
25,
52,
79,
61,
36,
34,
2,
43,
1.48,
49,
2,
82,
81,
81,
1.08,
60,
1.88,
58,
8,
9,
34,
23,
74,
72,
47,
56,
5,
27,
38,
13,
41,
50,
41,
6,
62,
4,
59,
63,
9,
41,
72,
45,
80,
19,
37,
58,
77,
38,
49,
20,
23,
55,
16,
81,
23,
82,
48,
56,
61,
43,
39,
22,
41,
37,
65,
8,
51,
63,
48,
17,
75,
51,
51,
41,
75,
57,
72,
52,
11,
28,
78,
82,
74,
31,
53,
0.24,
67,
17,
29,
56,
15,
29,
79,
82,
17,
8,
23,
36,
47,
24,
60,
63,
44,
38,
26,
18,
18,
13,
13,
47,
80,
24,
72,
5,
51,
82,
5,
66,
15,
53,
78,
62,
41,
41,
78,
14,
1.48,
71,
32,
16,
68,
0.56,
59,
32,
58,
40,
41,
21,
1.88,
55,
67,
49,
42,
54,
25,
62,
75,
72,
14,
38,
59,
23,
63,
82,
34,
8,
28,
49,
24,
17,
69,
58,
50,
5,
34,
64,
47,
61,
14,
57,
53,
67,
57,
38,
13,
37,
39,
13,
57,
11,
68,
79,
24,
44,
46,
66,
61,
16,
37,
43,
27,
44,
14,
81,
54,
77,
27,
35,
30,
48,
15,
26,
38,
29,
49,
55,
52,
49,
21,
24,
18,
52,
70,
51,
58,
46,
79,
11,
61,
51,
21,
71,
13,
16,
61,
49,
65,
40,
44,
11,
20,
24,
44,
5,
41,
23,
7,
74,
61,
80,
35,
43,
25,
72,
45,
9,
33,
53,
67,
59,
1.32,
27,
64,
55,
40,
33,
33,
39,
74,
38,
53,
22,
5,
24,
27,
41,
20,
71,
65,
38,
25,
53,
50,
1.24,
55,
38,
72,
43,
30,
69,
61,
34,
43,
62,
48,
37,
64,
51,
69,
75,
27,
49,
82,
36,
45,
64,
32,
15,
1.64,
34,
17,
76,
61,
30,
18,
48,
0.16,
4,
38,
27,
50,
19,
8,
78,
0.8,
53,
73,
58,
62,
78,
51,
78,
40,
35,
82,
46,
51,
53,
55,
43,
81,
12,
20,
53,
1.32,
22,
75,
66,
7,
26,
14,
32,
6,
42,
4,
75,
80,
52,
11,
14,
74,
58,
80,
31,
28,
25,
71,
51,
15,
45,
53,
69,
42,
4,
64,
5,
44,
78,
69,
80,
7,
2,
80,
69,
58,
75,
60,
75,
10,
38,
28,
3,
31,
59,
31,
58,
57,
31,
43,
40,
34,
55,
39,
42,
44,
69,
16,
8,
1.48,
1.88,
69,
13,
53,
16,
64,
7,
41,
65,
61,
63,
54,
14,
45,
51,
8,
52,
39,
69,
71,
73,
54,
10,
26,
41,
71,
46,
15,
29,
8,
21,
56,
14,
78,
36,
57,
79,
26,
22,
72,
54,
8,
62,
28,
50,
7,
33,
55,
25,
25,
37,
58,
45,
60,
66,
80,
38,
11,
63,
19,
17,
19,
40,
49,
69,
46,
78,
63,
3,
1.8,
18,
46,
8,
53,
38,
74,
24,
78,
60,
12,
32,
5,
40,
19,
28,
44,
50,
50,
18,
1.64,
5,
39,
65,
26,
42,
34,
45,
43,
40,
35,
2,
61,
64,
32,
23,
51,
52,
75,
40,
39,
6,
32,
55,
23,
52,
58,
17,
3,
23,
56,
45,
14,
45,
57,
35,
8,
42,
8,
62,
43,
8,
40,
2,
27,
47,
53,
62,
50,
26,
19,
0.32,
45,
45,
54,
64,
2,
66,
73,
31,
1.88,
58,
12,
60,
23,
26,
36,
3,
21,
67,
35,
26,
47,
81,
1.64,
52,
28,
45,
20,
1.56,
65,
45,
77,
37,
50,
0.56,
30,
31,
4,
65,
68,
63,
46,
54,
66,
29,
78,
56,
57,
2,
49,
31,
47,
37,
65,
61,
66,
24,
66,
64,
38,
7,
63,
71,
18,
65,
57,
62,
1.8,
35,
50,
73,
23,
32,
38,
50,
39,
7,
15,
67,
32,
5,
56,
49,
11,
61,
76,
34,
20,
24,
39,
55,
16,
36,
38,
20,
38,
52,
49,
80,
75,
13,
54,
28,
61,
0.16,
59,
12,
38,
15,
31,
52,
61,
50,
41,
20,
67,
45,
73,
52,
51,
56,
49,
10,
51,
57,
76,
28,
57,
39,
41,
72,
45,
33,
62,
27,
31,
24,
1.88,
28,
24,
30,
38,
1.64,
0.72,
57,
27,
30,
42,
36,
44,
59,
61,
18,
44,
16,
13,
9,
0.16,
81,
38,
58,
5,
64,
38,
56,
58,
9,
34,
79,
62,
60,
42,
59,
54,
59,
15,
14,
5,
17,
13,
48,
37,
74,
54,
64,
26,
55,
74,
0.48,
41,
68,
9,
36,
17,
24,
69,
38,
69,
32,
25,
2,
79,
28,
16,
66,
57,
61,
74,
70,
8,
49,
30,
51,
10,
34,
40,
3,
45,
4,
52,
68,
8,
82,
36,
42,
27,
55,
31,
82,
57,
78,
55,
51,
80,
17,
61,
62,
14,
62,
56,
41,
59,
29,
39,
66,
35,
36,
35,
26,
31,
56,
36,
41,
50,
42,
79,
57,
68,
25,
69,
42,
37,
2,
78,
36,
10,
39,
4,
48,
72,
63,
28,
80,
49,
57,
59,
43,
47,
54,
57,
56,
66,
18,
81,
30,
8,
55,
79,
49,
53,
26,
14,
25,
20,
18,
38,
72,
52,
71,
46,
33,
37,
54,
5,
45,
14,
5,
35,
20,
51,
40,
79,
61,
33,
27,
28,
67,
35,
78,
20,
43,
27,
45,
11,
31,
30,
27,
34,
19,
3,
53,
81,
44,
59,
43,
5,
31,
47,
12,
40,
49,
38,
15,
81,
48,
24,
47,
13,
16,
64,
11,
55,
38,
37,
34,
52,
50,
37,
55,
2,
68,
27,
53,
5,
3,
20,
19,
31,
63,
49,
72,
50,
41,
40,
17,
3,
33,
12,
23,
31,
65,
43,
22,
7,
81,
10,
5,
24,
45,
46,
64,
25,
12,
81,
65,
40,
81,
36,
32,
31,
55,
39,
17,
31,
36,
5,
29,
2,
62,
45,
18,
29,
52,
69,
71,
33,
43,
39,
60,
54,
61,
0.24,
11,
75,
64,
78,
14,
66,
48,
44,
30,
1.48,
61,
54,
22,
47,
67,
53,
45,
65,
39,
57,
45,
28,
30,
31,
70,
17,
44,
65,
6,
15,
80,
54,
79,
60,
12,
36,
54,
63,
2,
28,
38,
37,
80,
43,
60,
66,
45,
29,
4,
48,
68,
57,
29,
30,
47,
73,
66,
39,
66,
52,
34,
79,
39,
61,
26,
37,
66,
16,
51,
15,
1.72,
60,
66,
75,
40,
52,
13,
44,
37,
2,
48,
81,
76,
58,
11,
9,
54,
37,
16,
11,
32,
26,
21,
45,
40,
41,
71,
16,
7,
27,
22,
79,
38,
64,
4,
23,
35,
66,
34,
19,
73,
70,
45,
34,
81,
0.72,
13,
57,
38,
21,
38,
41,
26,
48,
18,
66,
27,
18,
11,
7,
54,
43,
61,
31,
40,
71,
57,
13,
21,
63,
7,
78,
66,
41,
11,
55,
42,
51,
79,
28,
73,
42,
37,
78,
74,
59,
82,
71,
19,
18,
53,
62,
3,
49,
51,
53,
10,
54,
40,
3,
12,
56,
43,
28,
28,
56,
14,
63,
29,
48,
47,
63,
40,
40,
55,
43,
59,
78,
27,
49,
14,
17,
42,
36,
19,
55,
8,
56,
54,
15,
43,
52,
62,
54,
60,
7,
3,
39,
60,
60,
46,
26,
12,
16,
2,
82,
32,
62,
75,
81,
37,
28,
25,
44,
11,
54,
43,
56,
37,
60,
4,
51,
82,
56,
60,
35,
32,
45,
10,
68,
51,
8,
63,
4,
33,
56,
49,
30,
16,
1.8,
54,
49,
17,
11,
51,
74,
42,
69,
22,
25,
17,
64,
50,
52,
37,
31,
52,
56,
43,
11,
2,
16,
23,
40,
15,
48,
33,
46,
53,
68,
60,
57,
51,
0.4,
76,
46,
23,
9,
53,
4,
62,
37,
82,
33,
3,
14,
16,
40,
18,
29,
56,
33,
2,
36,
30,
31,
16,
58,
19,
47,
59,
40,
26,
17,
30,
19,
78,
55,
57,
33,
35,
32,
55,
28,
25,
45,
34,
33,
65,
62,
36,
31,
54,
53,
44,
77,
67,
48,
42,
72,
49,
1.32,
45,
63,
33,
32,
0.48,
63,
70,
57,
8,
54,
37,
59,
78,
59,
10,
21,
2,
55,
20,
38,
33,
14,
32,
32,
68,
70,
24,
44,
39,
81,
19,
69,
42,
8,
8,
28,
66,
66,
47,
78,
65,
78,
68,
65,
70,
59,
31,
32,
5,
55,
61,
48,
1.16,
50,
63,
67,
43,
59,
47,
50,
51,
44,
39,
24,
62,
61,
78,
15,
23,
79,
57,
9,
55,
82,
19,
41,
79,
23,
77,
78,
24,
81,
46,
29,
59,
41,
17,
8,
23,
45,
67,
78,
18,
35,
54,
71,
24,
60,
75,
48,
62,
20,
69,
60,
12,
28,
55,
51,
16,
56,
52,
59,
9,
34,
47,
46,
49,
10,
76,
82,
3,
76,
16,
44,
17,
73,
31,
17,
18,
73,
8,
42,
40,
79,
53,
58,
48,
17,
23,
9,
81,
16,
21,
29,
8,
39,
64,
9,
17,
8,
62,
73,
48,
17,
10,
74,
62,
5,
43,
39,
37,
15,
36,
34,
75,
53,
61,
45,
17,
17,
19,
54,
51,
44,
82,
49,
34,
63,
79,
81,
14,
31,
57,
32,
66,
22,
1.32,
20,
41,
48,
49,
73,
19,
51,
1.08,
31,
4,
1.8,
44,
4,
55,
34,
30,
7,
75,
14,
61,
55,
14,
36,
57,
20,
32,
43,
43,
1.08,
49,
53,
18,
13,
5,
37,
49,
45,
50,
12,
12,
5,
39,
41,
41,
47,
57,
16,
3,
65,
80,
58,
74,
62,
33,
21,
17,
49,
24,
56,
26,
54,
42,
34,
56,
39,
77,
12,
65,
7,
25,
31,
14,
5,
60,
53,
55,
69,
39,
0.32,
34,
80,
20,
45,
41,
54,
23,
75,
36,
75,
47,
15,
40,
71,
38,
10,
11,
0.72,
9,
53,
28,
81,
16,
24,
62,
62,
59,
19,
51,
38,
26,
13,
4,
55,
44,
31,
17,
23,
51,
46,
81,
38,
50,
34,
60,
50,
49,
2,
47,
34,
22,
11,
54,
60,
3,
46,
30,
63,
50,
65,
78,
36,
18,
44,
4,
60,
19,
15,
20,
76,
39,
58,
64,
79,
11,
4,
43,
44,
37,
34,
59,
40,
8,
24,
18,
20,
70,
68,
17,
17,
21,
38,
59,
79,
50,
72,
54,
44,
48,
29,
44,
61,
58,
81,
29,
5,
35,
40,
37,
33,
35,
38,
26,
10,
72,
21,
20,
1.24,
26,
79,
78,
56,
46,
55,
30,
53,
32,
38,
33,
44,
44,
51,
41,
5,
42,
6,
46,
47,
25,
40,
49,
21,
61,
78,
41,
82,
39,
70,
31,
37,
21,
80,
21,
5,
81,
35,
24,
52,
70,
34,
17,
8,
1.72,
29,
55,
44,
19,
67,
72,
51,
63,
64,
0.72,
26,
59,
76,
13,
1.08,
57,
68,
9,
82,
45,
57,
18,
13,
81,
35,
51,
44
],
"xaxis": "x",
"yaxis": "y"
},
{
"marker": {
"color": "#D4A27F",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "avg_glucose_level",
"nbinsx": 30,
"type": "histogram",
"x": [
228.69,
105.92,
171.23,
174.12,
186.21,
70.09,
94.39,
58.57,
80.43,
120.46,
104.51,
214.09,
167.41,
191.61,
221.29,
89.22,
193.94,
233.29,
228.7,
208.3,
102.87,
104.12,
100.98,
195.23,
212.08,
83.41,
196.92,
252.72,
84.2,
84.03,
219.72,
74.63,
92.62,
60.91,
78.03,
71.22,
144.9,
213.03,
243.58,
107.26,
99.33,
58.09,
127.29,
124.13,
196.71,
59.32,
194.99,
180.93,
185.17,
74.9,
61.94,
93.72,
113.01,
221.58,
104.47,
86.23,
72.67,
179.12,
116.55,
228.56,
96.59,
66.72,
240.09,
110.85,
143.43,
96.16,
88.92,
79.79,
96.97,
111.81,
59.35,
86.94,
98.55,
226.98,
72.81,
68.02,
68.56,
64.14,
235.63,
76.34,
240.59,
78.92,
82.81,
74.1,
190.32,
231.61,
78.7,
110.52,
73.18,
191.82,
93.05,
64.17,
129.98,
68.53,
224.1,
82.1,
216.94,
76.11,
72.96,
82.28,
105.22,
59.86,
62.55,
259.63,
249.31,
131.41,
73.54,
200.59,
190.14,
130.54,
182.99,
206.09,
263.32,
140.1,
207.28,
194.37,
199.2,
103.68,
116.44,
70.28,
72.17,
221.79,
151.16,
67.29,
67.41,
239.07,
223.83,
76.57,
77.82,
92.98,
231.56,
102.16,
221.89,
195.71,
74.02,
203.87,
89.13,
133.19,
162.23,
98.02,
91.54,
97.43,
91.02,
185.49,
64.44,
56.11,
213.22,
78.18,
111.04,
76.13,
112.24,
137.3,
116.69,
215.94,
86.62,
205.77,
112.16,
118.03,
271.74,
97.92,
200.62,
242.52,
93.81,
74.96,
175.29,
90,
109.47,
87.85,
106.74,
80.13,
90.19,
134.8,
106.58,
83.24,
103.43,
208.65,
74.11,
95.84,
205.33,
116.1,
210.4,
219.73,
250.89,
72.73,
205.35,
70.3,
104.86,
216.58,
184.4,
97.76,
66.03,
81.95,
97.73,
70.94,
199.84,
218.46,
211.06,
86.3,
162.14,
88.2,
91.92,
76.46,
197.28,
233.94,
247.51,
84.96,
57.93,
78.8,
78.81,
95.12,
87.96,
110.89,
69.04,
161.28,
210.95,
77.59,
243.53,
77.67,
205.84,
77.08,
57.08,
162.96,
73.5,
95.04,
85.37,
84.62,
82.67,
57.33,
67.84,
75.7,
129.54,
60.22,
198.21,
109.82,
60.84,
94.61,
97.49,
206.72,
214.45,
82.9,
103.26,
55.78,
73.74,
149.75,
82.34,
62.6,
94.09,
55.42,
82.18,
117.92,
114.84,
79.17,
110.63,
190.7,
79.81,
113.63,
141.24,
56.96,
203.04,
94.3,
81.51,
137.74,
81.84,
242.3,
102.64,
146.01,
75.42,
220.49,
61.45,
88.19,
115.22,
97.93,
85.6,
79.83,
218.46,
62.2,
69.15,
127.71,
216.7,
71.89,
103.56,
79.03,
69.94,
90.12,
95.1,
234.58,
235.85,
78.05,
89.44,
111.38,
243.5,
182.2,
229.92,
82.24,
89.84,
90.69,
84.38,
215.6,
91.61,
138.16,
75.23,
75.18,
239.64,
58.95,
99.92,
116.23,
118.82,
66.3,
101.41,
200.28,
136.8,
96.57,
66.42,
98.09,
205.23,
86.78,
126.82,
78.4,
63.69,
89.86,
110.99,
83.82,
137.96,
210.78,
118.89,
72.55,
107.41,
93.25,
74.91,
80.05,
99.8,
91.97,
64.4,
73.02,
88.11,
138.29,
72.36,
68.76,
106.22,
101.52,
109.51,
85.84,
110.15,
91.72,
251.6,
123.1,
97.4,
68.34,
80.81,
120.31,
83.51,
74.09,
91.6,
87.52,
213.37,
75.39,
122.41,
223.36,
84.59,
70.16,
107.52,
80.88,
93.48,
70.21,
86.67,
101.37,
105.22,
70.31,
64.06,
178.29,
81.6,
147.48,
86.86,
71.16,
73.2,
203.81,
82,
97.55,
94.29,
75.22,
90.07,
73.73,
79.58,
77.88,
205.26,
58.63,
139.9,
211.03,
72.79,
91.65,
225.47,
94.48,
82.34,
180.63,
92.48,
116.66,
112.09,
130.34,
82.59,
123.87,
98.7,
227.1,
55.39,
66.07,
104.21,
145.15,
68.19,
97.53,
122.31,
83.51,
86.46,
112.06,
92.14,
201.76,
74.44,
95.93,
87.51,
72.35,
113.47,
103.78,
115.43,
95.59,
101.52,
170.05,
217.3,
196.01,
92.21,
100.09,
93.88,
75.05,
70.08,
74.2,
69.01,
96.17,
87.24,
82.58,
96.88,
73.32,
94.59,
132.08,
98.99,
104.48,
98.01,
79.14,
89.32,
65.24,
80.98,
186.17,
69.24,
111.19,
93.28,
122.19,
129.53,
114.53,
93.73,
77.49,
65.38,
94.18,
118.7,
96.29,
74.64,
96.99,
100.61,
97.08,
84.79,
78.75,
82.93,
88.78,
122.1,
74.81,
183.45,
114.94,
104.38,
102.91,
108.79,
73.31,
68.52,
102.64,
70.92,
86.11,
129.16,
113.85,
79.35,
80.79,
114.01,
93.74,
90.65,
98.41,
127.18,
138.06,
89.44,
105.61,
155.43,
80.83,
80.19,
60.34,
79.3,
98.44,
210.48,
70.34,
122.22,
75.29,
100.35,
193.83,
126.96,
183.34,
247.69,
76.2,
71.43,
93.36,
61.29,
103.62,
100.52,
191.47,
67.06,
144.1,
58.26,
239.82,
73.71,
88.53,
114.79,
113.8,
71.91,
65.3,
189.57,
114.25,
81.26,
87.88,
112.17,
99.97,
207.58,
109.16,
74.46,
118.51,
107.47,
182.86,
59.17,
215.64,
163.7,
196.36,
139.77,
141.23,
89.99,
108.53,
63.61,
85.54,
100.83,
59.82,
105.95,
188.11,
71.15,
60.53,
205.5,
204.86,
115.03,
79.87,
97.89,
125.26,
114.32,
92.23,
83.51,
102.34,
144.48,
88.39,
228.08,
93.79,
147.14,
106.43,
88.04,
92.2,
219.53,
75.56,
158.31,
111.43,
219.97,
148.91,
69.4,
68.34,
58.37,
71.2,
101.5,
92.04,
73,
111.15,
58.65,
100.54,
111.73,
97.78,
85.79,
98.54,
94.62,
112.43,
99.67,
67.39,
95.12,
85.06,
214.05,
79.09,
98.09,
76.36,
98.42,
82.01,
56.11,
111.85,
92.39,
112.54,
63.71,
70.35,
94.65,
64.64,
57.27,
107.25,
77.12,
55.32,
69.37,
57.57,
87.43,
98.73,
81.18,
56.23,
69.61,
59.49,
100.26,
56.75,
67.81,
77.46,
76.26,
86.39,
116.38,
75.74,
92.14,
149.95,
74.98,
55.25,
79.89,
130.61,
94.26,
94.34,
109.03,
81.03,
99.82,
140.08,
59.54,
80.33,
131.51,
89.21,
91.45,
90.58,
91.45,
89.63,
124.06,
99.47,
88.97,
80.93,
102.07,
91.69,
63.71,
200.49,
75.3,
240.71,
60.5,
131.85,
134.39,
56.18,
113.64,
156.57,
80.4,
59.26,
63.26,
86.24,
197.1,
90.9,
84.59,
121.8,
77.65,
64.41,
75.19,
67.97,
119.34,
94.92,
85.02,
103.51,
114.5,
70.78,
95.01,
84.11,
74.02,
78.49,
74.22,
55.34,
75.04,
109.73,
96.78,
194.62,
70.02,
222.21,
62.41,
55.83,
65.12,
73.62,
85.83,
103.86,
60.98,
82.69,
250.2,
128.97,
84.31,
74.8,
97.06,
77.54,
92.78,
173.43,
79.08,
106.18,
114.21,
91.08,
105.77,
74.88,
123.15,
184.25,
117.69,
55.26,
106.51,
55.27,
98.42,
99.2,
98.92,
92.7,
106.7,
152.81,
82.59,
91,
93.88,
76.99,
254.6,
93.99,
109.68,
92.04,
111.41,
97.92,
65.61,
62.37,
101.28,
88.31,
103.55,
60.26,
79.39,
122.01,
78.09,
89.7,
95.1,
112.64,
79.8,
82.2,
108.89,
114.32,
116.14,
122.74,
94.04,
86.6,
82.32,
83.83,
106.98,
93.88,
70.53,
212.01,
65.88,
106.97,
126.35,
129.16,
88.66,
112.95,
81.59,
76.82,
73.75,
91.56,
104.55,
84.84,
86.06,
109.1,
68.35,
121.04,
160.83,
85.62,
74.23,
110.76,
79.44,
119.52,
90.42,
109.32,
78.16,
79.13,
84.06,
89.3,
69.21,
186.45,
58.47,
189.49,
99.6,
126.18,
186.32,
62.49,
74.51,
94.63,
91.98,
56.77,
75.87,
77.19,
135.75,
100.15,
95.08,
125.38,
111.71,
94.64,
112.96,
105.36,
84.13,
102.47,
77.04,
87.01,
56.99,
73.01,
106.35,
80.63,
103.61,
194.04,
111.99,
57.51,
68.49,
60.2,
160,
86.96,
237.15,
99.06,
56.31,
84.31,
90.92,
104.33,
62.61,
231.19,
94.3,
104.48,
72.62,
81.64,
90.78,
70.71,
72.36,
59.76,
63.64,
76.26,
82.06,
110.91,
77.16,
69.58,
96.26,
87.27,
65.36,
109.51,
89.03,
91.34,
57.82,
81.15,
115.29,
111.02,
111.61,
109.02,
106.51,
207.32,
87.11,
61.68,
119.04,
86.05,
70.03,
207.64,
236.84,
78.11,
100.97,
99.21,
113.21,
92.95,
142.57,
92.87,
111.48,
123,
100.54,
106.68,
86.4,
61.81,
128.28,
204.63,
89.98,
232.89,
98.58,
57.94,
123.21,
76.34,
92.98,
92.81,
85.16,
90.1,
65.15,
104.62,
113.25,
59.28,
93.07,
77.42,
73.69,
74.9,
58.39,
102.05,
112.47,
72.84,
195.03,
170.95,
87.98,
65.81,
124.6,
83.65,
88.81,
86.75,
123.36,
89.68,
69.87,
109.22,
157.57,
110.73,
99.14,
59.15,
165.11,
77.44,
100.33,
57.93,
148.24,
91.96,
72.93,
84.27,
124.45,
90.51,
118.87,
56.42,
73.67,
89.11,
74.04,
145.23,
100.8,
85.22,
72.49,
82.61,
72.07,
79.85,
84.86,
131.89,
89.17,
227.91,
131.77,
102.01,
128.63,
144.1,
143.15,
116.67,
85.6,
142.12,
100.81,
69.17,
58.38,
86.34,
95.44,
94.78,
109.59,
79.26,
78.46,
85.29,
156.18,
69.52,
65.32,
108.47,
105.63,
77.93,
95.2,
78.52,
114.89,
95.43,
84.08,
110.55,
82.21,
82.38,
93.89,
75.08,
76.72,
84.93,
76.5,
65.47,
99.16,
63.53,
78.65,
63.78,
77.52,
111.64,
97.84,
113.01,
56.48,
204.5,
96.02,
206.25,
115.52,
60.69,
93.67,
67.79,
56.07,
254.63,
96.63,
84.3,
246.34,
195.16,
93.34,
162.3,
106.4,
110.25,
69.7,
87.03,
112.98,
82.47,
75.88,
84.63,
109.56,
89.45,
91.65,
121.14,
87.77,
106.56,
93.88,
153.76,
88.65,
111.22,
223.68,
55.86,
63.73,
103.94,
92.49,
229.2,
75.06,
85.57,
68.61,
56.3,
65.98,
204.57,
64.99,
73.4,
82.83,
93.55,
90.4,
98.1,
142.02,
251.46,
89.96,
82.44,
80.85,
220.52,
86.73,
76.93,
113.1,
96.84,
100.91,
116.5,
57.26,
129.31,
56.47,
86.06,
86.97,
78.78,
195.04,
103.25,
134.59,
93.74,
90.66,
91.68,
75.79,
91.68,
106.68,
82.31,
90.06,
65.47,
58.03,
83.09,
88.6,
63.18,
78.42,
65.08,
79.47,
95.52,
73,
138.07,
86.99,
112.07,
105.76,
55.67,
90.55,
218.65,
111.21,
83.56,
116.76,
83.5,
69.3,
67.55,
80.28,
211.49,
67.96,
99.29,
140.52,
75.62,
120.06,
117.45,
224.71,
122.75,
82.39,
57.02,
115.98,
226.11,
97.59,
82.57,
79.34,
125.68,
65.59,
120.85,
101.65,
210.94,
77.92,
230.68,
62.68,
55.22,
78.94,
109.69,
79.33,
79.91,
84.46,
68.4,
78.43,
198.02,
85.35,
204.17,
82.71,
121.19,
106.02,
66.36,
267.76,
107.18,
74.58,
80.92,
90.77,
90.84,
59.2,
133.76,
61.36,
118.62,
77.91,
68.98,
112.08,
96.2,
93.11,
115.54,
88.57,
180.76,
83.12,
94.89,
239.52,
99.15,
124.16,
86.38,
93.68,
229.86,
78.73,
93.71,
76.66,
112.38,
89.3,
103.44,
61.61,
94.12,
62.4,
210.96,
76.25,
103.48,
96.42,
78.59,
195.25,
103.76,
68.52,
92.17,
81.02,
94.33,
78.43,
217.39,
82.77,
88.13,
102.77,
101.75,
113.34,
63.33,
69.34,
118.81,
66.13,
67.53,
96.58,
57.89,
78.38,
94.15,
89.53,
127.75,
76.79,
141.15,
88.63,
130.15,
108.03,
104.07,
88.83,
197.79,
150.45,
100.39,
79.92,
96.79,
86.84,
61.27,
72.61,
82.41,
74.72,
57.37,
88.34,
101.34,
119.01,
71.98,
84.91,
91.63,
145.71,
167.31,
63.65,
79.63,
87.49,
92.82,
72.2,
181.23,
63.37,
74.82,
97.6,
93.04,
97.12,
82.81,
134.45,
98.67,
85.82,
189.45,
144.16,
82.56,
206.4,
67.97,
71.7,
72.02,
61.81,
103.09,
73.75,
156.7,
70.87,
138.47,
64.66,
91.88,
74.79,
78.18,
77.82,
112.23,
178.76,
121.43,
94.22,
197.58,
86.37,
112.09,
199.96,
85.33,
86.33,
80.47,
62.46,
78.85,
103.81,
78.04,
94.67,
62.25,
155.17,
83.28,
132.41,
62.78,
89.96,
103.25,
126.99,
102.27,
113.63,
86.09,
108.08,
108.34,
56.07,
82.91,
94.75,
101.79,
88.41,
101.05,
85.07,
108.1,
93.17,
158.89,
205.77,
112.34,
119.03,
237.21,
89.68,
246.53,
89.05,
81.74,
98.12,
105.59,
86,
65.51,
61.78,
109.56,
65.82,
90.16,
64.45,
91.85,
97.89,
206.33,
74.29,
107.4,
87.72,
85.99,
100.84,
124.61,
86.55,
90.42,
100.85,
87.74,
80.15,
107.29,
206.98,
227.28,
80.43,
228.7,
120.07,
93.47,
80.77,
150.27,
81.94,
70.75,
83.83,
73.66,
102.37,
77.83,
70.01,
119.88,
169.97,
123.79,
80.98,
84.21,
101.31,
86.26,
92.86,
140.28,
244.28,
124.16,
93.55,
99.34,
59.87,
72.76,
68.34,
61.1,
120.77,
60.39,
126.34,
93.24,
154.75,
62.81,
65.96,
79.53,
96.85,
251.99,
115.13,
102.3,
78.99,
68.8,
83.84,
94.66,
107.98,
61.01,
135.64,
92.65,
121.11,
107.69,
70.15,
83.02,
222.29,
139.43,
213.11,
77.23,
58.48,
87.33,
102.34,
151.3,
144.23,
110.6,
80.28,
111.02,
114.88,
227.51,
111.65,
201.01,
129.01,
124.08,
107.91,
95.4,
65.9,
78.98,
111.33,
104.36,
123.87,
210,
86.86,
113.96,
81.28,
102.5,
78.05,
86.07,
59.05,
95.88,
70.18,
111.13,
79.3,
65.84,
123.49,
207.45,
143.45,
89.11,
58.3,
78.68,
98.91,
67.92,
73,
97.5,
108.43,
90.43,
76.3,
121.27,
119.67,
226.93,
76.11,
253.16,
101.02,
238.53,
79.18,
207.79,
196.2,
231.76,
93.3,
110.47,
77.52,
216.92,
157.01,
93.51,
102.54,
87.78,
126.67,
194.98,
99.96,
58.87,
77.67,
83.14,
69.18,
218.54,
71.22,
82.41,
82.07,
73,
127.25,
72.63,
108.2,
123.95,
58.71,
98.46,
68.78,
133.63,
101.43,
65.3,
72.49,
65.49,
63.4,
105.29,
64.08,
78.34,
66.32,
168.15,
93.48,
79.2,
107.45,
83.86,
119.96,
70.58,
87.7,
65.29,
97.14,
110.68,
91.93,
88.54,
183,
115.93,
99.13,
69.24,
102.28,
71.31,
89.75,
87.66,
81.11,
178.33,
81.38,
109.56,
70.45,
73.44,
146.44,
65.04,
151.25,
106.41,
197.09,
93.58,
122.04,
80.06,
84.93,
68.4,
62.99,
139.67,
113.4,
101.92,
65.98,
84.3,
61.42,
85.52,
83.79,
73.83,
69.38,
66.11,
149.62,
242.84,
202.66,
216.9,
103.78,
114.88,
75.94,
55.51,
93.74,
71.44,
58.29,
115.83,
102.71,
99.69,
102.89,
56.63,
87.34,
109.51,
78.59,
78.26,
105.47,
68.19,
114.47,
66.85,
94.71,
208.05,
222.6,
80.81,
64.51,
151.56,
199.14,
73.41,
109.65,
69.11,
114.41,
103.46,
95.36,
57.33,
191.48,
71.63,
84.75,
89.24,
70.13,
82.26,
200.16,
80.73,
75.09,
80.57,
77.04,
145.26,
82.08,
103.35,
97.46,
123.65,
87.96,
85.92,
80.8,
99.3,
62.56,
144.2,
71.38,
95.5,
67.06,
70.16,
104.24,
122.48,
215.9,
108.56,
69.88,
91.54,
65.85,
60.74,
233.52,
110.7,
78.93,
124.5,
57.95,
92.82,
58.25,
213.54,
74.86,
78.35,
101.26,
86.87,
124.01,
83.41,
70.65,
92.4,
73.36,
219.5,
105.73,
83.79,
58.02,
61.94,
99.71,
63.19,
56.84,
217.66,
77.19,
76.22,
155.32,
70.67,
227.16,
58.69,
88.85,
137.94,
60.6,
93.67,
79.56,
122.46,
92.32,
80.63,
92.32,
59.89,
80.57,
88.52,
88.82,
102.08,
84.9,
82.21,
74.05,
90.46,
176.48,
94.07,
85.12,
71.81,
111.93,
94.4,
70,
95.57,
91.98,
66.29,
211.88,
76.55,
56.67,
100.52,
58.96,
81.66,
96.77,
91.63,
97.51,
225.6,
210.23,
92.82,
234.82,
65.67,
74.51,
81.99,
87.69,
125.29,
111.64,
70.93,
89.93,
137.45,
72.06,
97.9,
84.85,
230.59,
92.81,
168.15,
224.63,
108.12,
85.17,
84.14,
85.57,
82.81,
110.53,
61.88,
85.04,
140.4,
62.27,
99.44,
88.9,
64.1,
97.58,
58.66,
88.24,
61.32,
62.99,
86.04,
83.3,
208.17,
133.2,
81.54,
146.61,
79.33,
84.56,
103.44,
185.31,
102.53,
65.78,
62.6,
81.18,
76.68,
92.86,
90.95,
85.66,
68.37,
77.32,
187.87,
213.87,
119.62,
222.85,
100.82,
136.1,
149.42,
79.99,
118.21,
56.3,
84.49,
89.61,
100.09,
198.36,
93.61,
75.43,
100.31,
105.63,
82.84,
58.88,
92.27,
115.23,
196.25,
85.97,
114.33,
72.52,
63.47,
90.58,
92.26,
113.74,
72.79,
94.71,
194.53,
72.65,
86.68,
140.39,
77.87,
104.7,
95.82,
204.05,
130.56,
67.78,
105.29,
135.19,
116.12,
59.63,
56.43,
199.18,
209.26,
91.21,
95.16,
217.11,
222.46,
82.44,
74.11,
131.23,
187.52,
83.26,
99.07,
76.63,
65.09,
142.31,
80.99,
237.74,
154.08,
78.29,
164.67,
59.61,
67.75,
90.35,
77.86,
71.88,
118.82,
67.33,
121.66,
111.92,
92.82,
95.46,
59.99,
68.96,
90.26,
91.9,
91.08,
118.55,
102.04,
65.52,
82.09,
149.9,
100.33,
91.25,
89.81,
97.32,
75.53,
121.44,
83.52,
57.92,
75.27,
80.24,
88.69,
69.09,
62.67,
98.74,
56.51,
65.95,
129.07,
62.47,
69.82,
99.68,
223.35,
74.36,
102.97,
153.38,
87.1,
113.4,
94.88,
82.73,
67.8,
84.85,
135.89,
106.13,
74.33,
109.4,
75.4,
87.69,
95.85,
90.36,
63.45,
201.07,
122.91,
55.41,
60.99,
111.77,
77.63,
70.61,
124.34,
103.44,
124.5,
75.5,
98.85,
96.06,
93.74,
65.6,
113.24,
94.77,
145.22,
112.92,
84.79,
103.28,
96.18,
186.95,
68.66,
128.72,
85.04,
146.97,
65.69,
97.52,
90.43,
81.71,
198.24,
90.51,
97.78,
80.74,
57.02,
93,
95.37,
96.25,
86.74,
63.08,
129.16,
99.79,
83.34,
95.01,
229.21,
80.08,
62,
118.69,
77.57,
108.75,
108.23,
151.33,
82.43,
107.17,
91.46,
81.05,
114.37,
100.29,
68.37,
87.95,
73.27,
131.43,
107.29,
68.24,
74.01,
95.4,
95.75,
104.51,
108.87,
209.06,
86.57,
120.25,
85.51,
84.18,
82.37,
96.15,
74.55,
114.45,
113.68,
107.91,
83.85,
101.3,
72.13,
94.98,
228.42,
93.9,
93.13,
121.6,
84.4,
82.13,
73.33,
75.88,
212.97,
100.41,
202.05,
71.42,
107.5,
55.57,
58.14,
78.53,
78.79,
96.62,
65.28,
92.67,
206.25,
114.18,
231.69,
101.89,
107.22,
101.99,
72.56,
94.47,
98.76,
116.93,
219.96,
75.77,
124.35,
88.97,
72.49,
107.27,
97.96,
197.69,
112.3,
75.77,
76.51,
93.14,
80.84,
199.88,
65.34,
98.71,
170.22,
73.5,
102.96,
80.72,
74.19,
77.12,
81.2,
114.76,
109.97,
92.87,
113.05,
68.24,
74.35,
91.08,
88.83,
134.33,
208.78,
66.17,
222.29,
114.16,
61.67,
220.36,
76.03,
73.06,
187.88,
79.89,
79.02,
191.66,
76.74,
72.29,
92.59,
80.42,
62.47,
63.94,
77.73,
115.79,
96.3,
107.46,
108.33,
89.37,
93.64,
217.75,
83.44,
93.88,
55.34,
102.61,
62.08,
81.76,
59.2,
55.61,
226.88,
65.66,
58.7,
186.4,
122.32,
59.05,
149.13,
82.39,
169.49,
80.25,
203.81,
170.76,
80.85,
120.58,
86.57,
90.01,
68.84,
155.14,
158.48,
92.56,
90.15,
80.09,
189.44,
70.03,
249.29,
72.52,
96.93,
211.35,
85.87,
60.96,
120.09,
92.13,
83.07,
77.67,
97.24,
127.29,
134.8,
74.65,
85.84,
90.43,
74.35,
206.59,
196.33,
70.23,
242.94,
103.06,
80.86,
114.32,
83.91,
152.02,
75.19,
60.61,
89.14,
97.23,
135.19,
78.48,
75.92,
93.3,
97.39,
134.61,
104.02,
56.9,
112.66,
226.75,
76.28,
90.01,
107.84,
71.06,
185,
104.42,
89.57,
136.2,
128.23,
104.23,
133.82,
80.82,
102.35,
88.48,
88.81,
75.09,
199.83,
73.69,
111.27,
68.44,
65.36,
94.12,
96.24,
91.25,
77.29,
125.09,
107.18,
130.07,
57.3,
101.35,
94.03,
227.81,
66.67,
240.81,
88.02,
152.87,
96.04,
60.35,
118.66,
58.51,
96.04,
110.92,
92.72,
108.23,
56.34,
61.83,
131.99,
136.81,
239.28,
131.3,
117.75,
78.46,
83.53,
145.5,
88.33,
101.25,
131.43,
231.5,
82.89,
192.37,
82.88,
99.4,
77.75,
75.03,
220.47,
196.91,
180.8,
247.48,
216,
75.64,
95.07,
129.97,
124.26,
87.92,
85.15,
95.36,
87.16,
219.39,
71.92,
167.66,
87.21,
220.47,
80.47,
89.95,
111.32,
65.01,
110.07,
60.56,
89.83,
90.35,
71.5,
71.71,
173.96,
96.06,
113.2,
73.29,
65.71,
60.41,
89.11,
63.32,
85.68,
162.93,
89.22,
91.68,
198.33,
85.81,
79.59,
106.74,
191.33,
59.52,
206.52,
64.92,
80,
75.06,
91.18,
85.81,
148.37,
64.55,
97.31,
111.15,
80.97,
89.88,
62.66,
122.25,
87.08,
85,
100.49,
145.37,
112.19,
79.73,
101.13,
101.98,
98.39,
128.04,
98.12,
216.96,
94.92,
83.27,
96.86,
99.73,
91.71,
65.77,
161.57,
74.32,
170.93,
74.43,
86.36,
97.06,
110.38,
79.03,
62.48,
55.62,
84.9,
232.81,
86.55,
118.61,
80.21,
65.96,
61.34,
116.6,
85.13,
86.32,
69.22,
140.93,
83.43,
207.95,
134.29,
115.12,
86.25,
112.94,
73.54,
80.86,
83.91,
69.91,
95.62,
63.95,
122.5,
86,
120.27,
56.71,
131.63,
79.36,
120.77,
100.42,
155.86,
66.01,
86.99,
90.55,
229.58,
60.06,
65.66,
104.7,
113.86,
99.49,
165.99,
114.99,
79.58,
77.37,
93.55,
187.22,
167.59,
98.03,
88.79,
92.9,
117.34,
80.75,
77.51,
105.63,
114.34,
227.04,
87.56,
84.47,
96.26,
116.64,
79.57,
78.44,
64.15,
74.5,
70.32,
84.48,
144.08,
78.14,
133.58,
214.42,
80.63,
233.71,
96,
74.52,
131.4,
216.4,
108.8,
97.06,
266.59,
55.58,
65.87,
89.28,
56.12,
74.7,
83.06,
227.94,
84.88,
64.2,
137.22,
81.44,
90.26,
68.48,
100.02,
156.82,
81.66,
112.22,
90.62,
72.49,
107.47,
92.11,
70.54,
205,
126.39,
89.72,
92.08,
85.38,
95.25,
75.91,
203.44,
88.23,
97.47,
59.28,
101.15,
68.38,
55.46,
113.87,
96.35,
127.21,
78.9,
59.91,
160.94,
59.61,
87.94,
97.55,
116.02,
127.32,
75.5,
79.35,
108.56,
168.06,
75.52,
82.15,
200.28,
62.44,
84.31,
221.43,
165.47,
96.69,
63.98,
145.03,
98.53,
134.23,
62.48,
70.52,
93.21,
82.39,
88.29,
80.18,
84.02,
69.72,
192.16,
101.87,
77.55,
215.72,
87.84,
91.93,
58.63,
173.14,
106.83,
84.44,
92.16,
85.96,
202.57,
65.41,
102.51,
62.6,
150.1,
94.15,
65.5,
81.31,
78.96,
123.47,
90.4,
71.59,
209.5,
86.6,
57.43,
87.93,
203.16,
112.12,
92.02,
60.98,
86.35,
71.26,
72.61,
110.78,
99.78,
201.45,
127.78,
121.32,
83.89,
206.15,
102.9,
65.48,
123.23,
71.3,
79.05,
102.21,
105.22,
84.58,
77.3,
74.79,
88.75,
97.97,
79.18,
81.33,
126.68,
196.61,
219.92,
79.16,
99.68,
79.55,
87.86,
76.92,
77.24,
120.94,
77.66,
231.95,
111.48,
84.25,
216.38,
95.01,
105.74,
58.41,
56.89,
86.25,
86.49,
115.47,
92.22,
83.16,
110.68,
70.06,
213.33,
93.52,
87.62,
77.67,
95.66,
172.33,
106.59,
87.06,
93.02,
243.59,
86.7,
97.81,
116.04,
169.43,
68.41,
94.44,
73.48,
183.87,
66.08,
72.19,
101.09,
88.83,
69.99,
102.4,
87.26,
146.59,
59,
76.83,
101.83,
90.92,
67.5,
97.16,
85.62,
129.43,
106.01,
114.82,
121.39,
227.98,
78.97,
84.12,
95.04,
103.15,
82.91,
70.93,
153.6,
129.43,
81,
71.81,
84.46,
101.32,
62.56,
208.2,
199.42,
91.28,
77.99,
190.13,
149.68,
92.3,
235.54,
84.46,
68.7,
178.89,
227.74,
100.66,
73.62,
100.05,
213.8,
103.94,
84.2,
250.8,
112.29,
65.98,
99.1,
104.34,
217,
57.47,
78.24,
94.38,
100.31,
74.29,
103.43,
99.83,
217.4,
71.81,
79.79,
190.92,
94.76,
101.43,
115.86,
95.27,
89.02,
77.55,
75.86,
93.62,
72.71,
94.45,
88.57,
84.12,
89.11,
99.83,
150.03,
69.67,
77.46,
100.54,
142.38,
110.87,
73.6,
59.52,
142.68,
182.9,
75.28,
100.75,
163.82,
110.42,
79.59,
107.99,
90.22,
143.45,
78.57,
79.82,
95.88,
123.04,
92.34,
116.84,
90.04,
255.17,
123.66,
112.16,
88.43,
86.03,
227.96,
85.6,
111.68,
73.49,
87.82,
99.29,
63.73,
85.55,
79.2,
105.93,
94.19,
69.28,
71.37,
110.18,
59.68,
94.24,
82.1,
62.62,
107.83,
107.59,
116.98,
84.1,
160.64,
84.4,
81.13,
88.19,
127.13,
119.61,
143.47,
84.6,
158.93,
68.94,
69.38,
72,
57.76,
108.65,
65.42,
78.93,
98.34,
82.57,
124.37,
100.96,
75.22,
59.85,
104.75,
70.98,
92.77,
82.12,
106.35,
77.33,
96.01,
107.49,
88.47,
87.92,
91.13,
80.72,
59.83,
60.22,
82.41,
75.78,
102.42,
98.45,
93.51,
113.68,
97.41,
58.65,
81.68,
148.52,
142.12,
125.3,
118.81,
84.16,
78.74,
57.51,
61.11,
78.3,
76.19,
115.99,
100.8,
98.27,
93.85,
94.69,
136.23,
56.12,
93.03,
196.81,
87.81,
222.66,
223.58,
85.77,
88.23,
73.65,
141.8,
92.65,
61.04,
102.88,
123.39,
67.07,
69.88,
57.02,
123.81,
73.36,
90.54,
69.24,
192.39,
74.35,
71.94,
82.35,
73.28,
97.57,
83.3,
85.07,
60.4,
79.96,
70.96,
82.86,
201.38,
91.82,
91.85,
99.23,
68.13,
93.76,
103.89,
87.39,
89.53,
66.61,
236.14,
99.76,
87.09,
193.81,
97.27,
137.77,
105.19,
58.35,
68.79,
94.19,
121.17,
106.41,
104.79,
82.18,
96.81,
107.42,
69.2,
92.73,
86.11,
111.78,
122.26,
78.16,
105.77,
77.53,
77.35,
82.36,
239.95,
61.96,
72.1,
107.97,
60.77,
71.06,
60.36,
83.33,
65.33,
113.28,
170.88,
103.34,
106.08,
156.45,
60.05,
114.05,
202.21,
57.28,
99.49,
63.6,
80.01,
58.42,
67.38,
73.58,
103.92,
80.22,
103.76,
77.06,
90.22,
181.3,
85.29,
107.59,
87.72,
70.73,
72.75,
95.47,
70.54,
100.03,
59.78,
198.79,
90.39,
147.04,
64.92,
154.6,
95.98,
102.51,
96.52,
232.12,
78.12,
70.04,
97.61,
113.11,
71.06,
104.95,
81.94,
96.37,
114.16,
93.58,
73.89,
107.83,
107.21,
99.35,
203.57,
81.51,
69.26,
111.47,
86.09,
72.94,
76.77,
60.32,
133.24,
65.01,
74.63,
230.78,
72.5,
68.48,
85.03,
80.76,
95.36,
110.33,
89.18,
91.53,
65.16,
74.53,
93.29,
227.89,
121.46,
216.71,
74.96,
75.25,
91.82,
56.37,
149.15,
202.67,
65.38,
90.31,
221.8,
73.39,
147.12,
109.85,
103.21,
81.25,
84.69,
87.1,
81.92,
90.91,
64.4,
67.29,
124.48,
62.89,
87.51,
93.15,
64.87,
90.68,
104.64,
92.44,
120.46,
89.87,
105.28,
131.8,
66.33,
101.07,
83.37,
79.8,
103.17,
62.52,
110.36,
140.07,
84.86,
106.85,
102.07,
202.38,
67.07,
73.7,
77.59,
69.16,
215.81,
65.4,
139.72,
72.81,
149.17,
125.74,
90.21,
139.48,
90.77,
220.24,
81.42,
102.36,
82.85,
102.11,
80.92,
71.8,
88.47,
67.68,
98.65,
83.58,
104.04,
69.4,
106.22,
112.46,
72.42,
96.73,
107.4,
267.61,
109.46,
120.05,
106.33,
97.28,
83.13,
97.35,
79.54,
76.55,
56.11,
91.36,
79.27,
113.21,
105.99,
59.93,
88.83,
115.42,
71.93,
96.43,
161.95,
104.55,
71.25,
82.02,
78.02,
115.21,
65.12,
143.33,
80.55,
88.2,
147.42,
86.53,
91.95,
63.72,
63.78,
55.64,
88.88,
114.61,
56.08,
59.62,
75.85,
176.71,
80.07,
56.33,
87.16,
102.46,
161,
70.7,
81.26,
157.67,
89.59,
64.02,
110.69,
71.97,
82.07,
65.07,
207.62,
108.64,
201.58,
116.2,
231.43,
112.62,
220.26,
84.37,
82.83,
102.48,
69.12,
211.12,
95.38,
108.72,
88.65,
73.78,
59.48,
75.77,
80.96,
82.4,
78.93,
80.59,
70.59,
98.57,
61.87,
68.88,
66.25,
69.23,
73.2,
103.45,
104.86,
90.3,
90.87,
76.21,
74.14,
87.25,
215.33,
88.38,
104.4,
104.92,
106.54,
62.91,
61.47,
88.5,
107.61,
90.77,
137.91,
228.2,
92.15,
126.32,
94.96,
89.52,
66.59,
80.1,
73.72,
90.49,
92.99,
62.57,
65.51,
95.29,
83.7,
63.16,
87.77,
87.52,
113.65,
65.93,
93.78,
223.9,
77.54,
93.18,
169.74,
101.93,
125.03,
82.25,
85.81,
85.27,
86.06,
104.36,
78.29,
93.55,
207.96,
105.52,
109.27,
91.34,
176.78,
67.73,
73.27,
70.29,
91.47,
205.01,
73.87,
64.44,
111.65,
90.65,
94.2,
56.95,
92.37,
127.23,
191.78,
86.36,
82.72,
113.57,
89.58,
91.85,
88.89,
79.55,
108.62,
78.24,
88.49,
91.85,
72.01,
81.96,
70.56,
71.02,
99.75,
127.2,
69.76,
74.63,
102.27,
73.56,
57.77,
151.25,
78.32,
71.5,
84.7,
127.28,
80.72,
73.92,
93.24,
72.6,
69.77,
84.43,
84.13,
81.54,
57.17,
86.15,
163.02,
110.41,
102.58,
90.97,
140.96,
70.33,
64.18,
127.57,
109.23,
124.49,
142.82,
75.69,
74.08,
75.98,
57.8,
76.44,
214.43,
79.03,
66.46,
56.42,
112.72,
114.02,
72.33,
55.59,
73.48,
105.05,
84.78,
164.77,
220.64,
159.39,
69.53,
81.42,
83.93,
98.9,
82.64,
112.54,
75.25,
204.77,
63.86,
72.2,
80.08,
101.61,
114.32,
80.82,
90.6,
93.67,
87.15,
80.72,
83.16,
98.14,
83.13,
134.24,
248.37,
61.75,
70.55,
124.38,
92.21,
148.72,
79.25,
82.09,
194.53,
98.84,
135.74,
66.55,
115.69,
67.76,
88.65,
106.11,
95.49,
59.07,
228.92,
62.89,
96.84,
81.25,
92.71,
85.59,
89.06,
87.44,
64.07,
79.39,
67.84,
81.73,
105.52,
77.55,
127.42,
226.73,
89.74,
219.17,
74.85,
55.96,
215.92,
104.05,
62.54,
87.54,
76.81,
198.12,
78,
79.44,
75.82,
240.86,
89.28,
263.56,
81.13,
118.44,
97.37,
80.51,
78.88,
83.61,
73.19,
68.91,
104.3,
87.87,
96.7,
91.05,
200.14,
73.04,
85.81,
65.41,
108.68,
88.79,
107.82,
97.39,
141.09,
122.38,
77.91,
103.6,
159.7,
118.22,
84.5,
85.59,
84.42,
100.98,
81.11,
79.2,
207.71,
99.84,
98.05,
105.9,
146.21,
79.94,
72.28,
123.1,
67.06,
82.68,
125.89,
228.05,
98.23,
90.06,
119.9,
94.53,
64.66,
103.79,
88.51,
97.26,
92.59,
102.89,
92.26,
80,
56.75,
104.66,
60.39,
223.14,
105.51,
83.42,
71.06,
101.24,
91.19,
174.43,
214.51,
72.64,
62.69,
141.16,
89.61,
76.42,
124.38,
111.81,
57.57,
77.82,
60.09,
91.02,
65.46,
131.19,
79.89,
74.54,
231.31,
91.57,
238.78,
87.18,
78.94,
69.01,
110.28,
71.66,
62.57,
83.64,
107.18,
233.59,
84.88,
97.95,
58.19,
70.11,
112.77,
75.46,
63.63,
100.22,
81.77,
94.25,
188.13,
164.7,
68.27,
100.12,
151.23,
112.41,
205.97,
112.19,
71.93,
190.89,
138.02,
66.16,
105.73,
78.34,
102.1,
97.37,
193.87,
78.32,
130,
74,
111.04,
214.77,
97.25,
79.95,
162.24,
74.36,
69.68,
98.35,
75.73,
85.86,
80.34,
73.76,
94.96,
108.18,
85.23,
189.88,
197.11,
100.08,
68.62,
84.07,
99.65,
82.33,
94.49,
108.71,
64.94,
98.22,
83.5,
192.47,
125.89,
87.72,
68.94,
66.51,
85.82,
87.47,
87.74,
85.12,
103.37,
81.68,
76.51,
110.17,
65.12,
109.88,
199.38,
65.77,
55.72,
75.67,
88.18,
95.94,
86.93,
85.17,
111.1,
92,
97.47,
79.21,
83.97,
98.55,
202.98,
60.7,
92.22,
79.94,
83.68,
69.92,
136.96,
95.32,
107.78,
198.32,
100.16,
96.02,
112.69,
60.61,
79.77,
87.41,
226.38,
124.64,
69.42,
77.68,
112.79,
236.79,
219.82,
82.05,
104.08,
101.57,
71.32,
239.19,
78.45,
61.11,
87.29,
82.49,
83.15,
83.26,
78.7,
111.94,
104.77,
104.26,
113.84,
114.71,
61.54,
133.62,
111.96,
112.44,
123.61,
84.68,
206.62,
114.54,
216.88,
110.38,
204.92,
56.74,
68.35,
83.52,
59.74,
81.59,
123.83,
226.84,
93.2,
85.84,
74.64,
80.17,
85.03,
234.35,
116.25,
66.55,
88.38,
81.05,
68.4,
96.95,
66.24,
91.81,
84.86,
118.88,
85.91,
115.4,
75.34,
100.19,
200.73,
114.09,
67.3,
96.91,
116.2,
72.54,
96.01,
89.16,
108.64,
83.66,
81.21,
86.96,
92.95,
95.86,
85.52,
159.39,
202.51,
82.24,
109.81,
91.85,
123.49,
105.49,
101.53,
126.04,
95.49,
89.38,
82.3,
218,
79.7,
209.15,
70.22,
83.57,
60.6,
97.24,
162.72,
73.27,
92.62,
90,
78.28,
87.81,
196.5,
142.31,
94.96,
83.78,
86.1,
66.96,
76.7,
103.12,
84.03,
74.15,
73.29,
115.16,
83.55,
61.53,
100.74,
59.31,
63.22,
209.5,
64.51,
90.07,
113.95,
119.32,
99.12,
152.84,
78.23,
76.05,
116.49,
99.91,
76.1,
121.99,
116.21,
55.28,
103.73,
153.08,
98.69,
94.11,
117.03,
91.35,
82.48,
99.76,
111.1,
122.25,
84.19,
84.6,
126.35,
87.5,
77.52,
133.13,
96.1,
108.61,
205.23,
76.74,
138.55,
234.27,
75.16,
77.08,
101.19,
107.74,
62.32,
56.32,
131.28,
67.02,
76.72,
125.98,
72.09,
88.05,
55.47,
145.94,
239.21,
196.08,
93.93,
76.74,
83.74,
119.58,
68.66,
65.79,
176.38,
175.74,
193.45,
85.84,
93.85,
86.21,
75.41,
95.28,
95.87,
180.45,
95.39,
60.77,
102.84,
130.37,
65.44,
79.84,
62.61,
70.07,
108.8,
112.11,
92.35,
76.57,
69.5,
69.48,
89.18,
68.09,
92.71,
56.21,
82.56,
78.24,
84.66,
90.35,
68.12,
59.14,
85.64,
56.08,
99,
93.6,
105.75,
114.09,
57.42,
100.01,
62,
103.61,
118.46,
94.77,
72.12,
155.23,
90.6,
81.92,
67.1,
58.01,
173.9,
106.27,
60.57,
105.34,
96.28,
122.73,
104.12,
69.74,
106.95,
94.63,
86.97,
76.12,
99.94,
91.53,
145.46,
74.86,
74.42,
70.07,
89.85,
104.21,
157.77,
101.81,
103,
101.85,
85.92,
56.85,
89.04,
63.01,
65.48,
68.6,
81.68,
65.21,
72.53,
81.78,
116.85,
80.83,
96.21,
117.04,
78.26,
82.53,
71.25,
129.73,
115.47,
56.79,
100.16,
97.6,
90.54,
105.88,
217.94,
69.89,
93.28,
111.27,
91.04,
87.2,
109.12,
216.64,
74.28,
57.6,
173.97,
65.25,
80.92,
124.37,
102.39,
90.42,
79.17,
71.08,
81.54,
159.67,
93.96,
103.01,
92.49,
208.85,
122.39,
65.91,
70.25,
219.7,
65.7,
67.08,
107.58,
95.87,
68.17,
95.37,
90,
106.1,
56.13,
104.03,
157.01,
84.35,
67.99,
118.93,
185.28,
82.08,
88.32,
85.16,
69.54,
62.12,
198.3,
100.06,
101.13,
91.09,
61.54,
62.02,
103.11,
77.57,
97.34,
67.1,
79.89,
206.66,
95.79,
93.51,
116.78,
117.63,
60.01,
64.6,
71.63,
87.79,
99.64,
86.53,
200.68,
98.37,
117.98,
218.6,
101.3,
79.51,
223.26,
97.22,
172.27,
221.83,
74.61,
60.91,
69.79,
83.6,
83.95,
77.5,
64.29,
61.1,
100.71,
65.21,
119.77,
102.39,
59.67,
83.16,
218.1,
76.45,
200.46,
95.7,
70.23,
75.15,
95.8,
90.67,
84.79,
147.5,
134.12,
82.62,
125.11,
92.14,
217.79,
95.62,
93.8,
90.61,
139.2,
77.01,
105.26,
68.35,
99.07,
147.74,
61.29,
98.92,
90.74,
159.79,
233.47,
85.53,
76.7,
67.87,
77.26,
91.28,
129.19,
97.86,
112.33,
110.41,
91.58,
58.86,
76.88,
109.52,
181.23,
103.58,
81.96,
104.55,
76.12,
64.27,
200.98,
101.96,
84.17,
85.79,
71.46,
95.49,
87.62,
118.55,
82.14,
219.67,
135.84,
74.83,
60.37,
67.28,
77.79,
88.51,
90.38,
156.69,
158.9,
247.97,
114.71,
58.55,
231.15,
68.18,
118.75,
186.54,
96.14,
69.52,
101.93,
71.58,
83.1,
110.96,
122.83,
91.65,
84.1,
118.85,
89.32,
128.72,
98.05,
102.1,
96.19,
98.07,
71.4,
82.27,
158.33,
102.5,
72.18,
95.94,
87.15,
82.59,
89.31,
64.84,
107.52,
92.76,
79.16,
87,
145.25,
125.14,
88.6,
57.59,
59.43,
65.43,
154.03,
139.81,
111.81,
106.84,
221.06,
69.97,
77.07,
59.49,
109.39,
93.97,
163.17,
124.39,
212.62,
128.63,
84.1,
77.6,
70.51,
109.19,
55.23,
120.15,
72.16,
95.89,
64.68,
70.51,
81.77,
66.47,
146.08,
91.68,
108.96,
113.41,
111.08,
217.74,
78.5,
89.06,
74.8,
80.2,
76.52,
208.99,
99.72,
102.97,
197.36,
98.56,
86.06,
123.08,
75,
105.9,
73.87,
78.04,
107.74,
60.64,
82.57,
115.98,
110.38,
92.59,
82.64,
70.28,
106.54,
59.11,
58.89,
129.66,
73.57,
72.04,
125.63,
77.94,
68.99,
111.76,
65.36,
80.27,
81.73,
79.22,
142.63,
97.05,
70.38,
114.46,
64.62,
94.12,
70.19,
55.35,
115.52,
60.7,
69.24,
103.29,
92.74,
232.64,
82.46,
63.57,
73.24,
78.43,
57.46,
104.16,
110.42,
119.4,
119.13,
120.22,
80.8,
57.96,
81.24,
88.79,
70.91,
87.12,
126.09,
76.42,
56.33,
131.81,
95.23,
71.18,
87.71,
115.68,
207.37,
75.1,
69.72,
111.84,
111.08,
201.96,
68.68,
56.9,
91.16,
99.44,
78.85,
123.89,
90.44,
138.44,
75.06,
85.66,
102.76,
71.79,
167.16,
131.05,
86.92,
59.62,
103.72,
77.1,
94.75,
77.72,
76.09,
61.8,
83.73,
79.33,
108.32,
103.65,
86.19,
131.42,
69.46,
76.78,
103.78,
213.43,
93.52,
115.07,
69.45,
81,
83.16,
108.63,
88.06,
80.57,
248.24,
57.79,
87.4,
76.63,
58.01,
77.43,
64.45,
79.42,
82.94,
81.58,
62.21,
229.94,
116.06,
117.77,
66.69,
122.23,
168.68,
119.9,
110.33,
77.95,
80.35,
57.38,
96.47,
70.66,
202.06,
91.3,
67.56,
126.12,
86.4,
103.22,
88.85,
63.9,
81.32,
78.11,
134.65,
77.94,
56.94,
117.03,
207.84,
228.26,
84.43,
57.09,
94.89,
132.46,
75.13,
76.64,
91.32,
85.9,
76.19,
85.77,
63.42,
112.02,
203.76,
98.44,
79.57,
63.98,
101.46,
74.66,
68.66,
205.78,
111.24,
134.76,
110.1,
100.8,
113.08,
56.48,
77.77,
104.02,
83.59,
179.67,
91.89,
78.9,
230.74,
216.19,
83.7,
200.66,
73.99,
228.5,
70.43,
74.39,
102.92,
87.91,
118.55,
232.29,
125.32,
78.98,
100.2,
200.91,
95.24,
56.64,
80.48,
112.96,
236.04,
73,
61.13,
88.56,
66.16,
88.98,
94.39,
87.39,
77.91,
86.85,
94.37,
76.64,
254.95,
107.33,
80.94,
112.02,
196.58,
89.14,
69.94,
55.79,
73.08,
96.02,
189.82,
89.68,
101.06,
62.66,
84.03,
108.38,
78.79,
76.12,
68.01,
90.11,
193.61,
195.74,
94.77,
85,
141.37,
90.71,
106.8,
121.66,
85.18,
130.07,
74.24,
79.98,
106.76,
76.34,
221.24,
80.55,
63.98,
55.93,
73.94,
65.58,
83.8,
95.87,
112.83,
84.93,
103.08,
85.98,
83.75,
125.43,
101.58,
149.8,
112.55,
70.87,
92.96,
112.19,
85.08,
112.37,
151.26,
73.81,
77.2,
76.08,
123.98,
108.51,
98.07,
85.82,
86.21,
99.01,
80.08,
69.47,
85.07,
88,
144.14,
78.8,
86.78,
105.72,
64.37,
146.1,
58.66,
192.5,
103.5,
89.42,
212.92,
60.73,
73.18,
82.26,
87.4,
86.95,
74,
73.98,
124.78,
191.94,
247.87,
83.2,
88.62,
81.51,
115.71,
229.73,
57.4,
100.47,
81.36,
261.67,
110.2,
98.9,
95.19,
105.28,
83.03,
89.41,
70.56,
78.79,
70.13,
62.68,
71.77,
77.99,
256.74,
93.72,
63.74,
60.67,
112.08,
221.08,
101.81,
60.17,
65.29,
153.24,
61.42,
86.67,
82.42,
100.54,
111.48,
85.33,
126.57,
81.51,
67.97,
77.92,
80.54,
76.47,
87.15,
86.07,
106.52,
94.24,
89.73,
208.39,
128.61,
57.56,
227.23,
77.76,
72.76,
90.11,
203.27,
234.5,
71.12,
190.67,
116.04,
82.95,
112.75,
82.63,
92.75,
117.63,
197.06,
216.07,
76.03,
62.44,
108.82,
101.66,
121.15,
116.44,
93.3,
144.33,
98.02,
80.67,
94.27,
76.35,
57.56,
179.14,
63.49,
145.18,
66.3,
120.56,
69.2,
150.74,
81.38,
78.48,
203.87,
87,
84.68,
91.89,
88.97,
116.98,
135.82,
96.62,
106.53,
58.42,
79.62,
141.84,
78.05,
77.16,
86.3,
83.55,
102,
68.07,
81.77,
82.62,
102.03,
235.06,
76.98,
82.31,
83.84,
98.14,
84.04,
92.24,
94.92,
112.7,
74.34,
72.99,
104.9,
99.96,
85.27,
60.32,
94.14,
195.43,
200.25,
90.31,
95.33,
85.65,
223.64,
76.43,
98.66,
75.84,
85.46,
81.33,
199.78,
176.42,
101.56,
84.48,
93.21,
72.88,
84.1,
72.84,
70.89,
108.14,
244.3,
88.04,
65.84,
106.73,
106.03,
80.88,
140.1,
127.4,
87.72,
58.23,
69.37,
58.64,
59.67,
78.08,
105.08,
89.3,
61.38,
56.11,
75.53,
107.47,
76,
90.28,
65.22,
86.24,
166.38,
78.91,
226.28,
90.73,
172.86,
213.92,
92.23,
79.81,
81.78,
102,
68.42,
94.68,
125.33,
212.19,
89,
60.98,
58.19,
67.68,
84.99,
62.13,
57.1,
97.95,
94.06,
93.88,
90.3,
83.14,
118.66,
100.47,
90.78,
66.46,
73.56,
200.8,
57.28,
92.87,
96.47,
99.44,
77.96,
60.13,
88.17,
84.41,
72.34,
68.72,
222.58,
66.06,
156.43,
105.76,
73.07,
99.48,
79.6,
70,
112.98,
61.07,
63.43,
73.2,
102.73,
74.88,
122.43,
84.21,
80.42,
80.44,
80.63,
104.12,
96.82,
88.1,
64.85,
102.87,
83.75,
97.68,
97.58,
74.16,
86,
100.93,
232.78,
86.96,
67.5,
81.21,
110.14,
120.44,
187.47,
94.62,
89.57,
95.31,
75.07,
106.65,
234.06,
144.15,
58.81,
242.62,
93.04,
150,
63.56,
96.98,
66.7,
79.66,
90.16,
104.09,
90.96,
152.38,
114.54,
174.54,
106.47,
73.63,
98.52,
62.93,
67.87,
120.43,
76.56,
108.03,
83.16,
67.9,
115.46,
123.94,
231.54,
219.8,
187.99,
234.45,
75.47,
98.24,
66.2,
94.67,
75.95,
111.79,
80.89,
86.91,
59.36,
66.67,
87.54,
139.87,
67.92,
83.62,
110.97,
70.53,
115.91,
59.74,
62.64,
84.81,
78.68,
217.57,
79.76,
63.28,
78.48,
70.48,
120.96,
77.29,
108.51,
113.85,
97.65,
121.83,
81.53,
58.35,
65.42,
69.34,
109.09,
66.71,
114.92,
120.58,
165.36,
66.11,
126.34,
68.27,
92.86,
96.77,
89.24,
89.32,
65.63,
123.66,
63.82,
108.06,
136.18,
71.29,
76.58,
119.58,
109.3,
111.94,
81.64,
97.64,
110.32,
79,
95.08,
137.27,
57.83,
84.08,
234.51,
142.64,
69.77,
71.4,
79.61,
77.46,
96.85,
182.22,
67.26,
60.02,
113.45,
112.31,
68.53,
57.15,
214.73,
91.82,
71.8,
63.41,
106.24,
83.01,
67.92,
74.99,
75.22,
61.88,
65.67,
110.18,
119.3,
208.69,
95.18,
107.72,
83.27,
90.29,
81.87,
153.31,
101.46,
72.49,
86.58,
113.26,
84.92,
128.17,
93.93,
99.87,
129.31,
89.43,
101.6,
66.55,
231.72,
96.69,
102.13,
100.88,
66.22,
109.33,
206.53,
83.91,
67.92,
73.48,
101.31,
103.66,
70.73,
77.45,
193.8,
114.34,
69.25,
88.44,
84.09,
87.06,
92.64,
99.36,
135.63,
82.44,
81.88,
95.44,
72.71,
65.45,
203.01,
110.84,
83.12,
55.12,
105.91,
61.57,
83.1,
66.32,
72.56,
97.99,
63.27,
79.6,
177.56,
81.31,
59.91,
132.85,
198.84,
77.12,
87.17,
96.03,
83.56,
80.93,
84.54,
70.51,
56.87,
67.55,
243.52,
108.63,
69.06,
73.89,
84.49,
73.71,
129.01,
72.96,
110.23,
79.49,
89.33,
68.38,
135.79,
82.19,
93.2,
208.31,
111.33,
69.84,
121.71,
80.08,
84.4,
98.61,
86.61,
80.44,
97.23,
91.21,
103.69,
74.12,
71.08,
58.39,
176.34,
73.76,
67.21,
87.98,
94.23,
111.36,
84.07,
117.31,
68.86,
132.88,
211.83,
124.92,
57.42,
215.69,
154.67,
67.66,
153.48,
62.63,
77.93,
80.3,
106.69,
83.07,
140.14,
92.65,
111.37,
99,
78.24,
83.88,
86.21,
57.06,
112.25,
81.06,
104.37,
124.31,
101.95,
116.95,
99.76,
105.48,
100.6,
65.05,
75.9,
267.6,
109.03,
225.35,
196.26,
95.42,
123.04,
85.88,
107.43,
56.85,
102.15,
88.68,
182.52,
93.97,
104.03,
67.99,
83.37,
124.66,
212.87,
96.75,
63.28,
82.64,
71.34,
183.43,
101.19,
88.51,
126.85,
74.17,
76.04,
73.87,
99.07,
82.93,
133.24,
69.88,
69.12,
86.05,
185.27,
97.46,
81.9,
99.58,
124.54,
153.34,
112.35,
160.76,
73.29,
108.08,
206.49,
77.42,
117.59,
61.94,
116.68,
253.86,
203.36,
125.87,
64.09,
65.33,
96.84,
175.92,
100.65,
112.39,
81.1,
98.3,
89.68,
95.16,
91.04,
101.87,
96.86,
91.05,
55.84,
84.04,
92.06,
89.61,
69.92,
89.29,
120.23,
60.22,
223.78,
211.58,
179.38,
193.88,
89.01,
118.41,
138.51,
120.03,
91.01,
61.98,
135.32,
58.72,
90.42,
104,
102.5,
174.37,
106.56,
76.31,
77.28,
90.52,
108.35,
72.03,
90.57,
97.04,
83.89,
152.56,
75.93,
76.89,
62.13,
100.85,
95.05,
82.35,
82.38,
79.15,
76.62,
91.68,
71.88,
71.97,
97.95,
77.93,
82.85,
103.08,
125.2,
82.99,
166.29,
85.28
],
"xaxis": "x2",
"yaxis": "y2"
},
{
"marker": {
"color": "#EBDBBC",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "bmi",
"nbinsx": 30,
"type": "histogram",
"x": [
36.6,
32.5,
34.4,
24,
29,
27.4,
22.8,
24.2,
29.7,
36.8,
27.3,
28.2,
30.9,
37.5,
25.8,
37.8,
22.4,
48.9,
26.6,
32.5,
27.2,
23.5,
28.2,
28.3,
44.2,
25.4,
22.2,
30.5,
29.7,
26.5,
33.7,
23.1,
32,
29.9,
23.9,
28.5,
26.4,
20.2,
33.6,
38.6,
33.7,
39.2,
27.7,
31.4,
36.5,
33.2,
32.8,
27.7,
40.4,
22.2,
25.3,
30.2,
24,
47.5,
20.3,
30,
28.9,
28.1,
31.1,
27.4,
26.4,
21.7,
27,
24.1,
45.9,
44.1,
22.9,
29.7,
29.1,
27.3,
32.3,
41.1,
25.6,
29.8,
26.3,
37.5,
26.2,
29.4,
32.3,
24.4,
31.4,
27.7,
28,
28.8,
31.4,
34.6,
19.4,
28.5,
30.3,
40.4,
24.2,
41.5,
22.6,
24.2,
56.6,
27.1,
30.9,
27.3,
31.3,
24,
31,
28,
30.3,
31.7,
35.8,
28.4,
24,
29,
36.5,
20.1,
36.5,
26.7,
38.7,
29.9,
34.9,
27,
26.6,
25,
23.8,
21.8,
36.8,
30,
27.5,
24.6,
32.9,
26.1,
31.9,
34.1,
27.5,
25.6,
36.9,
31.4,
37.3,
34.1,
25,
45.7,
34.2,
23.6,
27.3,
22.3,
31.4,
26.4,
32.9,
37.1,
45,
25.5,
26.1,
30.8,
32,
29.9,
37.4,
31.7,
34.5,
27.9,
29.5,
46,
42.5,
35.5,
31.1,
26.9,
35.8,
45.5,
28.5,
26.6,
31.5,
32,
30.8,
31.1,
33,
23.4,
26.9,
33.6,
23.9,
26.3,
27.3,
30.7,
20.5,
21.5,
31,
27.1,
40,
28.6,
28.1,
28.4,
42.2,
25.8,
31.9,
31,
27.5,
29.6,
35.4,
16.9,
21.5,
34.4,
28,
26.8,
39.3,
31.7,
32.6,
28.4,
35.9,
21.2,
34.5,
42.4,
40.5,
36.7,
30.9,
29.3,
19.6,
18,
39.2,
17.6,
35.9,
19.1,
50.1,
17.7,
27,
32.3,
54.6,
35,
22,
39.4,
26.1,
42.4,
33,
19.7,
22.5,
24.6,
25.2,
41.8,
60.9,
31.5,
27.3,
23.7,
24.5,
28.4,
26.9,
26.7,
31.2,
25,
25.4,
27.5,
16,
27,
31.6,
25.1,
30.9,
24.8,
23.4,
29.4,
18.3,
20,
19.5,
36,
27.7,
27.5,
28.5,
26.8,
33.6,
29.1,
28.5,
34.9,
25.1,
35.3,
26.4,
31.5,
40.1,
43.1,
36.7,
29.3,
21.2,
31.2,
21.4,
27.9,
34.3,
31,
27.7,
36,
38.7,
27.6,
25.1,
16.5,
22.8,
35.4,
24.3,
34.3,
40.1,
25.7,
21.9,
38.4,
26.1,
30.5,
25.9,
54.7,
29.9,
18.6,
27.1,
24.9,
25.2,
19.4,
29,
48.2,
34.6,
24.6,
25.8,
26.1,
29,
27.2,
20.7,
30,
37.3,
34.1,
23.6,
25.2,
39.5,
23.5,
23.3,
64.8,
28.1,
24.4,
29.8,
24.9,
35.1,
32.3,
43.6,
21,
47.3,
16.6,
37.5,
24.2,
31.6,
21.6,
31,
31.1,
15.5,
27.3,
20.5,
35.6,
16.7,
41.8,
41.9,
16.4,
17.1,
29.2,
27.1,
37.9,
44.6,
22.8,
33.2,
22.3,
26.4,
39.6,
28.1,
39.2,
36,
37.8,
40.3,
41.5,
17.7,
21.2,
41.6,
23.8,
23.7,
24.8,
39,
37.9,
31.1,
23.2,
18.9,
36.1,
36.3,
40.5,
25.4,
46.5,
16.8,
46.6,
26.4,
26.2,
35.2,
20.9,
36.8,
34.4,
22.2,
13.8,
40.3,
28.4,
31.7,
34.2,
54.7,
24.6,
36.9,
31.1,
31.9,
31.8,
18,
28.5,
29.5,
22,
29.4,
28.8,
26.2,
26.9,
23.2,
27.9,
36.8,
28.9,
31,
29.4,
15.3,
37.1,
30.5,
38.2,
23.2,
30.2,
45.2,
21.8,
24.4,
17,
19.5,
49.8,
27.8,
25.1,
26.8,
60.2,
27.5,
28.1,
27.3,
27.4,
22.2,
22.9,
26.6,
23,
32.6,
22.1,
22.5,
25.5,
31.4,
26,
20,
31.6,
31.2,
21.7,
24.2,
25,
36.7,
28.9,
29.7,
44.3,
51,
39.7,
34.7,
35,
21.3,
29.1,
23.9,
36,
41.2,
27.1,
33.2,
25.4,
30.7,
34.8,
19.2,
31.7,
35.7,
37.8,
29.7,
35.8,
23.6,
39.7,
40.5,
21.4,
40.8,
24.7,
21,
45,
26.2,
28.3,
41.6,
19,
32.4,
34,
39.4,
28.7,
31.8,
31.2,
20.9,
32.1,
31,
23.1,
26.7,
27.9,
27.3,
51.5,
20.4,
29.6,
30.6,
33.6,
71.9,
24.2,
17.7,
22.6,
28.1,
26.5,
28.7,
39.5,
35.1,
27.9,
19.3,
28.4,
26.7,
40.9,
17.2,
28.3,
16.1,
27.6,
16.5,
35.8,
16.2,
24.6,
32,
35.3,
19.2,
40.4,
30.7,
24.3,
26.4,
34.7,
31.7,
35.6,
22.8,
28,
35.6,
40.6,
29.3,
21,
20,
26.7,
18.4,
34.5,
27.7,
21.1,
24.4,
19.4,
42.3,
32.2,
26.8,
25.4,
23.5,
50.2,
26.1,
17.5,
24.2,
30.8,
23.4,
30.9,
23.6,
18.7,
27.7,
16.7,
31.2,
17,
29.8,
19.7,
29.1,
27.2,
22.3,
27,
42.1,
34.2,
40.9,
29.4,
32.8,
21.9,
39.6,
28.3,
47.8,
39.3,
28,
27.1,
31.2,
23.1,
20.8,
34.1,
30.1,
35.8,
34.6,
29.8,
26.7,
30.2,
29.7,
54.6,
23.3,
35.6,
27,
21.6,
29.4,
22.8,
17.3,
29.8,
36.4,
34.7,
28.7,
26.7,
22.1,
27.7,
40.5,
23,
25.3,
22.1,
28,
12,
28.4,
29.6,
36.2,
22.8,
55.7,
24.3,
26.9,
25.3,
35.3,
18.3,
26,
21,
55.7,
27.6,
20.5,
30.2,
21.9,
28.8,
36.2,
25.9,
21.4,
20.4,
31.6,
14.4,
23.7,
30.2,
19.5,
32.6,
34.2,
43,
42.2,
19.7,
41.7,
21.6,
24.2,
19.2,
25.8,
23.2,
20.8,
28.4,
30.2,
23.1,
16.7,
39.5,
33.8,
34.6,
25,
43.9,
27.1,
25.9,
22.7,
27.1,
25.6,
28.4,
57.5,
35.8,
19.5,
31.2,
43.6,
31.2,
23.5,
18.7,
24.4,
29.4,
37,
29.4,
38.5,
23.5,
16.3,
35.9,
35.9,
20.3,
32.3,
27.9,
22.3,
31.8,
29.7,
27.1,
24.5,
28.9,
24.6,
31.6,
32.3,
41.1,
30,
26.4,
30,
20.8,
44,
30.6,
17.2,
29.1,
27.4,
23.5,
31.8,
23.5,
28.5,
32.7,
54.2,
25.6,
41.2,
27,
21.3,
34.3,
29.5,
31.6,
26.1,
27.5,
26.5,
33.2,
40.2,
32.5,
23.4,
32.5,
23.9,
29.5,
24,
17.7,
26.2,
33.3,
17.4,
29,
21.7,
37.8,
41.8,
24.2,
31.1,
23.1,
25.1,
41.3,
22.7,
24,
20.5,
20.4,
27.6,
27,
26.4,
34.9,
35,
28.5,
32.3,
23.9,
52.3,
26.4,
20.9,
23.3,
32.7,
26.5,
27.9,
30.3,
27.6,
14.6,
40.9,
28.4,
23.7,
27.9,
25.2,
34.4,
36.7,
22.2,
27.2,
27.3,
27.3,
42.2,
26.4,
39.4,
34.8,
20,
34.1,
31.4,
17.8,
46.1,
28.1,
24.7,
22.7,
34.6,
21.4,
27.4,
36.6,
32.9,
24.7,
21.4,
33.1,
26.7,
24.4,
25.8,
34.3,
18.1,
43.8,
26.9,
36.6,
24.9,
27.6,
20.9,
30.3,
37.4,
35.9,
50.3,
31.5,
24.4,
38.9,
28.6,
27.5,
43.7,
27,
29.3,
34.7,
39.9,
26.7,
24.8,
29.7,
15.9,
31.4,
27.8,
35.5,
31.7,
33.2,
27.7,
31.9,
25.8,
27.7,
23.4,
24.3,
30.3,
29.1,
20.1,
21.2,
31.6,
41.6,
36.4,
30.5,
32.8,
32.3,
30.1,
34.7,
29.7,
31.3,
35.6,
35.2,
27.6,
35.2,
28.1,
31.8,
23.3,
28.8,
19.5,
30,
25.5,
28.8,
40.2,
32.9,
22.4,
36.9,
19.8,
12.3,
32.4,
24.8,
16.4,
35.9,
23,
78,
30.5,
26.8,
35.3,
27.9,
22.1,
38.3,
41,
22.8,
30.1,
20.8,
31.7,
30.1,
30.8,
42.6,
37.1,
34.2,
43.4,
18.7,
34,
23.2,
41.7,
15.1,
20.6,
18.9,
26.6,
30.1,
32.1,
15.1,
33.5,
23.4,
43.2,
19.1,
26.1,
17.3,
32.1,
30.4,
29.9,
32.8,
22.7,
28.7,
35.2,
22.4,
32.3,
18.6,
42.1,
38,
22.3,
33.4,
23.2,
18,
20.1,
19.2,
28.7,
28.1,
22.6,
18,
21.4,
27.8,
27.6,
32.2,
24.9,
27.1,
24.6,
24.6,
18.9,
16.3,
31.8,
21,
20.1,
29.1,
32.3,
29.4,
50.2,
44.9,
28.5,
19.5,
45,
22.8,
25.5,
31.5,
31.6,
31,
29.5,
23.1,
30.7,
44.7,
33.5,
25.9,
28.3,
26.8,
27.8,
36,
22.1,
38.4,
30.1,
26.2,
26.6,
32.6,
27,
25.3,
31.9,
18.4,
30.4,
28.2,
35.7,
35,
27.2,
23,
25,
24.5,
30.9,
26,
27.2,
30.4,
21.7,
29.1,
17.6,
29.2,
28.1,
26.6,
26.9,
36.2,
25.9,
40.9,
31.5,
23.3,
37.6,
39.8,
35.1,
21.9,
53.4,
34.4,
24,
29.3,
26,
31.3,
31,
26.5,
16,
21.1,
42.2,
27.7,
23.6,
18.3,
44.3,
27.3,
55.2,
44.7,
33.1,
30,
40.1,
23.1,
30.3,
30.5,
22.8,
42,
29.9,
24,
36.7,
22.2,
25.5,
34.3,
16,
25.6,
23.3,
41.6,
35.6,
22,
26.8,
31.9,
19.7,
31.1,
34.4,
33.2,
21.6,
25.4,
41.6,
33.1,
21.8,
30,
35.2,
35.6,
27.8,
29.3,
37.2,
26.1,
21.6,
22.7,
30.2,
18,
27.8,
23.5,
41.2,
22.7,
29.2,
42.2,
24,
45.5,
19.4,
28.2,
20.1,
27.2,
26.5,
17.6,
42.8,
29.3,
21.3,
29.8,
24.1,
35.2,
18.8,
17.4,
31.5,
29.9,
27.3,
28.1,
28.9,
25.2,
24.3,
37.9,
28.2,
18.4,
23.1,
39.4,
29.5,
18.3,
28.7,
32.9,
26.7,
20.1,
17.6,
43.7,
41.1,
26.8,
31.2,
27.4,
25.1,
33,
30.7,
42.9,
14.3,
33.2,
43,
22.3,
32.8,
30.5,
36,
26.5,
20.1,
28.1,
16.2,
30,
31.3,
26.7,
37.7,
35.8,
27,
20.7,
22.2,
41.5,
33.5,
21.8,
22.1,
30.2,
38.7,
32.1,
41.3,
20.1,
22.1,
24.9,
23,
29.3,
22.8,
31.6,
19.4,
22.5,
31.6,
29.1,
43.4,
26.5,
29.7,
21.8,
23.4,
23,
21.5,
14.6,
28.5,
29,
26.9,
29.6,
28.5,
25.5,
30.5,
23,
22.8,
31.4,
35.2,
23.3,
31.2,
24.8,
25.8,
48.4,
43.1,
27.8,
21.4,
22.1,
31.8,
20.1,
29.1,
22.6,
25.1,
33.3,
15.9,
20.8,
19.5,
39,
23.1,
18.8,
50.6,
36.3,
32.8,
35.8,
26.7,
26.3,
26.5,
43.7,
24.7,
46.2,
49.5,
43.3,
30.9,
38.7,
28.6,
30.2,
30.5,
33.9,
28.6,
24.5,
17.2,
27.2,
19.3,
18.5,
32,
44.5,
37,
30.8,
24.7,
18.3,
44,
27.6,
45.4,
28.5,
17.6,
27.5,
32.8,
29.5,
36.4,
26.1,
35.3,
18.1,
27.1,
23,
55,
26.6,
24.1,
30.3,
32.2,
26.5,
30.6,
25.8,
41.9,
29.2,
44.3,
29.1,
17.5,
31.9,
32.2,
26,
25.4,
54.8,
27.9,
32.8,
34,
25.5,
35.6,
28.3,
36.9,
22.1,
31.5,
26.7,
24.9,
32.4,
32.2,
30.3,
28.7,
24.1,
25.3,
24.8,
34.8,
39.2,
24.7,
28.6,
27.7,
33.1,
27.3,
20.3,
33.9,
28.3,
32.4,
19.7,
33.7,
35.5,
20,
16.3,
36.4,
28.1,
26.9,
29.4,
17.2,
26.5,
19.5,
30.6,
31.9,
26.6,
19.9,
18,
26.7,
25.3,
31.4,
21.1,
17.9,
27.5,
37.6,
24.1,
18.1,
31,
26.6,
38.7,
27.2,
27.8,
28.3,
27.5,
43.3,
20.1,
25.9,
25.8,
16.4,
29.4,
28.9,
26.7,
27.8,
38.7,
26.8,
36.1,
26.7,
27,
21.2,
21,
37.4,
26.5,
28.9,
29.5,
17.7,
34.9,
28.1,
36.8,
33.3,
29.2,
34,
33.9,
28.7,
21.1,
38.2,
27.7,
35.8,
27.1,
20.5,
35.6,
15.6,
43,
17.8,
25.8,
20.5,
19.9,
24.4,
18.3,
35.1,
35.1,
37.1,
29.4,
31.7,
31.4,
33.1,
28.5,
24,
18,
27.6,
16.9,
16.4,
35.1,
28.8,
17.6,
21.3,
19.7,
37.3,
35.5,
25.5,
30,
21.9,
45.5,
34.9,
19.3,
32.1,
26.9,
32.5,
19.5,
31.7,
21,
20.3,
21.5,
40.4,
30.1,
40.2,
34.7,
25.9,
23.7,
34.3,
34.3,
26.3,
22.1,
33.3,
37.3,
20.5,
19.1,
34.7,
16.3,
28,
17.2,
41.1,
25,
27.5,
21.5,
15.1,
29.6,
30.2,
34.9,
30.7,
36.5,
21.5,
33.2,
50.2,
22.3,
27.3,
28.3,
31.9,
24.9,
32.7,
26.4,
24.8,
30.7,
25.4,
32,
24.4,
20.4,
29.5,
24.4,
22.8,
20,
36.3,
32.8,
34.4,
30.5,
29.7,
36.9,
34.2,
37,
47.5,
24.2,
30.6,
52.8,
38.6,
32.8,
42.9,
25.9,
24.7,
35.8,
31.2,
26.1,
30.8,
40.5,
25.2,
28.7,
30.4,
15.2,
36.6,
34.5,
23.7,
35.7,
38.9,
40,
18.4,
29,
20.6,
28.2,
66.8,
26.2,
34.8,
34.5,
30.6,
55.1,
18.8,
29.1,
22.1,
28.9,
34.7,
20.3,
25.1,
18.2,
25.5,
34.4,
48.5,
25.2,
32.5,
42.1,
19.5,
27.7,
25.1,
29.6,
32.9,
55.9,
25.1,
24.7,
17.5,
20.8,
23.3,
22.8,
26.6,
25.5,
25.8,
25.4,
57.3,
20.2,
27.7,
23.1,
14.3,
23.3,
23.4,
22.8,
30.9,
28.4,
41.9,
34.3,
25.1,
10.3,
31.8,
31.8,
23,
25.4,
14.1,
35.1,
26.9,
33,
22.1,
20.8,
30,
44,
16.6,
28.4,
26.3,
39.4,
15.7,
34.1,
29.8,
23.6,
18.3,
28.3,
18.5,
18.4,
24.1,
25.5,
25.3,
39.9,
28.8,
37.2,
49.8,
34.3,
29,
37.2,
19.4,
26.2,
21.1,
31.6,
29.3,
28.4,
35.3,
40.1,
39,
31.5,
28.5,
28.5,
56,
28.6,
35.2,
32.5,
35.9,
25.1,
16.8,
27.9,
16.3,
21.4,
32.7,
23.6,
19.8,
33.1,
28.1,
38.9,
23.8,
33.8,
26.7,
32.2,
28.1,
21.3,
44,
21.5,
37.3,
44.8,
21.4,
32.3,
35.4,
19.7,
31.3,
35.5,
25.8,
32.8,
35.9,
41.8,
27.3,
27.7,
13.4,
17,
20,
40.9,
38.5,
30.9,
33.4,
17.1,
28.6,
24,
32,
28.4,
20.3,
33.8,
43.2,
22.7,
29.9,
29.9,
22.7,
16.1,
33.8,
22.6,
25.5,
22.5,
27.9,
23.5,
32.2,
29.9,
28.7,
37.2,
22.2,
26.6,
24.6,
41.7,
26.2,
27.1,
41.8,
34.2,
19.2,
25.2,
18.7,
43,
28.2,
34.7,
25.5,
39.8,
20.8,
28.2,
31,
21.6,
26.2,
26,
22.5,
24,
31.5,
36.3,
25.4,
32.2,
39.8,
18.6,
30.7,
16.4,
20.5,
28.7,
29,
24.4,
29.9,
25.3,
23.5,
20.5,
33.5,
22,
29,
30.7,
37.4,
51.8,
16.6,
29.5,
27.3,
27.5,
33.9,
27.9,
22.9,
32.1,
18.2,
30.2,
31.3,
20.3,
23.2,
30.8,
22.9,
42.8,
22.2,
28.7,
32.3,
24.2,
23.9,
34.2,
29,
29.9,
23,
17.3,
21,
34.3,
19.8,
24.3,
16.4,
32.6,
23.7,
27,
25.6,
34,
24.9,
26.3,
43.1,
39.6,
15.2,
41.8,
20.1,
39.3,
28.3,
27.5,
33.9,
20,
34.8,
20.7,
24.6,
39.4,
29.4,
24.8,
32.3,
25.3,
39,
29.4,
31.9,
26.4,
20.6,
26.3,
23.6,
18,
26.4,
22,
30.5,
29.1,
24.9,
36.4,
31.2,
32.8,
31.6,
35.8,
35.2,
28,
24.9,
35.7,
30.7,
33.9,
27.8,
23.2,
17.1,
31.6,
23.8,
29.6,
45,
25.6,
27.7,
25.5,
18.3,
21,
28.6,
23.5,
36.1,
29.2,
27.6,
36,
20.4,
19.9,
29.2,
27.9,
38.1,
24.1,
37.8,
29.1,
35.7,
34.5,
34.1,
21.1,
57.7,
22.2,
20.5,
26.2,
23.5,
29.1,
39.8,
21.2,
20.2,
30.8,
33.8,
17.1,
21.3,
22.3,
28.9,
26.7,
41,
16.7,
29.1,
23.6,
27.8,
31.4,
28.2,
30.8,
36.1,
32.7,
26.4,
20.7,
18.5,
33.5,
14.1,
23.4,
23.2,
36,
35.6,
21.8,
34.9,
20.4,
25,
22.3,
27.3,
28.9,
30.4,
36.9,
27.7,
44.4,
28.2,
35,
20.6,
26.5,
25.1,
22.2,
27.3,
27.3,
41.9,
38.8,
18.3,
19.5,
17.2,
20.8,
23.8,
24.7,
23.1,
22.4,
29.9,
20,
28.7,
26.2,
29.6,
30.8,
32.7,
21.8,
35.7,
25.4,
32.8,
21.1,
31.7,
20,
34.7,
28,
16.4,
24.6,
24.6,
30.3,
23.7,
33.5,
24.9,
48.9,
31.4,
18.4,
24.2,
34.3,
25.1,
33.3,
22.6,
26.3,
29.7,
18.5,
21.4,
45.5,
34.2,
32.7,
38.1,
26.7,
22.6,
27.7,
20.7,
25.7,
17.3,
23.3,
16.2,
20.5,
33.6,
20.2,
49.3,
26.2,
30.1,
25.1,
32.7,
26.4,
21,
24.1,
20.4,
30.9,
39.1,
27.7,
29.5,
30.3,
37.8,
30.2,
24.2,
25.2,
22.2,
17.7,
29.6,
32.2,
28.7,
19.5,
38.5,
20.6,
21.5,
43.8,
18,
28.2,
26.6,
35.5,
30.7,
18.7,
22.4,
25.2,
24.2,
20.5,
24.5,
24.8,
42.6,
23.8,
42.3,
42.2,
26.1,
22.8,
42.3,
28.5,
23,
32.8,
49.8,
23.8,
31.7,
34.9,
54,
26.8,
21.3,
19.8,
42.4,
16.4,
28.2,
26.3,
17,
23.3,
56.1,
25.1,
35.3,
36.3,
30.4,
43.9,
26.4,
28.3,
42.3,
38,
32.7,
28.8,
38.5,
30.1,
31.3,
28.4,
26.6,
20.4,
34.7,
32.5,
30.7,
29.6,
39.4,
26.1,
27.2,
41.3,
20.9,
41.5,
31.1,
24.5,
23,
24.7,
19.3,
37,
31,
33.1,
26.6,
27.7,
40.3,
19.5,
26.7,
26.1,
38.2,
21.3,
97.6,
40.9,
27.3,
20.9,
29.2,
17.9,
38,
26.1,
53.9,
22.2,
24.6,
33.4,
34.4,
17.6,
19.2,
31.8,
28.1,
32.1,
17.4,
20.2,
23.4,
39.5,
21.5,
24.3,
15.3,
32.4,
16.1,
34.3,
43.8,
19.6,
36.7,
33.7,
38.9,
28,
23.9,
16.6,
42.9,
17.1,
27.2,
30.3,
33.9,
20,
19.4,
21.5,
28.5,
34.4,
43.7,
13.7,
33.7,
18,
27.9,
21.8,
35.8,
23.1,
30.3,
32,
25,
30.7,
24.3,
11.5,
30.7,
33.7,
26.5,
17.6,
22.5,
18.5,
22.4,
25.3,
37.3,
39.7,
28,
41.4,
25.3,
28.1,
41.2,
28.8,
31,
28.3,
23.9,
44.2,
27.6,
35.7,
37.4,
27.5,
23.4,
25.8,
22.8,
19.5,
38,
23.4,
29.5,
28.2,
14.2,
28.9,
31.4,
32.4,
22,
26.4,
26.1,
37.6,
18.8,
23.8,
49.4,
23.8,
20.4,
40.3,
23.6,
34.3,
32.7,
28.7,
24.5,
24.8,
23.2,
23,
39.3,
20.1,
25.4,
23.8,
28.4,
15.4,
28.2,
26,
41.5,
32.2,
25.7,
33,
35,
27.4,
16.4,
31.5,
29.2,
25.9,
32.3,
30.4,
45.1,
25.9,
36.6,
18.6,
27.5,
31.3,
37.5,
23.1,
35,
27,
29.8,
23.5,
48.5,
31.8,
20,
29.4,
24.3,
45.4,
29.5,
49.2,
29.4,
29.1,
38.8,
23.4,
48.7,
19.7,
20.3,
33.5,
36.7,
24.4,
38.8,
44.2,
33.4,
32.5,
15.1,
21.5,
30.4,
33.4,
18.2,
18.7,
38.4,
36.4,
20.6,
48.9,
18.2,
17,
31.8,
33,
24.7,
28.1,
40.3,
35.2,
31.2,
37.6,
38.7,
28.1,
29.2,
31.1,
24.1,
18.7,
39.6,
15.7,
53.8,
32.4,
42.7,
18.6,
31.4,
33.7,
24.5,
33.7,
35.4,
22.5,
33.6,
29.7,
25.7,
20.2,
22.7,
17.4,
22.2,
23.8,
28.7,
33.1,
28,
16.7,
27.1,
26.4,
23.7,
33.3,
18.9,
25.5,
35.9,
23.2,
22.2,
34,
20.4,
34.5,
31.9,
19.8,
29,
36.7,
21.3,
23.7,
25.7,
34,
30.7,
44.8,
27.7,
23.2,
30.1,
29.3,
29.5,
25.8,
26.2,
28.8,
29.8,
26.5,
27.8,
29,
19.1,
17.1,
24.6,
18.3,
42.8,
46.5,
38.4,
26,
26.8,
21.1,
29,
43,
36.6,
27.5,
40.2,
37.1,
32,
32.2,
20.6,
24.1,
25,
25.2,
27.6,
48.8,
27.6,
39.5,
27.2,
26.3,
28.9,
30,
23.7,
25.5,
24.6,
20.7,
36.4,
23.4,
32.3,
18.8,
15.5,
21.4,
31.7,
29.7,
34.3,
22.1,
21.5,
30.4,
21.6,
30.9,
21.9,
19.2,
30.3,
23,
24.1,
52.7,
27.6,
23.9,
26.9,
23.9,
40.5,
32,
20.5,
44.5,
16.2,
35.8,
24,
33.9,
36,
22.9,
44.5,
17.5,
32.3,
30.3,
33.5,
30.9,
25.5,
30,
32.2,
21.7,
24.7,
28.8,
27,
26,
27.1,
18.5,
18.8,
31.5,
18.1,
24.3,
28,
17.3,
17.2,
26.9,
22.4,
25.8,
21.3,
43,
24.4,
30.1,
21.3,
17.8,
28.4,
23.4,
24.3,
27.8,
30.5,
25.8,
26.7,
28.1,
22.2,
25.1,
20.9,
28.9,
35.9,
29.8,
26.7,
28.4,
26.7,
32.5,
43.8,
28.3,
36.6,
33.1,
27,
23.6,
21.8,
23.5,
28.7,
31.6,
31.9,
25.2,
25.9,
32.5,
28.1,
27.4,
21.2,
26.8,
18.5,
30.6,
19.9,
26.5,
43.8,
31.8,
36.6,
22.9,
18.7,
29.6,
43.6,
20.3,
21.3,
39.2,
52.8,
30.2,
35.3,
37,
32.1,
30.5,
22.8,
34.5,
37.4,
19.5,
55.7,
30.4,
25.3,
23.1,
30.7,
18.8,
28.6,
27.5,
26.5,
40.3,
37.9,
17.1,
53.5,
33.6,
43.4,
30.5,
38.1,
29.9,
32.7,
28.6,
38.5,
22.9,
38,
43.8,
20.8,
26.8,
25.5,
25.4,
26.7,
50.5,
21.3,
34.7,
17.9,
36.3,
18.4,
34.5,
33.4,
19.4,
25.6,
24.5,
30,
28.9,
28.1,
23,
33.5,
34.8,
17.6,
21.1,
29,
15.8,
41.2,
29.7,
40.8,
33.2,
37,
24.5,
34.5,
28,
26.8,
21,
23.8,
29.3,
24.4,
22.6,
22.8,
29.7,
32.4,
35.8,
31.1,
31.9,
33.7,
25.6,
19.9,
45.3,
15.5,
28.3,
40.2,
24.3,
24.6,
26.6,
30.9,
23.5,
23.9,
34.2,
34.9,
26.2,
36.2,
22.4,
19.3,
41.7,
46,
26.9,
40.1,
27.7,
29.5,
26,
22.7,
16,
27.7,
33.1,
33.1,
29.7,
34,
33.3,
27,
14.2,
19.4,
15.3,
28.7,
21,
28.9,
20.3,
28,
29.6,
30.2,
22.2,
28.4,
29.6,
28.6,
32.6,
29,
30.1,
31.2,
20.7,
45.1,
14.8,
37.4,
19.9,
16,
27.4,
33.4,
18.5,
33.4,
20.8,
22,
32.5,
19.2,
32.9,
23.5,
21.7,
29.1,
30.3,
29.4,
30.1,
32.9,
34,
38.6,
29.3,
43.6,
22.7,
36.6,
22.6,
25.6,
31.4,
26,
27,
33.3,
17.3,
26.9,
24.8,
20,
35.8,
36.9,
29.6,
31.1,
25.1,
51.9,
36.3,
28.2,
35.8,
30.9,
22.1,
17.6,
29.5,
27,
33.2,
24.4,
34.4,
36.7,
27.2,
27.5,
32.6,
28.4,
25.5,
28.7,
23.8,
34.4,
26.1,
28.5,
29.6,
29.4,
28.2,
28.7,
38.1,
22.2,
25.7,
17.7,
26.4,
32.8,
34.5,
39.5,
23.5,
38.8,
21.2,
32,
63.3,
32.6,
25.2,
27.4,
38.6,
32.9,
26,
29.2,
29.2,
26.4,
23.1,
21.2,
26.2,
23.4,
19.8,
20.4,
25.9,
34.1,
18.3,
35,
26.4,
27.6,
27,
31.3,
18,
22.1,
34.4,
33.2,
23.1,
39.7,
40.7,
30.9,
27.5,
28.3,
33.4,
33.1,
41.5,
17.1,
30,
24.9,
32.5,
26.1,
19.2,
38.7,
29.5,
28.1,
14.1,
33.9,
26,
52.8,
29.2,
26.3,
23.6,
18.6,
29.7,
30.9,
22.8,
25.5,
25.6,
23,
20.6,
28.4,
29.7,
26,
37.5,
42.3,
21.4,
29.3,
31,
22,
20.9,
45.9,
61.2,
29.1,
28.4,
22.6,
24.2,
16.3,
35.9,
29.8,
37,
35.8,
21.1,
37.6,
24.8,
31.9,
17.5,
17.6,
38.9,
15.2,
27.4,
27.9,
16.1,
39,
36.6,
33.7,
27.6,
31,
28.5,
31.4,
25.3,
17.2,
26.9,
32,
22.5,
17.3,
27,
33.1,
25,
31.1,
19.6,
24.7,
48,
38,
24,
30,
24.6,
23.1,
17.4,
43.1,
27.1,
14.3,
46.8,
26,
29.3,
50.1,
18.6,
27,
25.7,
25.5,
27.3,
24.1,
42.7,
41.2,
20.2,
30.9,
27.7,
22.3,
39.4,
34.2,
20.1,
36.8,
33.9,
27.7,
22.7,
32.2,
22,
23.2,
26.7,
23,
23.4,
24.1,
31.5,
26.1,
23.7,
20.8,
18.8,
32.5,
24.3,
24.1,
32.5,
39.3,
23.9,
22,
37.3,
33,
24.7,
32.8,
29.8,
24.1,
21.6,
27.1,
27,
18.7,
35.8,
27.1,
33.1,
25.9,
26.7,
22.2,
26,
23.5,
23.5,
15.9,
24.9,
26.5,
35.7,
32.1,
28.5,
40.1,
26.6,
21.7,
28.8,
32,
31.4,
32.1,
19.8,
25.3,
28.8,
26,
30.7,
43.3,
35.2,
20.9,
35.3,
27,
26.1,
29.1,
32.6,
21.8,
32.3,
28.1,
32.3,
21.7,
25,
28.9,
21.7,
45.3,
40.2,
22.5,
21.3,
26.1,
28.8,
32.4,
20.4,
25.9,
38.8,
30.8,
24.6,
20.6,
38.8,
40.4,
36.2,
18.8,
27.6,
28.3,
39.4,
33.5,
40.4,
25.9,
27.6,
44.7,
30.8,
22.3,
27.9,
22.9,
27.3,
25,
48.3,
22.7,
39.1,
27.8,
36.7,
38.5,
29.4,
30.5,
23.6,
20.6,
31.7,
24.9,
33.4,
22.2,
26.5,
27.1,
30.3,
18.6,
26.4,
17.8,
37.4,
32.1,
38.2,
39.1,
29.5,
28.6,
40.1,
31.8,
34.6,
24.5,
26.2,
33,
22.3,
58.1,
36.9,
43.9,
18.2,
23.1,
32.6,
41.2,
28.6,
26,
36.8,
35.8,
24.2,
22.9,
23.1,
28.9,
26.5,
29.3,
24.8,
20.1,
18.2,
34.7,
24,
29,
27.3,
27.6,
20.4,
17.7,
27.9,
41.5,
27.4,
27.8,
17.8,
32,
37.3,
28.1,
29.1,
22.9,
38.8,
29,
28.6,
29.8,
28.9,
29.1,
16.7,
34.1,
25.7,
30.8,
23.9,
18.8,
41.8,
36.4,
28.7,
28.3,
22.4,
15.1,
22.7,
26.6,
20.8,
23,
35.8,
29.9,
43.4,
36.3,
40.1,
35.9,
32.4,
20.3,
33.2,
22.3,
18.1,
28.2,
29.7,
29,
25.4,
26.5,
22.7,
27.5,
23.8,
35.6,
27.2,
30.4,
21.5,
31.8,
29.5,
30.6,
27.6,
23,
24.8,
28,
22.2,
27.8,
30.2,
26.8,
22,
34.3,
29.2,
30.3,
21.4,
26.6,
30,
33.7,
30.9,
31.1,
23,
20.2,
31.6,
23.9,
26.1,
15.8,
28.7,
28.9,
25.1,
19.8,
30.8,
22.1,
20.4,
31.2,
24.9,
27,
20.1,
24.4,
22.6,
22.4,
26.2,
22.9,
32.6,
31.3,
43.9,
41.8,
37.7,
20.8,
21.6,
22.1,
39.2,
36.7,
32,
25.9,
28.9,
29.3,
32.3,
33.2,
31.6,
20.6,
23.3,
30.8,
23.7,
49.3,
30.3,
24.4,
28.2,
33.8,
42.6,
31.9,
34,
19.8,
30.5,
21.7,
24.6,
38.1,
19.2,
30.1,
23.3,
34.5,
30.8,
43.8,
29.9,
50.4,
22.6,
19.5,
33.4,
24.2,
52.7,
25.6,
26.9,
35.2,
22.1,
34.4,
26,
26.9,
20.1,
24.7,
32.1,
29.8,
27.5,
22.8,
27.6,
33,
33.7,
25.1,
15.3,
22.2,
21.4,
26,
29.9,
28.6,
21.2,
16,
22.7,
25.3,
31.6,
38.9,
27.1,
24.1,
31.5,
32,
31.8,
34,
23.4,
29.3,
17.2,
31.9,
31.6,
23.2,
30,
26.3,
31.8,
25.5,
21.3,
29.6,
28.7,
30.8,
32.1,
34,
16.9,
32.1,
22.6,
31.3,
29,
27.6,
17.7,
35.9,
33.8,
17.6,
48.3,
39.9,
11.3,
31.5,
29.6,
19.4,
18.1,
36.2,
17.9,
33,
20.4,
33.6,
34.5,
30,
12.8,
26.2,
13.5,
25.6,
18.8,
14.5,
23.4,
39.7,
40.8,
29.5,
33,
25.6,
33.1,
28.1,
49.3,
16.9,
35.9,
32.1,
34.1,
15.1,
42,
16.8,
28.8,
32.2,
16.2,
44.2,
23.4,
31,
28.7,
23.6,
35.7,
39.5,
27.3,
31.3,
33.4,
29.2,
26,
18.1,
22.4,
29.4,
21.6,
27.9,
29.6,
30.2,
43.4,
30.5,
33.2,
27.7,
33.5,
28,
31.5,
24,
27.4,
37.9,
26.2,
22.4,
43.7,
28.4,
29.8,
24.8,
34.8,
27.4,
33.5,
35,
24.4,
28.3,
29.1,
31.3,
32.8,
29.1,
31.9,
14.2,
26.3,
22.8,
24.8,
34.1,
21.5,
26.1,
21.4,
33.5,
23,
18.5,
23.4,
31.4,
31.1,
37.7,
25.3,
35.6,
24.8,
32.7,
24.9,
26,
22.8,
24,
20.3,
30.3,
23.7,
25.7,
21.9,
15.8,
30.5,
25.7,
19,
28.8,
22.4,
32.4,
36.6,
27.9,
27.7,
34.3,
26.7,
19,
20.6,
26.7,
29.2,
21.3,
51.9,
14.1,
28.9,
30.9,
36.1,
25,
28.6,
20.5,
16.7,
36.6,
26.4,
33.1,
27.5,
26.9,
23.9,
26.9,
27.8,
32.7,
28.7,
22.6,
26.5,
28.3,
24.3,
24,
32.4,
28.7,
36.7,
27.7,
24.8,
31.2,
26.1,
26.8,
26.7,
19.6,
25.8,
32.5,
28.3,
24.5,
25.2,
32.3,
30.1,
35.7,
20,
23.5,
23.4,
22.3,
28.7,
20.9,
29.4,
27.6,
43.9,
28.7,
34.7,
37.1,
24.2,
22.7,
29.3,
20,
22.7,
25.4,
37.6,
44.7,
26.3,
25,
32.3,
24.9,
33.7,
26.6,
53.4,
41.7,
22,
20.3,
42.7,
28.2,
32.5,
22.5,
26.5,
41,
27,
25.9,
23.7,
30,
15,
21.5,
25.9,
24.3,
39.2,
14.4,
26.6,
30.7,
35.4,
29.2,
26.7,
21.5,
19.1,
25.4,
34,
23.9,
20.9,
18.2,
27.6,
50.3,
24.5,
16.6,
24,
18.8,
34,
25.8,
30.3,
28.9,
21.3,
26.8,
29.2,
25,
21.5,
17.1,
32.5,
26.9,
30.4,
29.8,
20.3,
41.1,
33.9,
39,
31.2,
38.2,
44.7,
33.5,
27,
31.1,
29.2,
26.6,
17.9,
24.2,
19.5,
28.5,
21.3,
41.3,
31.3,
40.8,
36.6,
26.7,
18.7,
15.2,
26.8,
25.1,
31.3,
59.7,
29.8,
33.5,
24.5,
33.6,
39.7,
47.4,
26.4,
33,
31.4,
27.2,
35.7,
29.5,
21,
26.6,
24.3,
43.9,
52.5,
27,
32.7,
29.4,
15.1,
32.1,
32.9,
27.6,
26.5,
19.2,
25.9,
21.9,
17.4,
13.2,
25.2,
28.2,
31.5,
25.2,
14.5,
28,
15.1,
31.4,
26.3,
39.6,
28.9,
31.4,
34.3,
27,
27.2,
23.8,
25.3,
32.6,
31.8,
22.4,
28,
23.9,
25.3,
16.4,
26.9,
25.4,
18.7,
37.5,
41.4,
37.5,
15.8,
37.6,
30.5,
22.1,
19.2,
38,
18.7,
25.7,
30.1,
36.3,
20.4,
20.9,
28.4,
33.8,
24,
43.3,
36.4,
37.9,
32.6,
23.9,
18.1,
36.4,
29.2,
30.8,
23.8,
17.9,
31.8,
19.5,
31.5,
24.1,
35.9,
31.6,
19,
18.8,
33.5,
25,
52.9,
24.8,
24.5,
40.4,
20.2,
31.9,
25.4,
15.4,
38.6,
31,
26.4,
37.7,
35.2,
54.7,
21.6,
23.9,
26.1,
39.7,
29.9,
31.4,
34.5,
29.4,
26.9,
22.4,
20.5,
18.6,
31.4,
18.5,
41.8,
21.2,
28.6,
16,
30.6,
29,
28.6,
34.8,
42,
30.3,
30.9,
32.1,
19.6,
32.8,
31.5,
34.7,
21.5,
27.6,
28.6,
21.1,
23.8,
33.5,
33.4,
23.3,
21,
21.2,
18.4,
26.9,
20.2,
21.9,
24.2,
29.6,
38.1,
22,
29.8,
24,
26.9,
38,
16.9,
23.4,
38.5,
21.6,
15.9,
25.1,
41.1,
21.5,
21,
24.1,
30.6,
19.8,
29.2,
33.7,
31,
21.5,
19.1,
21.4,
24.8,
35.1,
28.6,
35.7,
45.4,
34.5,
21.2,
22.7,
39.1,
21.9,
16.5,
23,
22.5,
20.4,
28.7,
22.4,
26.3,
23.6,
25.9,
30.7,
24.5,
24,
20.1,
38,
26.6,
24.5,
41.3,
34.8,
35.5,
24.1,
19,
16.6,
28.6,
22.4,
38.6,
32,
40.1,
32,
23.9,
25,
28.5,
19.8,
25.7,
28,
37.2,
24.8,
39.2,
61.6,
27.2,
38,
36.2,
16.6,
38,
31.4,
35.3,
35.8,
28.6,
34,
15.5,
31.1,
37.5,
37.7,
24.2,
33.4,
33,
25.7,
19.4,
33.4,
18,
40.1,
26.9,
22.5,
27.6,
23.3,
35.7,
28.4,
29.4,
31.9,
25.1,
35.6,
21.1,
17.8,
31.5,
26.6,
44.8,
31.3,
17.2,
25.3,
28.8,
49.9,
18,
23.5,
18.1,
41.7,
20.7,
30.3,
27.8,
18.9,
20.4,
31.8,
44.8,
37.1,
39.9,
24.1,
18.1,
36.3,
19.1,
21.1,
28.9,
18.9,
43.3,
37.2,
32.2,
26.3,
25.4,
18.3,
21.4,
22.9,
16.2,
29.6,
21.5,
24.2,
37.3,
33.3,
33.1,
22.2,
24.4,
30.3,
28.2,
17,
53.8,
30.4,
22.3,
25.3,
29.8,
43.8,
34.5,
25.5,
31.5,
39.2,
47.3,
17,
38.2,
31.1,
22.4,
32,
24.7,
36.3,
20.2,
27.4,
29.6,
54.3,
28.6,
43.9,
22.2,
28.4,
23.7,
18.3,
18.3,
28.9,
27.1,
13.8,
21.9,
24,
20.8,
39.8,
26.2,
43.9,
27.3,
22,
40,
20.1,
42.2,
25.8,
27.2,
30.9,
47.9,
33.1,
37.8,
25.4,
28.4,
16.6,
35.1,
32.7,
29.8,
13,
27.5,
23.6,
36.9,
16.7,
26.5,
37,
27.7,
23.5,
40.4,
27.7,
28.3,
55,
28.7,
25,
24.8,
35.5,
25,
17.3,
37.8,
32.8,
28.7,
28.8,
28.4,
24.9,
29.6,
26.1,
29.5,
23.9,
25.8,
36.2,
31.3,
26.1,
19.1,
17.6,
21.4,
24,
25.5,
19.9,
19.9,
35.5,
26.9,
24.2,
30,
30.9,
26.5,
30.6,
19.1,
23.2,
30.5,
15.8,
16.9,
18,
13.9,
36.7,
30.8,
34.6,
16.3,
38.2,
27.3,
30.4,
34.5,
17.4,
32,
33.4,
40.2,
30.1,
46.2,
35.6,
29.4,
27.3,
17.4,
26.9,
17.7,
23.6,
27.7,
38,
35.2,
32.4,
36.1,
30.6,
29,
22.3,
27.3,
17.4,
39,
26.7,
24.2,
22.8,
25.6,
21.9,
25.9,
30.6,
25.7,
33.3,
23.8,
17.2,
31,
24.7,
27.1,
38.3,
50.9,
29.2,
23.6,
33.5,
16.3,
30.8,
31.1,
26.4,
18.6,
37.3,
26.6,
17.6,
22.3,
20.2,
30.9,
31.5,
29.7,
32.5,
32.8,
25.4,
27.2,
31.6,
39.6,
19.8,
29.6,
25.5,
25.8,
32.1,
25.6,
26.3,
39.4,
25.6,
34,
35.8,
24.9,
29.3,
23.4,
24.5,
29.8,
28.9,
27.3,
23.2,
34.2,
23.3,
26.9,
21.8,
26.9,
29.9,
22.4,
27.5,
35,
27.9,
25.5,
28.6,
34.8,
50.6,
30.9,
14.8,
31.4,
22.3,
19.1,
27.4,
17.5,
36.2,
25.4,
25.7,
28.6,
33.7,
37.9,
28.4,
20,
29.1,
27.6,
27.6,
24.2,
31,
34.5,
27,
20.7,
26.1,
19.2,
28,
23.6,
57.2,
33,
22.2,
24.8,
24.7,
27.9,
21.6,
34.8,
32.9,
28,
25,
24.7,
27.3,
34.2,
39.1,
22.7,
33.9,
18.6,
16.7,
20.6,
20.1,
45.2,
25.3,
32.4,
34.9,
43.7,
23,
21.5,
24.9,
22.9,
25,
21.3,
46.1,
64.4,
37,
21.3,
28.3,
31.1,
25.1,
27.6,
29.4,
14.1,
35.2,
23.9,
34,
27.1,
29.5,
20.7,
39.5,
33.7,
23.2,
30.1,
28.8,
92,
38,
27.7,
43.2,
24.1,
28.7,
34.8,
23.5,
28.5,
20.3,
43,
18.3,
27.2,
22,
50.8,
35.4,
55.9,
27.9,
19.8,
26,
26.8,
32.5,
15,
14.2,
17,
26.2,
24.9,
26.6,
22.7,
32.7,
36.7,
31.5,
36.9,
27.6,
20.7,
20.2,
23.4,
25.3,
28.2,
27,
24.8,
34.8,
17.3,
27.8,
16.2,
21.7,
21.4,
29.3,
36.2,
28.3,
23.5,
25.9,
27.1,
21.5,
30.5,
23.4,
37.6,
24,
25.1,
28.7,
41.8,
25.6,
27.2,
24.4,
20.7,
19.6,
24.8,
32.6,
20.5,
36.2,
45.4,
27.6,
34.7,
32.4,
28.7,
45.3,
40.3,
38.9,
46,
32.4,
19.4,
27.5,
26.2,
31.1,
34.6,
24.3,
34.6,
28.5,
32.1,
28,
20.9,
33.8,
25.8,
31.8,
26.8,
25.9,
29.2,
25.3,
41.7,
38.8,
29.1,
30.9,
39.3,
41.8,
26.2,
33.8,
26.1,
24.1,
30.7,
22.4,
23,
30.2,
30.3,
28.7,
36.3,
16.3,
34.5,
20.8,
31,
17.6,
22.7,
38.4,
40.8,
32.9,
31.3,
28.5,
27.6,
40.2,
24.7,
17.7,
25.4,
28.5,
26.2,
26,
45.3,
31.5,
20.6,
29.4,
57.9,
39.1,
23.8,
38.1,
19.5,
35.5,
26.4,
22.2,
29.1,
41.9,
22.6,
33.3,
21.3,
20.4,
28.7,
28.8,
38.7,
32.3,
37.9,
25.3,
27.7,
25.1,
16.8,
27.7,
28.6,
37.6,
32.7,
22.7,
16,
23.5,
45.8,
20.8,
16.7,
22.2,
33.1,
32.7,
41.4,
24.1,
28,
24.2,
23.5,
23,
20.3,
24.8,
26.1,
24.2,
19.3,
18.2,
24.2,
41.5,
28,
37,
32.2,
35.9,
47.6,
32.1,
55.7,
22.2,
20.1,
14.6,
28.4,
20.6,
19.8,
23.4,
25.1,
30.1,
33.3,
24,
27.8,
21.4,
25.5,
21.8,
18.9,
21.9,
30.6,
29.8,
28.7,
35.5,
29.1,
32.8,
22.8,
31.3,
29,
19,
24.1,
21.4,
28,
27.6,
28.7,
24.5,
25,
29.6,
29.9,
35.6,
27.9,
42.6,
21.2,
28.5,
25.6,
31.1,
30.4,
22.9,
31.9,
48.8,
43,
21.8,
35.5,
24.3,
23.1,
18.5,
42.4,
32.3,
22.6,
17.8,
18.4,
26.9,
32,
23.4,
31.4,
22.4,
28.9,
27.4,
57.2,
28.4,
23.5,
28.3,
38.2,
33.3,
33.3,
28.8,
32.1,
28.9,
25.5,
25.9,
19.5,
23.8,
24.9,
18.5,
30.6,
16.1,
32.5,
27.4,
30.2,
23.5,
36,
24.8,
27.5,
40,
18.1,
18.8,
23.6,
29.7,
43.7,
27.3,
20.2,
23.8,
21.4,
25.1,
17.9,
34.5,
27.1,
26.1,
43.4,
33.2,
37.2,
41.3,
35.4,
26.1,
24.5,
32.7,
29.2,
20.4,
33.5,
14,
33.5,
27.5,
35.3,
30.1,
32.6,
47.5,
31.6,
23.5,
40.3,
34.1,
16.1,
26.4,
19,
34.7,
23.3,
32.6,
26,
35.1,
18.6,
30.4,
32.8,
21.6,
23.3,
31.4,
26.3,
20.1,
28.3,
40.4,
18.6,
44.7,
25.3,
28.3,
39.9,
34.7,
31.9,
35,
32.7,
30.6,
27.9,
20.4,
29.6,
23.5,
46.4,
27.4,
25.2,
25.8,
33.3,
27.7,
28.9,
33.1,
38.1,
27.9,
17.4,
27.1,
22.7,
28.3,
21.5,
30,
18.6,
45.2,
39.7,
31.5,
23.2,
27.3,
26.5,
19.3,
26.1,
19.8,
25.1,
37.3,
30.3,
16.1,
25.1,
26.7,
29.3,
38.7,
35.8,
21.7,
31.4,
26.9,
33,
24.5,
44.9,
25.5,
22.1,
24.3,
32.4,
33.1,
26.7,
37.9,
39.6,
19.8,
30,
23.1,
34.3,
38.4,
32.8,
34.4,
44.6,
32.8,
26.4,
27.3,
32.4,
43.2,
33.6,
26.4,
46.9,
36.3,
34.9,
29.6,
16.5,
35.4,
23.6,
33.1,
40.2,
14.8,
34.8,
33.5,
31.9,
16.1,
50.2,
35.9,
39.6,
27.5,
32.3,
15.4,
28.4,
19.5,
25,
26.3,
39.6,
38.6,
33.9,
32.2,
25.1,
37.4,
30.1,
27,
32.4,
26.8,
27.2,
36.7,
36,
37.2,
19.1,
21.8,
26.5,
35,
32.6,
23.9,
33.7,
29.4,
29,
20.2,
27.4,
25,
30.7,
23,
26.6,
18.1,
47.1,
29.7,
19.9,
13.3,
32.7,
31.8,
31.4,
20.4,
33.2,
30.5,
30.3,
34.2,
34.4,
48.1,
22.8,
30.5,
35.9,
33.7,
24.1,
25.4,
25.5,
29.2,
17.4,
29.9,
21.5,
24.7,
25.1,
34.4,
27.9,
31.5,
31.8,
30.1,
26.4,
27.2,
22.5,
29.8,
26.1,
24.9,
23.5,
30.1,
37.3,
25.5,
35.3,
27.6,
28.7,
29.9,
34.2,
24.9,
36.8,
24.5,
34.6,
30,
28.8,
28.6,
23.6,
20.1,
21.1,
34.1,
29.5,
37.8,
31.2,
31.1,
34.2,
17.7,
39.2,
22.2,
29.5,
27,
16.8,
23.3,
33.5,
15.6,
28.2,
22.6,
20.2,
22.2,
22.6,
26.7,
22.9,
21.5,
30.3,
14.8,
24.3,
17.4,
29,
26.3,
51.7,
29.2,
18.3,
21.9,
16.3,
27.4,
20.4,
23.2,
30.3,
16.7,
31,
33.5,
17,
19.4,
14.6,
32.2,
37.7,
27.4,
25,
20.1,
21.7,
38.2,
17,
28.4,
30,
34.1,
19.3,
21.6,
32.9,
27.2,
27.8,
29.9,
28.4,
24.1,
24,
23.4,
32.6,
33.1,
33.8,
30.3,
36.9,
29.7,
38.8,
30.9,
36.9,
18.8,
25.5,
32.3,
25.8,
31.1,
27.9,
18.6,
20.1,
26.7,
30.3,
30.4,
30,
24.9,
60.9,
24.3,
38.7,
18.6,
17.1,
29.4,
16.9,
34.2,
27.6,
47.8,
18.9,
24.4,
25.2,
30.7,
26.1,
25.3,
32.8,
45.7,
24.6,
32.3,
47.6,
34.4,
18.2,
44.5,
36.6,
19.4,
22.9,
20,
36.1,
27.3,
42.2,
26.4,
23.6,
18.6,
18,
31.1,
30,
43.8,
23.8,
36.2,
32.6,
18,
25.5,
29.9,
32.1,
25.4,
23.4,
23.4,
21.8,
30.8,
37.9,
42.5,
23.4,
35.4,
32.3,
27,
46.3,
30.1,
34.7,
18.3,
16.2,
33.2,
18.6,
25.1,
24.1,
33.1,
24.8,
23.4,
54.1,
28.9,
38.1,
37.2,
19.6,
29,
26.7,
20.5,
34.1,
42.4,
37.7,
39.1,
28.9,
30.7,
33.8,
32.1,
40.5,
24.8,
31.8,
27,
13.7,
23.4,
16.4,
14.9,
38.8,
38.6,
32.2,
30.6,
28.1,
35.2,
21.8,
28.1,
28.2,
28.4,
37.4,
21.8,
19.8,
16.2,
33.6,
21.2,
28.4,
41.1,
24.9,
56.6,
28.8,
33.7,
38.6,
33.8,
22.4,
37.3,
23.4,
39.1,
21.2,
31.2,
28,
21.1,
15.7,
29.1,
26.1,
16.2,
27.1,
28.8,
37.9,
21.6,
30.1,
29.7,
26.4,
46,
26.1,
23.2,
28.4,
20.5,
24.6,
32.2,
30.5,
24.9,
26.5,
34.5,
21.5,
15.9,
17.7,
26.5,
24.4,
26.6,
23.9,
30.1,
23.6,
18.4,
32.1,
24.4,
27.3,
34.8,
28.4,
40.2,
31.1,
24,
38.1,
29.9,
24.5,
49.5,
35.5,
29.5,
28.8,
22.1,
29.4,
45,
27.1,
28.3,
20.1,
31.3,
17.6,
24.5,
24.1,
31.3,
31.5,
29.9,
30.2,
27.8,
15.6,
26.3,
24.8,
17.1,
20.3,
18.7,
28.8,
28.7,
24.6,
25.3,
29.7,
21.1,
26.9,
26.2,
26.3,
24.8,
25,
34.6,
42.7,
24.5,
19.3,
29.3,
22.1,
27.8,
24.7,
25.3,
41.2,
47.6,
23.4,
22.7,
29.7,
32.3,
36.9,
27.7,
24.3,
37.4,
25.1,
24.3,
24.3,
28.7,
16.8,
35.8,
40,
24.3,
25.6,
37.8,
23,
21,
15.5,
17.1,
28,
40.8,
37.5,
24.2,
26.9,
33.1,
21.8,
34.7,
30.2,
16.8,
21,
30.9,
38.9,
24.3,
17.4,
28.2,
40.8,
17.5,
28.3,
24.5,
21.7,
46.9,
18.6,
40,
30.6,
25.6,
26.2
],
"xaxis": "x3",
"yaxis": "y3"
}
],
"layout": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"height": 500,
"margin": {
"b": 80,
"l": 50,
"r": 50,
"t": 80
},
"paper_bgcolor": "#EEECE2",
"plot_bgcolor": "#EEECE2",
"showlegend": false,
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"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": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"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": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"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": "white",
"showlakes": true,
"showland": true,
"subunitcolor": "#C8D4E3"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "white",
"polar": {
"angularaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
},
"bgcolor": "white",
"radialaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"yaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"zaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"baxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"bgcolor": "white",
"caxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
}
}
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene B",
"size": 20
},
"text": "Distribution of age, avg_glucose_level, bmi",
"x": 0.5
},
"width": 1200,
"xaxis": {
"anchor": "y",
"domain": [
0,
0.26666666666666666
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"standoff": 25,
"text": "age"
}
},
"xaxis2": {
"anchor": "y2",
"domain": [
0.3666666666666667,
0.6333333333333333
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"standoff": 25,
"text": "avg_glucose_level"
}
},
"xaxis3": {
"anchor": "y3",
"domain": [
0.7333333333333334,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"standoff": 25,
"text": "bmi"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
},
"yaxis2": {
"anchor": "x2",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
},
"yaxis3": {
"anchor": "x3",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_combined_histograms(\n",
" stroke_df,\n",
" numerical_features,\n",
" nbins=30,\n",
" save_path=\"../images/numerical_distributions.png\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/numerical_distributions.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The histograms reveal the following about `age`, `avg_glucose_level`, and `bmi`:\n",
"\n",
"- **Age:** Shows a relatively uniform distribution across most age ranges, with slight increases in frequency for middle-aged adults (around 45-65). There's a noticeable drop-off for very young (<20) and very old (>80) ages. This uniform distribution is unusual for demographic data and may warrant further investigation into the data collection process or potential sampling biases.\n",
"\n",
"- **Average Glucose Level:** Strongly right-skewed, with a peak around 90-100 mg/dL and a long tail extending to higher values. There's a secondary smaller peak around 200-250 mg/dL, which could indicate a subgroup with diabetes or pre-diabetes.\n",
"\n",
"- **BMI:** Approximately normally distributed, centered around 25-30, with a slight right skew. There are notable outliers at very high BMI values (>60) that warrant further investigation.\n",
"\n",
"**Next up, we can move on to the categorical features.**\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"categorical_features = [\n",
" \"gender\",\n",
" \"hypertension\",\n",
" \"heart_disease\",\n",
" \"ever_married\",\n",
" \"work_type\",\n",
" \"residence_type\",\n",
" \"smoking_status\",\n",
" \"stroke\",\n",
"]\n",
"\n",
"categorical_features_set1 = [\n",
" \"gender\",\n",
" \"hypertension\",\n",
" \"heart_disease\",\n",
" \"smoking_status\",\n",
"]\n",
"categorical_features_set2 = [\"ever_married\", \"work_type\", \"residence_type\", \"stroke\"]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"marker": {
"color": "#CC7B5C",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "gender",
"type": "bar",
"x": [
"Female",
"Male",
"Other"
],
"xaxis": "x",
"y": [
2897,
2011,
1
],
"yaxis": "y"
},
{
"marker": {
"color": "#D4A27F",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "hypertension",
"type": "bar",
"x": [
0,
1
],
"xaxis": "x2",
"y": [
4458,
451
],
"yaxis": "y2"
},
{
"marker": {
"color": "#EBDBBC",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "heart_disease",
"type": "bar",
"x": [
0,
1
],
"xaxis": "x3",
"y": [
4666,
243
],
"yaxis": "y3"
},
{
"marker": {
"color": "#9C8AA5",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "smoking_status",
"type": "bar",
"x": [
"never smoked",
"Unknown",
"formerly smoked",
"smokes"
],
"xaxis": "x4",
"y": [
1852,
1483,
837,
737
],
"yaxis": "y4"
}
],
"layout": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"height": 500,
"margin": {
"b": 150,
"l": 50,
"r": 50,
"t": 80
},
"paper_bgcolor": "#EEECE2",
"plot_bgcolor": "#EEECE2",
"showlegend": false,
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"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": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"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": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"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": "white",
"showlakes": true,
"showland": true,
"subunitcolor": "#C8D4E3"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "white",
"polar": {
"angularaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
},
"bgcolor": "white",
"radialaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"yaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"zaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"baxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"bgcolor": "white",
"caxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
}
}
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene B",
"size": 20
},
"text": "Distribution of gender, hypertension, heart_disease, smoking_status",
"x": 0.5
},
"width": 1600,
"xaxis": {
"anchor": "y",
"domain": [
0,
0.175
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "gender"
}
},
"xaxis2": {
"anchor": "y2",
"domain": [
0.275,
0.45
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "hypertension"
}
},
"xaxis3": {
"anchor": "y3",
"domain": [
0.55,
0.7250000000000001
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "heart_disease"
}
},
"xaxis4": {
"anchor": "y4",
"domain": [
0.825,
1
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "smoking_status"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
},
"yaxis2": {
"anchor": "x2",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
},
"yaxis3": {
"anchor": "x3",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
},
"yaxis4": {
"anchor": "x4",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_combined_bar_charts(\n",
" stroke_df,\n",
" categorical_features_set1,\n",
" max_features_per_plot=4,\n",
" save_path=\"../images/categorical_distributions_set1\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/categorical_distributions_set1_chunk_1.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The bar plots reveal the following about `gender`, `hypertension`, `heart_disease`, and `smoking_status`\n",
"\n",
"- **Gender:**\n",
"\n",
" - The dataset contains more females than males.\n",
" - There's a very small number of \"Other\" gender entries, which may need special handling in the analysis.\n",
"\n",
"- **Hypertension:**\n",
"\n",
" - Highly imbalanced distribution.\n",
" - The vast majority of patients do not have hypertension (value 0).\n",
" - This imbalance will need to be addressed in the modeling phase to prevent bias.\n",
"\n",
"- **Heart Disease:**\n",
"\n",
" - Similar to hypertension, there's a significant imbalance.\n",
" - Most patients in the dataset do not have heart disease (value 0).\n",
" - This imbalance also requires attention during model development.\n",
"\n",
"- **Smoking Status:**\n",
" - \"Never smoked\" is the most common category.\n",
" - There's a significant number of \"Unknown\" entries, which may require special handling.\n",
" - \"Formerly smoked\" and \"smokes\" categories have lower, but similar frequencies.\n",
" - The high number of \"Unknown\" entries could impact the analysis and may need imputation or special treatment.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"marker": {
"color": "#CC7B5C",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "ever_married",
"type": "bar",
"x": [
"Yes",
"No"
],
"xaxis": "x",
"y": [
3204,
1705
],
"yaxis": "y"
},
{
"marker": {
"color": "#D4A27F",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "work_type",
"type": "bar",
"x": [
"Private",
"Self-employed",
"children",
"Govt_job",
"Never_worked"
],
"xaxis": "x2",
"y": [
2811,
775,
671,
630,
22
],
"yaxis": "y2"
},
{
"marker": {
"color": "#EBDBBC",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "residence_type",
"type": "bar",
"x": [
"Urban",
"Rural"
],
"xaxis": "x3",
"y": [
2490,
2419
],
"yaxis": "y3"
},
{
"marker": {
"color": "#9C8AA5",
"line": {
"color": "#000000",
"width": 1
}
},
"name": "stroke",
"type": "bar",
"x": [
0,
1
],
"xaxis": "x4",
"y": [
4700,
209
],
"yaxis": "y4"
}
],
"layout": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"height": 500,
"margin": {
"b": 150,
"l": 50,
"r": 50,
"t": 80
},
"paper_bgcolor": "#EEECE2",
"plot_bgcolor": "#EEECE2",
"showlegend": false,
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"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": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"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": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"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": "white",
"showlakes": true,
"showland": true,
"subunitcolor": "#C8D4E3"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "white",
"polar": {
"angularaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
},
"bgcolor": "white",
"radialaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"yaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"zaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"baxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"bgcolor": "white",
"caxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
}
}
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene B",
"size": 20
},
"text": "Distribution of ever_married, work_type, residence_type, stroke",
"x": 0.5
},
"width": 1600,
"xaxis": {
"anchor": "y",
"domain": [
0,
0.175
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "ever_married"
}
},
"xaxis2": {
"anchor": "y2",
"domain": [
0.275,
0.45
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "work_type"
}
},
"xaxis3": {
"anchor": "y3",
"domain": [
0.55,
0.7250000000000001
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "residence_type"
}
},
"xaxis4": {
"anchor": "y4",
"domain": [
0.825,
1
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "stroke"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
},
"yaxis2": {
"anchor": "x2",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
},
"yaxis3": {
"anchor": "x3",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
},
"yaxis4": {
"anchor": "x4",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Count"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_combined_bar_charts(\n",
" stroke_df,\n",
" categorical_features_set2,\n",
" max_features_per_plot=4,\n",
" save_path=\"../images/categorical_distributions_set2\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/categorical_distributions_set2_chunk_1.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The bar plots reveal the following about `ever_married`, `work_type`, `residence_type` and `stroke`\n",
"\n",
"- **Ever Married:**\n",
"\n",
" - More married individuals (\"Yes\") than unmarried (\"No\") in the dataset.\n",
" - This could be correlated with age and might provide insights when analyzed together.\n",
"\n",
"- **Work Type:**\n",
"\n",
" - \"Private\" is the most common category, followed by \"Self-employed\".\n",
" - \"Govt_job\" and \"children\" categories have similar, lower frequencies.\n",
" - There are very few \"Never_worked\" entries.\n",
" - The \"children\" category might overlap with the younger age group, warranting further investigation.\n",
"\n",
"- **Residence Type:**\n",
"\n",
" - Nearly equal distribution between Urban and Rural residences.\n",
" - This balance is good for analyzing the impact of residence type on stroke risk without bias from uneven representation.\n",
"\n",
"- **Stroke:**\n",
" - The vast majority of individuals (about 4000) are in the \"0\" category, which represents no stroke.\n",
" - A much smaller number (less than 500) are in the \"1\" category, representing those who have had a stroke.\n",
" - This imbalance in the target variable will need to be addressed during model development.\n",
"\n",
"**Next, we can move on to checking the outliers in the numerical features.**\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"boxmean": "sd",
"marker": {
"color": "#CC7B5C",
"line": {
"color": "#000000",
"width": 1
}
},
"showlegend": false,
"type": "box",
"xaxis": "x",
"y": [
67,
80,
49,
79,
81,
74,
69,
78,
81,
61,
54,
79,
50,
64,
75,
60,
71,
52,
79,
82,
71,
80,
65,
69,
57,
42,
82,
80,
48,
82,
74,
72,
58,
49,
78,
54,
82,
60,
76,
58,
81,
39,
79,
77,
63,
82,
73,
54,
56,
80,
67,
45,
78,
70,
76,
59,
80,
67,
66,
63,
52,
80,
80,
79,
43,
59,
79,
68,
54,
61,
70,
47,
74,
79,
81,
57,
80,
45,
78,
70,
58,
57,
69,
64,
77,
74,
81,
57,
50,
54,
79,
53,
79,
80,
45,
68,
71,
61,
74,
38,
77,
58,
53,
80,
56,
72,
80,
82,
73,
78,
69,
68,
71,
76,
76,
76,
75,
82,
59,
74,
69,
72,
66,
78,
57,
70,
68,
80,
68,
55,
77,
50,
57,
71,
81,
78,
70,
78,
56,
79,
81,
60,
80,
72,
80,
78,
81,
46,
59,
32,
61,
63,
63,
82,
82,
65,
51,
59,
68,
72,
59,
70,
69,
74,
80,
77,
78,
54,
78,
81,
78,
78,
63,
39,
51,
63,
48,
81,
79,
78,
55,
73,
81,
79,
63,
81,
49,
57,
81,
39,
80,
81,
79,
73,
77,
78,
68,
57,
77,
51,
60,
66,
57,
68,
68,
57,
14,
75,
78,
3,
58,
8,
70,
14,
47,
52,
75,
32,
74,
79,
79,
37,
37,
40,
35,
20,
42,
44,
79,
65,
57,
49,
71,
59,
25,
67,
38,
54,
70,
27,
47,
58,
3,
58,
14,
32,
23,
55,
17,
59,
13,
4,
16,
67,
22,
45,
66,
58,
69,
58,
53,
78,
44,
66,
27,
43,
13,
51,
30,
46,
43,
79,
27,
38,
78,
57,
61,
37,
48,
29,
22,
11,
22,
53,
27,
76,
57,
8,
21,
18,
78,
78,
33,
52,
51,
11,
48,
82,
54,
49,
24,
37,
64,
66,
23,
68,
55,
25,
48,
79,
47,
20,
60,
32,
66,
43,
36,
52,
29,
39,
76,
0.64,
34,
72,
43,
57,
44,
4,
44,
18,
41,
23,
53,
56,
0.88,
25,
14,
53,
4,
39,
53,
5,
4,
37,
70,
24,
55,
70,
34,
26,
39,
38,
20,
17,
60,
75,
31,
58,
5,
13,
22,
52,
61,
78,
57,
21,
22,
80,
7,
64,
64,
38,
12,
33,
3,
37,
22,
24,
75,
12,
63,
52,
68,
5,
56,
43,
40,
53,
52,
31,
69,
64,
64,
62,
2,
53,
65,
22,
26,
53,
20,
63,
13,
21,
65,
65,
42,
72,
13,
29,
59,
42,
13,
75,
33,
43,
11,
7,
16,
44,
78,
25,
69,
61,
43,
38,
68,
26,
18,
43,
43,
32,
81,
25,
66,
11,
53,
78,
9,
48,
31,
20,
21,
42,
35,
53,
60,
74,
23,
55,
71,
5,
14,
15,
66,
56,
60,
57,
29,
76,
52,
42,
71,
64,
39,
46,
51,
31,
22,
41,
28,
44,
68,
49,
10,
31,
67,
70,
38,
5,
54,
30,
23,
31,
72,
23,
25,
51,
20,
12,
57,
65,
16,
43,
30,
29,
54,
57,
42,
18,
9,
26,
40,
77,
15,
76,
52,
55,
5,
55,
36,
46,
5,
39,
12,
67,
1.8,
72,
0.32,
64,
27,
25,
81,
32,
57,
52,
54,
36,
18,
45,
29,
51,
23,
54,
54,
18,
26,
72,
5,
65,
42,
47,
24,
57,
51,
48,
56,
30,
63,
46,
53,
1.08,
15,
59,
24,
53,
39,
23,
31,
5,
44,
1.8,
40,
7,
56,
42,
48,
82,
52,
42,
73,
55,
50,
43,
33,
80,
33,
74,
65,
40,
52,
66,
20,
51,
60,
59,
58,
42,
28,
57,
66,
56,
48,
37,
27,
15,
61,
80,
43,
53,
70,
58,
49,
65,
54,
74,
56,
79,
71,
28,
9,
4,
36,
78,
52,
80,
28,
38,
56,
75,
76,
10,
40,
82,
17,
58,
12,
53,
26,
48,
25,
27,
16,
20,
2,
1.8,
28,
32,
35,
52,
55,
42,
52,
18,
13,
45,
29,
12,
37,
30,
44,
79,
38,
45,
66,
19,
49,
66,
47,
59,
23,
79,
37,
78,
70,
34,
28,
29,
13,
22,
56,
81,
28,
9,
50,
70,
30,
51,
24,
17,
9,
50,
49,
6,
30,
73,
20,
42,
79,
55,
27,
71,
23,
71,
56,
31,
64,
46,
55,
2,
58,
78,
1.16,
35,
64,
18,
19,
47,
54,
39,
37,
17,
20,
31,
56,
31,
82,
33,
42,
20,
22,
61,
31,
31,
17,
30,
69,
63,
19,
7,
51,
38,
2,
58,
69,
64,
31,
59,
54,
24,
19,
17,
29,
42,
31,
29,
23,
25,
14,
66,
77,
79,
52,
41,
31,
39,
51,
40,
48,
55,
41,
39,
41,
5,
38,
40,
12,
20,
53,
21,
70,
61,
70,
38,
59,
36,
31,
65,
61,
2,
52,
33,
5,
32,
54,
51,
39,
40,
19,
9,
53,
23,
19,
1,
48,
71,
44,
53,
70,
1.4,
49,
17,
40,
46,
32,
2,
40,
19,
43,
46,
71,
31,
41,
65,
20,
51,
37,
49,
62,
28,
80,
45,
37,
3,
26,
58,
59,
74,
17,
62,
37,
19,
59,
59,
62,
80,
54,
27,
4,
81,
41,
27,
51,
59,
51,
71,
48,
49,
25,
31,
68,
39,
32,
39,
63,
69,
29,
1.72,
58,
32,
63,
57,
77,
8,
21,
16,
8,
71,
74,
7,
19,
64,
23,
75,
80,
40,
24,
74,
52,
54,
19,
23,
13,
51,
60,
79,
75,
69,
57,
47,
33,
51,
47,
15,
1,
20,
6,
50,
31,
15,
3,
30,
17,
24,
5,
33,
30,
66,
27,
21,
55,
19,
75,
71,
16,
49,
10,
37,
59,
7,
61,
34,
9,
46,
0.88,
50,
19,
21,
2,
37,
20,
50,
61,
48,
82,
64,
50,
1.4,
0.24,
55,
29,
4,
70,
35,
80,
54,
39,
26,
6,
41,
42,
9,
55,
71,
14,
77,
50,
49,
51,
79,
63,
66,
20,
37,
22,
60,
39,
53,
55,
63,
57,
82,
56,
41,
8,
34,
75,
57,
72,
21,
51,
15,
24,
30,
82,
62,
79,
19,
45,
5,
57,
31,
61,
27,
61,
53,
76,
57,
9,
34,
51,
61,
19,
50,
80,
13,
55,
67,
30,
67,
82,
5,
81,
48,
66,
38,
8,
47,
27,
53,
27,
36,
50,
32,
58,
73,
62,
50,
51,
19,
30,
45,
30,
28,
70,
0.32,
23,
18,
41,
52,
77,
34,
67,
1.64,
59,
62,
47,
22,
33,
27,
44,
18,
47,
66,
42,
56,
69,
65,
53,
36,
69,
19,
3,
15,
59,
61,
28,
52,
33,
80,
47,
2,
48,
1.56,
26,
62,
1.72,
38,
28,
67,
57,
40,
10,
55,
7,
56,
37,
69,
45,
28,
52,
46,
45,
21,
4,
45,
71,
46,
3,
79,
71,
74,
52,
49,
32,
62,
78,
48,
18,
80,
34,
40,
59,
11,
35,
29,
23,
59,
44,
62,
32,
2,
79,
2,
43,
43,
62,
73,
31,
42,
20,
55,
23,
33,
14,
79,
64,
76,
46,
80,
21,
33,
32,
0.72,
49,
41,
37,
58,
79,
23,
78,
41,
68,
30,
1.88,
32,
20,
20,
10,
8,
65,
65,
46,
53,
79,
43,
56,
3,
32,
41,
53,
67,
33,
21,
26,
69,
15,
3,
1.24,
80,
2,
50,
30,
15,
71,
4,
2,
11,
45,
22,
63,
50,
25,
49,
39,
43,
67,
48,
38,
14,
63,
37,
20,
60,
13,
34,
22,
46,
53,
28,
62,
11,
66,
80,
8,
53,
63,
31,
43,
79,
8,
38,
80,
49,
50,
13,
73,
60,
44,
79,
5,
51,
5,
65,
81,
46,
44,
25,
49,
80,
22,
61,
3,
50,
45,
55,
21,
0.8,
50,
73,
58,
30,
55,
57,
77,
29,
46,
40,
29,
37,
35,
54,
56,
26,
75,
44,
63,
17,
46,
47,
50,
76,
47,
47,
49,
71,
65,
40,
82,
64,
50,
40,
74,
30,
68,
6,
4,
60,
59,
72,
62,
13,
72,
1.08,
35,
54,
40,
3,
2,
41,
61,
58,
17,
64,
32,
42,
39,
10,
78,
68,
24,
59,
45,
12,
37,
43,
12,
13,
74,
2,
59,
21,
55,
14,
53,
75,
37,
49,
55,
57,
45,
46,
12,
12,
77,
6,
76,
52,
62,
82,
69,
78,
37,
50,
41,
54,
43,
37,
9,
11,
56,
5,
17,
6,
57,
18,
2,
76,
1,
54,
45,
54,
67,
53,
47,
44,
65,
40,
3,
81,
24,
9,
52,
63,
78,
22,
2,
79,
49,
74,
62,
16,
62,
50,
49,
24,
21,
17,
22,
13,
21,
8,
16,
18,
68,
49,
80,
31,
25,
39,
35,
71,
71,
40,
41,
2,
3,
53,
1.88,
60,
5,
30,
49,
68,
23,
2,
24,
41,
63,
64,
38,
1.24,
38,
43,
27,
42,
46,
67,
34,
51,
77,
30,
81,
12,
18,
35,
82,
53,
75,
32,
27,
56,
71,
78,
44,
62,
27,
42,
66,
82,
49,
77,
59,
54,
38,
55,
13,
45,
54,
51,
47,
25,
61,
55,
23,
59,
4,
65,
50,
42,
51,
66,
22,
5,
19,
47,
12,
53,
26,
62,
81,
63,
25,
8,
76,
44,
65,
54,
24,
65,
12,
33,
74,
18,
77,
48,
52,
23,
25,
44,
78,
37,
32,
67,
41,
2,
75,
17,
13,
25,
57,
31,
61,
46,
15,
47,
68,
0.4,
40,
62,
45,
47,
35,
51,
60,
59,
1.24,
18,
81,
15,
73,
0.08,
53,
45,
70,
56,
7,
66,
53,
20,
15,
51,
34,
33,
53,
69,
28,
81,
20,
1.48,
2,
77,
38,
23,
67,
59,
41,
21,
41,
54,
25,
6,
21,
32,
64,
70,
44,
60,
56,
53,
59,
53,
63,
42,
63,
52,
62,
68,
36,
3,
40,
8,
65,
28,
40,
65,
58,
44,
54,
79,
62,
37,
63,
27,
6,
30,
15,
50,
32,
33,
62,
82,
71,
20,
44,
57,
50,
50,
44,
47,
35,
8,
3,
29,
36,
19,
41,
23,
14,
35,
45,
52,
19,
21,
8,
35,
33,
53,
31,
43,
61,
57,
11,
36,
16,
60,
13,
69,
22,
63,
45,
58,
61,
30,
78,
68,
16,
38,
39,
51,
37,
16,
39,
24,
51,
50,
27,
73,
65,
76,
53,
65,
31,
1.72,
45,
56,
62,
35,
16,
31,
5,
26,
10,
22,
82,
50,
47,
31,
22,
12,
63,
78,
4,
76,
81,
39,
59,
2,
37,
78,
75,
34,
48,
13,
54,
13,
24,
76,
1,
57,
46,
26,
56,
34,
25,
60,
22,
35,
48,
38,
12,
22,
0.64,
13,
78,
23,
35,
78,
50,
61,
43,
60,
51,
21,
16,
25,
36,
8,
48,
14,
60,
61,
60,
56,
9,
45,
14,
37,
52,
47,
82,
64,
12,
79,
77,
25,
26,
14,
42,
37,
8,
78,
18,
78,
55,
68,
47,
47,
51,
43,
78,
63,
45,
53,
12,
56,
29,
55,
49,
23,
53,
56,
56,
61,
45,
80,
47,
3,
63,
68,
76,
62,
30,
44,
47,
25,
31,
71,
32,
72,
44,
63,
37,
54,
56,
25,
19,
62,
27,
5,
20,
21,
44,
39,
38,
26,
51,
39,
5,
59,
45,
43,
80,
42,
8,
68,
5,
57,
44,
16,
60,
58,
43,
50,
1.48,
5,
9,
67,
21,
36,
70,
57,
52,
28,
41,
81,
33,
31,
42,
10,
34,
70,
33,
58,
25,
1.4,
47,
14,
82,
52,
72,
52,
61,
15,
1.56,
6,
3,
18,
53,
58,
31,
29,
5,
40,
75,
52,
39,
40,
78,
39,
17,
45,
0.56,
13,
26,
42,
44,
3,
25,
41,
51,
20,
25,
18,
37,
51,
2,
38,
64,
22,
71,
32,
32,
63,
0.24,
54,
25,
80,
31,
53,
35,
31,
60,
0.56,
21,
78,
59,
0.64,
10,
60,
11,
48,
50,
69,
20,
22,
55,
57,
29,
32,
54,
37,
58,
41,
72,
32,
54,
79,
56,
45,
6,
45,
60,
65,
57,
58,
8,
18,
49,
2,
52,
63,
57,
50,
12,
35,
35,
64,
42,
62,
3,
43,
52,
64,
59,
81,
15,
50,
73,
19,
30,
40,
47,
66,
47,
41,
63,
12,
1.32,
46,
8,
74,
74,
4,
26,
63,
48,
24,
47,
70,
41,
42,
57,
51,
15,
11,
27,
53,
52,
68,
65,
23,
43,
55,
52,
21,
78,
49,
27,
52,
22,
77,
57,
13,
14,
29,
49,
1.24,
21,
36,
42,
56,
46,
79,
25,
67,
37,
44,
36,
17,
50,
53,
39,
71,
18,
55,
52,
27,
24,
72,
41,
29,
26,
23,
43,
43,
49,
13,
26,
10,
39,
41,
25,
10,
54,
7,
53,
55,
6,
72,
42,
50,
55,
47,
3,
49,
12,
28,
40,
55,
9,
77,
18,
73,
67,
58,
2,
38,
5,
34,
78,
57,
17,
71,
19,
56,
64,
13,
40,
80,
62,
79,
9,
26,
1.72,
16,
40,
46,
45,
59,
31,
69,
73,
56,
80,
60,
40,
30,
66,
78,
37,
41,
34,
18,
69,
28,
3,
57,
34,
50,
55,
2,
27,
57,
73,
52,
65,
48,
54,
8,
45,
48,
77,
34,
33,
39,
39,
40,
46,
1,
5,
14,
49,
45,
13,
76,
14,
20,
9,
78,
49,
55,
43,
16,
58,
67,
71,
8,
18,
47,
59,
28,
67,
71,
56,
44,
6,
78,
32,
40,
63,
51,
22,
82,
17,
34,
42,
32,
73,
47,
34,
64,
52,
55,
64,
23,
18,
65,
12,
61,
66,
59,
47,
47,
71,
62,
28,
7,
68,
37,
75,
5,
1.08,
34,
49,
8,
50,
2,
1.16,
76,
18,
82,
67,
27,
57,
51,
68,
56,
18,
63,
31,
37,
64,
47,
1.8,
42,
37,
69,
13,
73,
48,
78,
21,
54,
19,
82,
80,
21,
46,
14,
6,
3,
47,
34,
41,
67,
5,
20,
63,
73,
62,
0.88,
50,
71,
30,
80,
64,
7,
36,
79,
35,
28,
72,
12,
25,
27,
62,
58,
30,
56,
28,
47,
16,
29,
26,
26,
55,
29,
29,
33,
19,
10,
3,
60,
16,
35,
41,
19,
79,
41,
1.64,
51,
33,
33,
48,
45,
61,
31,
60,
51,
53,
52,
42,
56,
14,
40,
58,
52,
30,
47,
34,
82,
31,
58,
47,
65,
13,
37,
9,
7,
37,
62,
14,
77,
36,
3,
78,
49,
44,
10,
8,
60,
56,
73,
65,
34,
13,
80,
46,
80,
65,
24,
33,
11,
57,
19,
82,
39,
14,
60,
3,
57,
79,
65,
67,
60,
36,
36,
26,
71,
78,
77,
74,
50,
9,
1.64,
54,
8,
36,
38,
7,
5,
14,
0.88,
30,
39,
17,
14,
52,
78,
2,
32,
0.88,
50,
48,
66,
46,
54,
81,
15,
77,
59,
48,
49,
44,
39,
34,
50,
62,
38,
43,
28,
58,
28,
18,
31,
55,
54,
73,
26,
73,
38,
75,
81,
20,
12,
78,
42,
16,
4,
60,
45,
23,
21,
67,
10,
24,
50,
14,
13,
71,
66,
32,
46,
48,
45,
55,
31,
25,
73,
14,
56,
60,
27,
28,
46,
11,
63,
59,
57,
35,
54,
7,
48,
69,
59,
72,
49,
73,
24,
26,
27,
78,
77,
59,
0.32,
39,
35,
75,
80,
35,
22,
39,
6,
62,
13,
56,
53,
5,
44,
13,
59,
35,
52,
76,
51,
79,
69,
78,
60,
10,
42,
21,
56,
59,
69,
25,
63,
25,
58,
28,
46,
53,
37,
12,
14,
60,
42,
65,
80,
32,
53,
50,
23,
45,
0.8,
49,
70,
82,
23,
79,
74,
78,
50,
34,
47,
52,
67,
62,
18,
75,
42,
29,
52,
18,
67,
66,
5,
18,
52,
44,
46,
36,
61,
7,
15,
61,
20,
2,
47,
25,
66,
2,
63,
53,
33,
23,
23,
81,
55,
69,
58,
70,
48,
15,
31,
2,
80,
69,
52,
80,
54,
2,
39,
38,
76,
45,
1.24,
77,
46,
50,
52,
41,
40,
59,
48,
42,
26,
39,
34,
54,
57,
1.56,
50,
64,
44,
38,
32,
17,
81,
15,
5,
70,
52,
67,
17,
61,
31,
58,
12,
30,
65,
23,
14,
27,
53,
58,
9,
59,
46,
41,
55,
43,
15,
36,
36,
47,
18,
19,
65,
17,
54,
46,
59,
48,
18,
56,
3,
41,
67,
60,
47,
24,
43,
17,
45,
24,
79,
79,
27,
64,
34,
50,
58,
26,
50,
80,
2,
46,
29,
6,
32,
16,
44,
23,
48,
34,
78,
42,
40,
10,
39,
42,
47,
14,
45,
58,
73,
62,
62,
51,
0.8,
57,
8,
69,
16,
56,
41,
1.24,
42,
57,
20,
8,
43,
62,
42,
63,
80,
13,
20,
71,
50,
76,
41,
76,
78,
5,
43,
49,
70,
24,
41,
20,
34,
32,
27,
44,
20,
52,
57,
29,
16,
35,
5,
63,
59,
63,
52,
50,
43,
27,
30,
8,
14,
23,
6,
37,
38,
3,
26,
58,
57,
58,
76,
79,
34,
75,
11,
71,
40,
24,
0.64,
82,
32,
81,
79,
62,
39,
60,
48,
24,
70,
17,
56,
3,
65,
72,
10,
29,
44,
46,
33,
0.24,
55,
56,
50,
78,
63,
31,
65,
51,
60,
69,
23,
46,
16,
26,
44,
23,
38,
18,
63,
23,
32,
8,
77,
41,
34,
25,
35,
15,
1.64,
4,
33,
28,
37,
50,
76,
72,
16,
44,
46,
46,
73,
29,
19,
27,
36,
61,
9,
25,
68,
26,
17,
75,
34,
53,
18,
48,
5,
79,
16,
32,
55,
70,
51,
39,
79,
80,
34,
59,
54,
22,
21,
4,
26,
62,
33,
51,
75,
42,
82,
61,
62,
59,
31,
12,
76,
71,
15,
44,
30,
47,
47,
60,
62,
47,
78,
35,
57,
12,
26,
33,
60,
35,
10,
50,
44,
41,
7,
45,
60,
49,
45,
55,
54,
39,
76,
28,
70,
28,
79,
30,
26,
57,
16,
40,
77,
36,
40,
2,
52,
78,
22,
33,
40,
20,
19,
25,
23,
28,
6,
37,
10,
34,
60,
22,
52,
61,
10,
36,
29,
69,
62,
62,
79,
13,
49,
41,
56,
8,
24,
61,
40,
22,
80,
60,
57,
55,
39,
51,
77,
32,
45,
49,
13,
74,
72,
57,
51,
55,
73,
12,
6,
76,
38,
50,
75,
1.16,
32,
12,
32,
13,
1.32,
63,
29,
53,
49,
81,
66,
61,
52,
8,
82,
43,
4,
45,
62,
42,
72,
26,
9,
31,
20,
22,
23,
57,
52,
64,
26,
54,
32,
66,
13,
77,
18,
11,
16,
73,
36,
10,
54,
42,
52,
32,
43,
28,
79,
11,
75,
55,
78,
48,
73,
56,
54,
55,
65,
69,
29,
64,
16,
75,
17,
24,
50,
52,
82,
56,
56,
18,
33,
57,
30,
52,
3,
44,
33,
24,
24,
54,
72,
37,
36,
22,
68,
24,
35,
32,
52,
21,
82,
33,
62,
57,
14,
63,
50,
12,
80,
74,
46,
66,
26,
70,
62,
15,
67,
63,
42,
49,
78,
61,
41,
73,
64,
65,
81,
47,
40,
46,
2,
56,
54,
26,
45,
57,
78,
2,
76,
51,
54,
36,
57,
25,
7,
76,
45,
66,
45,
79,
25,
53,
35,
78,
32,
0.56,
26,
34,
65,
15,
17,
71,
42,
30,
75,
9,
47,
75,
61,
61,
27,
55,
2,
51,
45,
36,
46,
48,
79,
50,
79,
32,
36,
27,
22,
5,
9,
31,
26,
43,
33,
34,
66,
44,
56,
67,
18,
55,
61,
0.08,
48,
44,
68,
52,
15,
2,
60,
37,
5,
25,
52,
79,
61,
36,
34,
2,
43,
1.48,
49,
2,
82,
81,
81,
1.08,
60,
1.88,
58,
8,
9,
34,
23,
74,
72,
47,
56,
5,
27,
38,
13,
41,
50,
41,
6,
62,
4,
59,
63,
9,
41,
72,
45,
80,
19,
37,
58,
77,
38,
49,
20,
23,
55,
16,
81,
23,
82,
48,
56,
61,
43,
39,
22,
41,
37,
65,
8,
51,
63,
48,
17,
75,
51,
51,
41,
75,
57,
72,
52,
11,
28,
78,
82,
74,
31,
53,
0.24,
67,
17,
29,
56,
15,
29,
79,
82,
17,
8,
23,
36,
47,
24,
60,
63,
44,
38,
26,
18,
18,
13,
13,
47,
80,
24,
72,
5,
51,
82,
5,
66,
15,
53,
78,
62,
41,
41,
78,
14,
1.48,
71,
32,
16,
68,
0.56,
59,
32,
58,
40,
41,
21,
1.88,
55,
67,
49,
42,
54,
25,
62,
75,
72,
14,
38,
59,
23,
63,
82,
34,
8,
28,
49,
24,
17,
69,
58,
50,
5,
34,
64,
47,
61,
14,
57,
53,
67,
57,
38,
13,
37,
39,
13,
57,
11,
68,
79,
24,
44,
46,
66,
61,
16,
37,
43,
27,
44,
14,
81,
54,
77,
27,
35,
30,
48,
15,
26,
38,
29,
49,
55,
52,
49,
21,
24,
18,
52,
70,
51,
58,
46,
79,
11,
61,
51,
21,
71,
13,
16,
61,
49,
65,
40,
44,
11,
20,
24,
44,
5,
41,
23,
7,
74,
61,
80,
35,
43,
25,
72,
45,
9,
33,
53,
67,
59,
1.32,
27,
64,
55,
40,
33,
33,
39,
74,
38,
53,
22,
5,
24,
27,
41,
20,
71,
65,
38,
25,
53,
50,
1.24,
55,
38,
72,
43,
30,
69,
61,
34,
43,
62,
48,
37,
64,
51,
69,
75,
27,
49,
82,
36,
45,
64,
32,
15,
1.64,
34,
17,
76,
61,
30,
18,
48,
0.16,
4,
38,
27,
50,
19,
8,
78,
0.8,
53,
73,
58,
62,
78,
51,
78,
40,
35,
82,
46,
51,
53,
55,
43,
81,
12,
20,
53,
1.32,
22,
75,
66,
7,
26,
14,
32,
6,
42,
4,
75,
80,
52,
11,
14,
74,
58,
80,
31,
28,
25,
71,
51,
15,
45,
53,
69,
42,
4,
64,
5,
44,
78,
69,
80,
7,
2,
80,
69,
58,
75,
60,
75,
10,
38,
28,
3,
31,
59,
31,
58,
57,
31,
43,
40,
34,
55,
39,
42,
44,
69,
16,
8,
1.48,
1.88,
69,
13,
53,
16,
64,
7,
41,
65,
61,
63,
54,
14,
45,
51,
8,
52,
39,
69,
71,
73,
54,
10,
26,
41,
71,
46,
15,
29,
8,
21,
56,
14,
78,
36,
57,
79,
26,
22,
72,
54,
8,
62,
28,
50,
7,
33,
55,
25,
25,
37,
58,
45,
60,
66,
80,
38,
11,
63,
19,
17,
19,
40,
49,
69,
46,
78,
63,
3,
1.8,
18,
46,
8,
53,
38,
74,
24,
78,
60,
12,
32,
5,
40,
19,
28,
44,
50,
50,
18,
1.64,
5,
39,
65,
26,
42,
34,
45,
43,
40,
35,
2,
61,
64,
32,
23,
51,
52,
75,
40,
39,
6,
32,
55,
23,
52,
58,
17,
3,
23,
56,
45,
14,
45,
57,
35,
8,
42,
8,
62,
43,
8,
40,
2,
27,
47,
53,
62,
50,
26,
19,
0.32,
45,
45,
54,
64,
2,
66,
73,
31,
1.88,
58,
12,
60,
23,
26,
36,
3,
21,
67,
35,
26,
47,
81,
1.64,
52,
28,
45,
20,
1.56,
65,
45,
77,
37,
50,
0.56,
30,
31,
4,
65,
68,
63,
46,
54,
66,
29,
78,
56,
57,
2,
49,
31,
47,
37,
65,
61,
66,
24,
66,
64,
38,
7,
63,
71,
18,
65,
57,
62,
1.8,
35,
50,
73,
23,
32,
38,
50,
39,
7,
15,
67,
32,
5,
56,
49,
11,
61,
76,
34,
20,
24,
39,
55,
16,
36,
38,
20,
38,
52,
49,
80,
75,
13,
54,
28,
61,
0.16,
59,
12,
38,
15,
31,
52,
61,
50,
41,
20,
67,
45,
73,
52,
51,
56,
49,
10,
51,
57,
76,
28,
57,
39,
41,
72,
45,
33,
62,
27,
31,
24,
1.88,
28,
24,
30,
38,
1.64,
0.72,
57,
27,
30,
42,
36,
44,
59,
61,
18,
44,
16,
13,
9,
0.16,
81,
38,
58,
5,
64,
38,
56,
58,
9,
34,
79,
62,
60,
42,
59,
54,
59,
15,
14,
5,
17,
13,
48,
37,
74,
54,
64,
26,
55,
74,
0.48,
41,
68,
9,
36,
17,
24,
69,
38,
69,
32,
25,
2,
79,
28,
16,
66,
57,
61,
74,
70,
8,
49,
30,
51,
10,
34,
40,
3,
45,
4,
52,
68,
8,
82,
36,
42,
27,
55,
31,
82,
57,
78,
55,
51,
80,
17,
61,
62,
14,
62,
56,
41,
59,
29,
39,
66,
35,
36,
35,
26,
31,
56,
36,
41,
50,
42,
79,
57,
68,
25,
69,
42,
37,
2,
78,
36,
10,
39,
4,
48,
72,
63,
28,
80,
49,
57,
59,
43,
47,
54,
57,
56,
66,
18,
81,
30,
8,
55,
79,
49,
53,
26,
14,
25,
20,
18,
38,
72,
52,
71,
46,
33,
37,
54,
5,
45,
14,
5,
35,
20,
51,
40,
79,
61,
33,
27,
28,
67,
35,
78,
20,
43,
27,
45,
11,
31,
30,
27,
34,
19,
3,
53,
81,
44,
59,
43,
5,
31,
47,
12,
40,
49,
38,
15,
81,
48,
24,
47,
13,
16,
64,
11,
55,
38,
37,
34,
52,
50,
37,
55,
2,
68,
27,
53,
5,
3,
20,
19,
31,
63,
49,
72,
50,
41,
40,
17,
3,
33,
12,
23,
31,
65,
43,
22,
7,
81,
10,
5,
24,
45,
46,
64,
25,
12,
81,
65,
40,
81,
36,
32,
31,
55,
39,
17,
31,
36,
5,
29,
2,
62,
45,
18,
29,
52,
69,
71,
33,
43,
39,
60,
54,
61,
0.24,
11,
75,
64,
78,
14,
66,
48,
44,
30,
1.48,
61,
54,
22,
47,
67,
53,
45,
65,
39,
57,
45,
28,
30,
31,
70,
17,
44,
65,
6,
15,
80,
54,
79,
60,
12,
36,
54,
63,
2,
28,
38,
37,
80,
43,
60,
66,
45,
29,
4,
48,
68,
57,
29,
30,
47,
73,
66,
39,
66,
52,
34,
79,
39,
61,
26,
37,
66,
16,
51,
15,
1.72,
60,
66,
75,
40,
52,
13,
44,
37,
2,
48,
81,
76,
58,
11,
9,
54,
37,
16,
11,
32,
26,
21,
45,
40,
41,
71,
16,
7,
27,
22,
79,
38,
64,
4,
23,
35,
66,
34,
19,
73,
70,
45,
34,
81,
0.72,
13,
57,
38,
21,
38,
41,
26,
48,
18,
66,
27,
18,
11,
7,
54,
43,
61,
31,
40,
71,
57,
13,
21,
63,
7,
78,
66,
41,
11,
55,
42,
51,
79,
28,
73,
42,
37,
78,
74,
59,
82,
71,
19,
18,
53,
62,
3,
49,
51,
53,
10,
54,
40,
3,
12,
56,
43,
28,
28,
56,
14,
63,
29,
48,
47,
63,
40,
40,
55,
43,
59,
78,
27,
49,
14,
17,
42,
36,
19,
55,
8,
56,
54,
15,
43,
52,
62,
54,
60,
7,
3,
39,
60,
60,
46,
26,
12,
16,
2,
82,
32,
62,
75,
81,
37,
28,
25,
44,
11,
54,
43,
56,
37,
60,
4,
51,
82,
56,
60,
35,
32,
45,
10,
68,
51,
8,
63,
4,
33,
56,
49,
30,
16,
1.8,
54,
49,
17,
11,
51,
74,
42,
69,
22,
25,
17,
64,
50,
52,
37,
31,
52,
56,
43,
11,
2,
16,
23,
40,
15,
48,
33,
46,
53,
68,
60,
57,
51,
0.4,
76,
46,
23,
9,
53,
4,
62,
37,
82,
33,
3,
14,
16,
40,
18,
29,
56,
33,
2,
36,
30,
31,
16,
58,
19,
47,
59,
40,
26,
17,
30,
19,
78,
55,
57,
33,
35,
32,
55,
28,
25,
45,
34,
33,
65,
62,
36,
31,
54,
53,
44,
77,
67,
48,
42,
72,
49,
1.32,
45,
63,
33,
32,
0.48,
63,
70,
57,
8,
54,
37,
59,
78,
59,
10,
21,
2,
55,
20,
38,
33,
14,
32,
32,
68,
70,
24,
44,
39,
81,
19,
69,
42,
8,
8,
28,
66,
66,
47,
78,
65,
78,
68,
65,
70,
59,
31,
32,
5,
55,
61,
48,
1.16,
50,
63,
67,
43,
59,
47,
50,
51,
44,
39,
24,
62,
61,
78,
15,
23,
79,
57,
9,
55,
82,
19,
41,
79,
23,
77,
78,
24,
81,
46,
29,
59,
41,
17,
8,
23,
45,
67,
78,
18,
35,
54,
71,
24,
60,
75,
48,
62,
20,
69,
60,
12,
28,
55,
51,
16,
56,
52,
59,
9,
34,
47,
46,
49,
10,
76,
82,
3,
76,
16,
44,
17,
73,
31,
17,
18,
73,
8,
42,
40,
79,
53,
58,
48,
17,
23,
9,
81,
16,
21,
29,
8,
39,
64,
9,
17,
8,
62,
73,
48,
17,
10,
74,
62,
5,
43,
39,
37,
15,
36,
34,
75,
53,
61,
45,
17,
17,
19,
54,
51,
44,
82,
49,
34,
63,
79,
81,
14,
31,
57,
32,
66,
22,
1.32,
20,
41,
48,
49,
73,
19,
51,
1.08,
31,
4,
1.8,
44,
4,
55,
34,
30,
7,
75,
14,
61,
55,
14,
36,
57,
20,
32,
43,
43,
1.08,
49,
53,
18,
13,
5,
37,
49,
45,
50,
12,
12,
5,
39,
41,
41,
47,
57,
16,
3,
65,
80,
58,
74,
62,
33,
21,
17,
49,
24,
56,
26,
54,
42,
34,
56,
39,
77,
12,
65,
7,
25,
31,
14,
5,
60,
53,
55,
69,
39,
0.32,
34,
80,
20,
45,
41,
54,
23,
75,
36,
75,
47,
15,
40,
71,
38,
10,
11,
0.72,
9,
53,
28,
81,
16,
24,
62,
62,
59,
19,
51,
38,
26,
13,
4,
55,
44,
31,
17,
23,
51,
46,
81,
38,
50,
34,
60,
50,
49,
2,
47,
34,
22,
11,
54,
60,
3,
46,
30,
63,
50,
65,
78,
36,
18,
44,
4,
60,
19,
15,
20,
76,
39,
58,
64,
79,
11,
4,
43,
44,
37,
34,
59,
40,
8,
24,
18,
20,
70,
68,
17,
17,
21,
38,
59,
79,
50,
72,
54,
44,
48,
29,
44,
61,
58,
81,
29,
5,
35,
40,
37,
33,
35,
38,
26,
10,
72,
21,
20,
1.24,
26,
79,
78,
56,
46,
55,
30,
53,
32,
38,
33,
44,
44,
51,
41,
5,
42,
6,
46,
47,
25,
40,
49,
21,
61,
78,
41,
82,
39,
70,
31,
37,
21,
80,
21,
5,
81,
35,
24,
52,
70,
34,
17,
8,
1.72,
29,
55,
44,
19,
67,
72,
51,
63,
64,
0.72,
26,
59,
76,
13,
1.08,
57,
68,
9,
82,
45,
57,
18,
13,
81,
35,
51,
44
],
"yaxis": "y"
},
{
"boxmean": "sd",
"marker": {
"color": "#D4A27F",
"line": {
"color": "#000000",
"width": 1
}
},
"showlegend": false,
"type": "box",
"xaxis": "x2",
"y": [
228.69,
105.92,
171.23,
174.12,
186.21,
70.09,
94.39,
58.57,
80.43,
120.46,
104.51,
214.09,
167.41,
191.61,
221.29,
89.22,
193.94,
233.29,
228.7,
208.3,
102.87,
104.12,
100.98,
195.23,
212.08,
83.41,
196.92,
252.72,
84.2,
84.03,
219.72,
74.63,
92.62,
60.91,
78.03,
71.22,
144.9,
213.03,
243.58,
107.26,
99.33,
58.09,
127.29,
124.13,
196.71,
59.32,
194.99,
180.93,
185.17,
74.9,
61.94,
93.72,
113.01,
221.58,
104.47,
86.23,
72.67,
179.12,
116.55,
228.56,
96.59,
66.72,
240.09,
110.85,
143.43,
96.16,
88.92,
79.79,
96.97,
111.81,
59.35,
86.94,
98.55,
226.98,
72.81,
68.02,
68.56,
64.14,
235.63,
76.34,
240.59,
78.92,
82.81,
74.1,
190.32,
231.61,
78.7,
110.52,
73.18,
191.82,
93.05,
64.17,
129.98,
68.53,
224.1,
82.1,
216.94,
76.11,
72.96,
82.28,
105.22,
59.86,
62.55,
259.63,
249.31,
131.41,
73.54,
200.59,
190.14,
130.54,
182.99,
206.09,
263.32,
140.1,
207.28,
194.37,
199.2,
103.68,
116.44,
70.28,
72.17,
221.79,
151.16,
67.29,
67.41,
239.07,
223.83,
76.57,
77.82,
92.98,
231.56,
102.16,
221.89,
195.71,
74.02,
203.87,
89.13,
133.19,
162.23,
98.02,
91.54,
97.43,
91.02,
185.49,
64.44,
56.11,
213.22,
78.18,
111.04,
76.13,
112.24,
137.3,
116.69,
215.94,
86.62,
205.77,
112.16,
118.03,
271.74,
97.92,
200.62,
242.52,
93.81,
74.96,
175.29,
90,
109.47,
87.85,
106.74,
80.13,
90.19,
134.8,
106.58,
83.24,
103.43,
208.65,
74.11,
95.84,
205.33,
116.1,
210.4,
219.73,
250.89,
72.73,
205.35,
70.3,
104.86,
216.58,
184.4,
97.76,
66.03,
81.95,
97.73,
70.94,
199.84,
218.46,
211.06,
86.3,
162.14,
88.2,
91.92,
76.46,
197.28,
233.94,
247.51,
84.96,
57.93,
78.8,
78.81,
95.12,
87.96,
110.89,
69.04,
161.28,
210.95,
77.59,
243.53,
77.67,
205.84,
77.08,
57.08,
162.96,
73.5,
95.04,
85.37,
84.62,
82.67,
57.33,
67.84,
75.7,
129.54,
60.22,
198.21,
109.82,
60.84,
94.61,
97.49,
206.72,
214.45,
82.9,
103.26,
55.78,
73.74,
149.75,
82.34,
62.6,
94.09,
55.42,
82.18,
117.92,
114.84,
79.17,
110.63,
190.7,
79.81,
113.63,
141.24,
56.96,
203.04,
94.3,
81.51,
137.74,
81.84,
242.3,
102.64,
146.01,
75.42,
220.49,
61.45,
88.19,
115.22,
97.93,
85.6,
79.83,
218.46,
62.2,
69.15,
127.71,
216.7,
71.89,
103.56,
79.03,
69.94,
90.12,
95.1,
234.58,
235.85,
78.05,
89.44,
111.38,
243.5,
182.2,
229.92,
82.24,
89.84,
90.69,
84.38,
215.6,
91.61,
138.16,
75.23,
75.18,
239.64,
58.95,
99.92,
116.23,
118.82,
66.3,
101.41,
200.28,
136.8,
96.57,
66.42,
98.09,
205.23,
86.78,
126.82,
78.4,
63.69,
89.86,
110.99,
83.82,
137.96,
210.78,
118.89,
72.55,
107.41,
93.25,
74.91,
80.05,
99.8,
91.97,
64.4,
73.02,
88.11,
138.29,
72.36,
68.76,
106.22,
101.52,
109.51,
85.84,
110.15,
91.72,
251.6,
123.1,
97.4,
68.34,
80.81,
120.31,
83.51,
74.09,
91.6,
87.52,
213.37,
75.39,
122.41,
223.36,
84.59,
70.16,
107.52,
80.88,
93.48,
70.21,
86.67,
101.37,
105.22,
70.31,
64.06,
178.29,
81.6,
147.48,
86.86,
71.16,
73.2,
203.81,
82,
97.55,
94.29,
75.22,
90.07,
73.73,
79.58,
77.88,
205.26,
58.63,
139.9,
211.03,
72.79,
91.65,
225.47,
94.48,
82.34,
180.63,
92.48,
116.66,
112.09,
130.34,
82.59,
123.87,
98.7,
227.1,
55.39,
66.07,
104.21,
145.15,
68.19,
97.53,
122.31,
83.51,
86.46,
112.06,
92.14,
201.76,
74.44,
95.93,
87.51,
72.35,
113.47,
103.78,
115.43,
95.59,
101.52,
170.05,
217.3,
196.01,
92.21,
100.09,
93.88,
75.05,
70.08,
74.2,
69.01,
96.17,
87.24,
82.58,
96.88,
73.32,
94.59,
132.08,
98.99,
104.48,
98.01,
79.14,
89.32,
65.24,
80.98,
186.17,
69.24,
111.19,
93.28,
122.19,
129.53,
114.53,
93.73,
77.49,
65.38,
94.18,
118.7,
96.29,
74.64,
96.99,
100.61,
97.08,
84.79,
78.75,
82.93,
88.78,
122.1,
74.81,
183.45,
114.94,
104.38,
102.91,
108.79,
73.31,
68.52,
102.64,
70.92,
86.11,
129.16,
113.85,
79.35,
80.79,
114.01,
93.74,
90.65,
98.41,
127.18,
138.06,
89.44,
105.61,
155.43,
80.83,
80.19,
60.34,
79.3,
98.44,
210.48,
70.34,
122.22,
75.29,
100.35,
193.83,
126.96,
183.34,
247.69,
76.2,
71.43,
93.36,
61.29,
103.62,
100.52,
191.47,
67.06,
144.1,
58.26,
239.82,
73.71,
88.53,
114.79,
113.8,
71.91,
65.3,
189.57,
114.25,
81.26,
87.88,
112.17,
99.97,
207.58,
109.16,
74.46,
118.51,
107.47,
182.86,
59.17,
215.64,
163.7,
196.36,
139.77,
141.23,
89.99,
108.53,
63.61,
85.54,
100.83,
59.82,
105.95,
188.11,
71.15,
60.53,
205.5,
204.86,
115.03,
79.87,
97.89,
125.26,
114.32,
92.23,
83.51,
102.34,
144.48,
88.39,
228.08,
93.79,
147.14,
106.43,
88.04,
92.2,
219.53,
75.56,
158.31,
111.43,
219.97,
148.91,
69.4,
68.34,
58.37,
71.2,
101.5,
92.04,
73,
111.15,
58.65,
100.54,
111.73,
97.78,
85.79,
98.54,
94.62,
112.43,
99.67,
67.39,
95.12,
85.06,
214.05,
79.09,
98.09,
76.36,
98.42,
82.01,
56.11,
111.85,
92.39,
112.54,
63.71,
70.35,
94.65,
64.64,
57.27,
107.25,
77.12,
55.32,
69.37,
57.57,
87.43,
98.73,
81.18,
56.23,
69.61,
59.49,
100.26,
56.75,
67.81,
77.46,
76.26,
86.39,
116.38,
75.74,
92.14,
149.95,
74.98,
55.25,
79.89,
130.61,
94.26,
94.34,
109.03,
81.03,
99.82,
140.08,
59.54,
80.33,
131.51,
89.21,
91.45,
90.58,
91.45,
89.63,
124.06,
99.47,
88.97,
80.93,
102.07,
91.69,
63.71,
200.49,
75.3,
240.71,
60.5,
131.85,
134.39,
56.18,
113.64,
156.57,
80.4,
59.26,
63.26,
86.24,
197.1,
90.9,
84.59,
121.8,
77.65,
64.41,
75.19,
67.97,
119.34,
94.92,
85.02,
103.51,
114.5,
70.78,
95.01,
84.11,
74.02,
78.49,
74.22,
55.34,
75.04,
109.73,
96.78,
194.62,
70.02,
222.21,
62.41,
55.83,
65.12,
73.62,
85.83,
103.86,
60.98,
82.69,
250.2,
128.97,
84.31,
74.8,
97.06,
77.54,
92.78,
173.43,
79.08,
106.18,
114.21,
91.08,
105.77,
74.88,
123.15,
184.25,
117.69,
55.26,
106.51,
55.27,
98.42,
99.2,
98.92,
92.7,
106.7,
152.81,
82.59,
91,
93.88,
76.99,
254.6,
93.99,
109.68,
92.04,
111.41,
97.92,
65.61,
62.37,
101.28,
88.31,
103.55,
60.26,
79.39,
122.01,
78.09,
89.7,
95.1,
112.64,
79.8,
82.2,
108.89,
114.32,
116.14,
122.74,
94.04,
86.6,
82.32,
83.83,
106.98,
93.88,
70.53,
212.01,
65.88,
106.97,
126.35,
129.16,
88.66,
112.95,
81.59,
76.82,
73.75,
91.56,
104.55,
84.84,
86.06,
109.1,
68.35,
121.04,
160.83,
85.62,
74.23,
110.76,
79.44,
119.52,
90.42,
109.32,
78.16,
79.13,
84.06,
89.3,
69.21,
186.45,
58.47,
189.49,
99.6,
126.18,
186.32,
62.49,
74.51,
94.63,
91.98,
56.77,
75.87,
77.19,
135.75,
100.15,
95.08,
125.38,
111.71,
94.64,
112.96,
105.36,
84.13,
102.47,
77.04,
87.01,
56.99,
73.01,
106.35,
80.63,
103.61,
194.04,
111.99,
57.51,
68.49,
60.2,
160,
86.96,
237.15,
99.06,
56.31,
84.31,
90.92,
104.33,
62.61,
231.19,
94.3,
104.48,
72.62,
81.64,
90.78,
70.71,
72.36,
59.76,
63.64,
76.26,
82.06,
110.91,
77.16,
69.58,
96.26,
87.27,
65.36,
109.51,
89.03,
91.34,
57.82,
81.15,
115.29,
111.02,
111.61,
109.02,
106.51,
207.32,
87.11,
61.68,
119.04,
86.05,
70.03,
207.64,
236.84,
78.11,
100.97,
99.21,
113.21,
92.95,
142.57,
92.87,
111.48,
123,
100.54,
106.68,
86.4,
61.81,
128.28,
204.63,
89.98,
232.89,
98.58,
57.94,
123.21,
76.34,
92.98,
92.81,
85.16,
90.1,
65.15,
104.62,
113.25,
59.28,
93.07,
77.42,
73.69,
74.9,
58.39,
102.05,
112.47,
72.84,
195.03,
170.95,
87.98,
65.81,
124.6,
83.65,
88.81,
86.75,
123.36,
89.68,
69.87,
109.22,
157.57,
110.73,
99.14,
59.15,
165.11,
77.44,
100.33,
57.93,
148.24,
91.96,
72.93,
84.27,
124.45,
90.51,
118.87,
56.42,
73.67,
89.11,
74.04,
145.23,
100.8,
85.22,
72.49,
82.61,
72.07,
79.85,
84.86,
131.89,
89.17,
227.91,
131.77,
102.01,
128.63,
144.1,
143.15,
116.67,
85.6,
142.12,
100.81,
69.17,
58.38,
86.34,
95.44,
94.78,
109.59,
79.26,
78.46,
85.29,
156.18,
69.52,
65.32,
108.47,
105.63,
77.93,
95.2,
78.52,
114.89,
95.43,
84.08,
110.55,
82.21,
82.38,
93.89,
75.08,
76.72,
84.93,
76.5,
65.47,
99.16,
63.53,
78.65,
63.78,
77.52,
111.64,
97.84,
113.01,
56.48,
204.5,
96.02,
206.25,
115.52,
60.69,
93.67,
67.79,
56.07,
254.63,
96.63,
84.3,
246.34,
195.16,
93.34,
162.3,
106.4,
110.25,
69.7,
87.03,
112.98,
82.47,
75.88,
84.63,
109.56,
89.45,
91.65,
121.14,
87.77,
106.56,
93.88,
153.76,
88.65,
111.22,
223.68,
55.86,
63.73,
103.94,
92.49,
229.2,
75.06,
85.57,
68.61,
56.3,
65.98,
204.57,
64.99,
73.4,
82.83,
93.55,
90.4,
98.1,
142.02,
251.46,
89.96,
82.44,
80.85,
220.52,
86.73,
76.93,
113.1,
96.84,
100.91,
116.5,
57.26,
129.31,
56.47,
86.06,
86.97,
78.78,
195.04,
103.25,
134.59,
93.74,
90.66,
91.68,
75.79,
91.68,
106.68,
82.31,
90.06,
65.47,
58.03,
83.09,
88.6,
63.18,
78.42,
65.08,
79.47,
95.52,
73,
138.07,
86.99,
112.07,
105.76,
55.67,
90.55,
218.65,
111.21,
83.56,
116.76,
83.5,
69.3,
67.55,
80.28,
211.49,
67.96,
99.29,
140.52,
75.62,
120.06,
117.45,
224.71,
122.75,
82.39,
57.02,
115.98,
226.11,
97.59,
82.57,
79.34,
125.68,
65.59,
120.85,
101.65,
210.94,
77.92,
230.68,
62.68,
55.22,
78.94,
109.69,
79.33,
79.91,
84.46,
68.4,
78.43,
198.02,
85.35,
204.17,
82.71,
121.19,
106.02,
66.36,
267.76,
107.18,
74.58,
80.92,
90.77,
90.84,
59.2,
133.76,
61.36,
118.62,
77.91,
68.98,
112.08,
96.2,
93.11,
115.54,
88.57,
180.76,
83.12,
94.89,
239.52,
99.15,
124.16,
86.38,
93.68,
229.86,
78.73,
93.71,
76.66,
112.38,
89.3,
103.44,
61.61,
94.12,
62.4,
210.96,
76.25,
103.48,
96.42,
78.59,
195.25,
103.76,
68.52,
92.17,
81.02,
94.33,
78.43,
217.39,
82.77,
88.13,
102.77,
101.75,
113.34,
63.33,
69.34,
118.81,
66.13,
67.53,
96.58,
57.89,
78.38,
94.15,
89.53,
127.75,
76.79,
141.15,
88.63,
130.15,
108.03,
104.07,
88.83,
197.79,
150.45,
100.39,
79.92,
96.79,
86.84,
61.27,
72.61,
82.41,
74.72,
57.37,
88.34,
101.34,
119.01,
71.98,
84.91,
91.63,
145.71,
167.31,
63.65,
79.63,
87.49,
92.82,
72.2,
181.23,
63.37,
74.82,
97.6,
93.04,
97.12,
82.81,
134.45,
98.67,
85.82,
189.45,
144.16,
82.56,
206.4,
67.97,
71.7,
72.02,
61.81,
103.09,
73.75,
156.7,
70.87,
138.47,
64.66,
91.88,
74.79,
78.18,
77.82,
112.23,
178.76,
121.43,
94.22,
197.58,
86.37,
112.09,
199.96,
85.33,
86.33,
80.47,
62.46,
78.85,
103.81,
78.04,
94.67,
62.25,
155.17,
83.28,
132.41,
62.78,
89.96,
103.25,
126.99,
102.27,
113.63,
86.09,
108.08,
108.34,
56.07,
82.91,
94.75,
101.79,
88.41,
101.05,
85.07,
108.1,
93.17,
158.89,
205.77,
112.34,
119.03,
237.21,
89.68,
246.53,
89.05,
81.74,
98.12,
105.59,
86,
65.51,
61.78,
109.56,
65.82,
90.16,
64.45,
91.85,
97.89,
206.33,
74.29,
107.4,
87.72,
85.99,
100.84,
124.61,
86.55,
90.42,
100.85,
87.74,
80.15,
107.29,
206.98,
227.28,
80.43,
228.7,
120.07,
93.47,
80.77,
150.27,
81.94,
70.75,
83.83,
73.66,
102.37,
77.83,
70.01,
119.88,
169.97,
123.79,
80.98,
84.21,
101.31,
86.26,
92.86,
140.28,
244.28,
124.16,
93.55,
99.34,
59.87,
72.76,
68.34,
61.1,
120.77,
60.39,
126.34,
93.24,
154.75,
62.81,
65.96,
79.53,
96.85,
251.99,
115.13,
102.3,
78.99,
68.8,
83.84,
94.66,
107.98,
61.01,
135.64,
92.65,
121.11,
107.69,
70.15,
83.02,
222.29,
139.43,
213.11,
77.23,
58.48,
87.33,
102.34,
151.3,
144.23,
110.6,
80.28,
111.02,
114.88,
227.51,
111.65,
201.01,
129.01,
124.08,
107.91,
95.4,
65.9,
78.98,
111.33,
104.36,
123.87,
210,
86.86,
113.96,
81.28,
102.5,
78.05,
86.07,
59.05,
95.88,
70.18,
111.13,
79.3,
65.84,
123.49,
207.45,
143.45,
89.11,
58.3,
78.68,
98.91,
67.92,
73,
97.5,
108.43,
90.43,
76.3,
121.27,
119.67,
226.93,
76.11,
253.16,
101.02,
238.53,
79.18,
207.79,
196.2,
231.76,
93.3,
110.47,
77.52,
216.92,
157.01,
93.51,
102.54,
87.78,
126.67,
194.98,
99.96,
58.87,
77.67,
83.14,
69.18,
218.54,
71.22,
82.41,
82.07,
73,
127.25,
72.63,
108.2,
123.95,
58.71,
98.46,
68.78,
133.63,
101.43,
65.3,
72.49,
65.49,
63.4,
105.29,
64.08,
78.34,
66.32,
168.15,
93.48,
79.2,
107.45,
83.86,
119.96,
70.58,
87.7,
65.29,
97.14,
110.68,
91.93,
88.54,
183,
115.93,
99.13,
69.24,
102.28,
71.31,
89.75,
87.66,
81.11,
178.33,
81.38,
109.56,
70.45,
73.44,
146.44,
65.04,
151.25,
106.41,
197.09,
93.58,
122.04,
80.06,
84.93,
68.4,
62.99,
139.67,
113.4,
101.92,
65.98,
84.3,
61.42,
85.52,
83.79,
73.83,
69.38,
66.11,
149.62,
242.84,
202.66,
216.9,
103.78,
114.88,
75.94,
55.51,
93.74,
71.44,
58.29,
115.83,
102.71,
99.69,
102.89,
56.63,
87.34,
109.51,
78.59,
78.26,
105.47,
68.19,
114.47,
66.85,
94.71,
208.05,
222.6,
80.81,
64.51,
151.56,
199.14,
73.41,
109.65,
69.11,
114.41,
103.46,
95.36,
57.33,
191.48,
71.63,
84.75,
89.24,
70.13,
82.26,
200.16,
80.73,
75.09,
80.57,
77.04,
145.26,
82.08,
103.35,
97.46,
123.65,
87.96,
85.92,
80.8,
99.3,
62.56,
144.2,
71.38,
95.5,
67.06,
70.16,
104.24,
122.48,
215.9,
108.56,
69.88,
91.54,
65.85,
60.74,
233.52,
110.7,
78.93,
124.5,
57.95,
92.82,
58.25,
213.54,
74.86,
78.35,
101.26,
86.87,
124.01,
83.41,
70.65,
92.4,
73.36,
219.5,
105.73,
83.79,
58.02,
61.94,
99.71,
63.19,
56.84,
217.66,
77.19,
76.22,
155.32,
70.67,
227.16,
58.69,
88.85,
137.94,
60.6,
93.67,
79.56,
122.46,
92.32,
80.63,
92.32,
59.89,
80.57,
88.52,
88.82,
102.08,
84.9,
82.21,
74.05,
90.46,
176.48,
94.07,
85.12,
71.81,
111.93,
94.4,
70,
95.57,
91.98,
66.29,
211.88,
76.55,
56.67,
100.52,
58.96,
81.66,
96.77,
91.63,
97.51,
225.6,
210.23,
92.82,
234.82,
65.67,
74.51,
81.99,
87.69,
125.29,
111.64,
70.93,
89.93,
137.45,
72.06,
97.9,
84.85,
230.59,
92.81,
168.15,
224.63,
108.12,
85.17,
84.14,
85.57,
82.81,
110.53,
61.88,
85.04,
140.4,
62.27,
99.44,
88.9,
64.1,
97.58,
58.66,
88.24,
61.32,
62.99,
86.04,
83.3,
208.17,
133.2,
81.54,
146.61,
79.33,
84.56,
103.44,
185.31,
102.53,
65.78,
62.6,
81.18,
76.68,
92.86,
90.95,
85.66,
68.37,
77.32,
187.87,
213.87,
119.62,
222.85,
100.82,
136.1,
149.42,
79.99,
118.21,
56.3,
84.49,
89.61,
100.09,
198.36,
93.61,
75.43,
100.31,
105.63,
82.84,
58.88,
92.27,
115.23,
196.25,
85.97,
114.33,
72.52,
63.47,
90.58,
92.26,
113.74,
72.79,
94.71,
194.53,
72.65,
86.68,
140.39,
77.87,
104.7,
95.82,
204.05,
130.56,
67.78,
105.29,
135.19,
116.12,
59.63,
56.43,
199.18,
209.26,
91.21,
95.16,
217.11,
222.46,
82.44,
74.11,
131.23,
187.52,
83.26,
99.07,
76.63,
65.09,
142.31,
80.99,
237.74,
154.08,
78.29,
164.67,
59.61,
67.75,
90.35,
77.86,
71.88,
118.82,
67.33,
121.66,
111.92,
92.82,
95.46,
59.99,
68.96,
90.26,
91.9,
91.08,
118.55,
102.04,
65.52,
82.09,
149.9,
100.33,
91.25,
89.81,
97.32,
75.53,
121.44,
83.52,
57.92,
75.27,
80.24,
88.69,
69.09,
62.67,
98.74,
56.51,
65.95,
129.07,
62.47,
69.82,
99.68,
223.35,
74.36,
102.97,
153.38,
87.1,
113.4,
94.88,
82.73,
67.8,
84.85,
135.89,
106.13,
74.33,
109.4,
75.4,
87.69,
95.85,
90.36,
63.45,
201.07,
122.91,
55.41,
60.99,
111.77,
77.63,
70.61,
124.34,
103.44,
124.5,
75.5,
98.85,
96.06,
93.74,
65.6,
113.24,
94.77,
145.22,
112.92,
84.79,
103.28,
96.18,
186.95,
68.66,
128.72,
85.04,
146.97,
65.69,
97.52,
90.43,
81.71,
198.24,
90.51,
97.78,
80.74,
57.02,
93,
95.37,
96.25,
86.74,
63.08,
129.16,
99.79,
83.34,
95.01,
229.21,
80.08,
62,
118.69,
77.57,
108.75,
108.23,
151.33,
82.43,
107.17,
91.46,
81.05,
114.37,
100.29,
68.37,
87.95,
73.27,
131.43,
107.29,
68.24,
74.01,
95.4,
95.75,
104.51,
108.87,
209.06,
86.57,
120.25,
85.51,
84.18,
82.37,
96.15,
74.55,
114.45,
113.68,
107.91,
83.85,
101.3,
72.13,
94.98,
228.42,
93.9,
93.13,
121.6,
84.4,
82.13,
73.33,
75.88,
212.97,
100.41,
202.05,
71.42,
107.5,
55.57,
58.14,
78.53,
78.79,
96.62,
65.28,
92.67,
206.25,
114.18,
231.69,
101.89,
107.22,
101.99,
72.56,
94.47,
98.76,
116.93,
219.96,
75.77,
124.35,
88.97,
72.49,
107.27,
97.96,
197.69,
112.3,
75.77,
76.51,
93.14,
80.84,
199.88,
65.34,
98.71,
170.22,
73.5,
102.96,
80.72,
74.19,
77.12,
81.2,
114.76,
109.97,
92.87,
113.05,
68.24,
74.35,
91.08,
88.83,
134.33,
208.78,
66.17,
222.29,
114.16,
61.67,
220.36,
76.03,
73.06,
187.88,
79.89,
79.02,
191.66,
76.74,
72.29,
92.59,
80.42,
62.47,
63.94,
77.73,
115.79,
96.3,
107.46,
108.33,
89.37,
93.64,
217.75,
83.44,
93.88,
55.34,
102.61,
62.08,
81.76,
59.2,
55.61,
226.88,
65.66,
58.7,
186.4,
122.32,
59.05,
149.13,
82.39,
169.49,
80.25,
203.81,
170.76,
80.85,
120.58,
86.57,
90.01,
68.84,
155.14,
158.48,
92.56,
90.15,
80.09,
189.44,
70.03,
249.29,
72.52,
96.93,
211.35,
85.87,
60.96,
120.09,
92.13,
83.07,
77.67,
97.24,
127.29,
134.8,
74.65,
85.84,
90.43,
74.35,
206.59,
196.33,
70.23,
242.94,
103.06,
80.86,
114.32,
83.91,
152.02,
75.19,
60.61,
89.14,
97.23,
135.19,
78.48,
75.92,
93.3,
97.39,
134.61,
104.02,
56.9,
112.66,
226.75,
76.28,
90.01,
107.84,
71.06,
185,
104.42,
89.57,
136.2,
128.23,
104.23,
133.82,
80.82,
102.35,
88.48,
88.81,
75.09,
199.83,
73.69,
111.27,
68.44,
65.36,
94.12,
96.24,
91.25,
77.29,
125.09,
107.18,
130.07,
57.3,
101.35,
94.03,
227.81,
66.67,
240.81,
88.02,
152.87,
96.04,
60.35,
118.66,
58.51,
96.04,
110.92,
92.72,
108.23,
56.34,
61.83,
131.99,
136.81,
239.28,
131.3,
117.75,
78.46,
83.53,
145.5,
88.33,
101.25,
131.43,
231.5,
82.89,
192.37,
82.88,
99.4,
77.75,
75.03,
220.47,
196.91,
180.8,
247.48,
216,
75.64,
95.07,
129.97,
124.26,
87.92,
85.15,
95.36,
87.16,
219.39,
71.92,
167.66,
87.21,
220.47,
80.47,
89.95,
111.32,
65.01,
110.07,
60.56,
89.83,
90.35,
71.5,
71.71,
173.96,
96.06,
113.2,
73.29,
65.71,
60.41,
89.11,
63.32,
85.68,
162.93,
89.22,
91.68,
198.33,
85.81,
79.59,
106.74,
191.33,
59.52,
206.52,
64.92,
80,
75.06,
91.18,
85.81,
148.37,
64.55,
97.31,
111.15,
80.97,
89.88,
62.66,
122.25,
87.08,
85,
100.49,
145.37,
112.19,
79.73,
101.13,
101.98,
98.39,
128.04,
98.12,
216.96,
94.92,
83.27,
96.86,
99.73,
91.71,
65.77,
161.57,
74.32,
170.93,
74.43,
86.36,
97.06,
110.38,
79.03,
62.48,
55.62,
84.9,
232.81,
86.55,
118.61,
80.21,
65.96,
61.34,
116.6,
85.13,
86.32,
69.22,
140.93,
83.43,
207.95,
134.29,
115.12,
86.25,
112.94,
73.54,
80.86,
83.91,
69.91,
95.62,
63.95,
122.5,
86,
120.27,
56.71,
131.63,
79.36,
120.77,
100.42,
155.86,
66.01,
86.99,
90.55,
229.58,
60.06,
65.66,
104.7,
113.86,
99.49,
165.99,
114.99,
79.58,
77.37,
93.55,
187.22,
167.59,
98.03,
88.79,
92.9,
117.34,
80.75,
77.51,
105.63,
114.34,
227.04,
87.56,
84.47,
96.26,
116.64,
79.57,
78.44,
64.15,
74.5,
70.32,
84.48,
144.08,
78.14,
133.58,
214.42,
80.63,
233.71,
96,
74.52,
131.4,
216.4,
108.8,
97.06,
266.59,
55.58,
65.87,
89.28,
56.12,
74.7,
83.06,
227.94,
84.88,
64.2,
137.22,
81.44,
90.26,
68.48,
100.02,
156.82,
81.66,
112.22,
90.62,
72.49,
107.47,
92.11,
70.54,
205,
126.39,
89.72,
92.08,
85.38,
95.25,
75.91,
203.44,
88.23,
97.47,
59.28,
101.15,
68.38,
55.46,
113.87,
96.35,
127.21,
78.9,
59.91,
160.94,
59.61,
87.94,
97.55,
116.02,
127.32,
75.5,
79.35,
108.56,
168.06,
75.52,
82.15,
200.28,
62.44,
84.31,
221.43,
165.47,
96.69,
63.98,
145.03,
98.53,
134.23,
62.48,
70.52,
93.21,
82.39,
88.29,
80.18,
84.02,
69.72,
192.16,
101.87,
77.55,
215.72,
87.84,
91.93,
58.63,
173.14,
106.83,
84.44,
92.16,
85.96,
202.57,
65.41,
102.51,
62.6,
150.1,
94.15,
65.5,
81.31,
78.96,
123.47,
90.4,
71.59,
209.5,
86.6,
57.43,
87.93,
203.16,
112.12,
92.02,
60.98,
86.35,
71.26,
72.61,
110.78,
99.78,
201.45,
127.78,
121.32,
83.89,
206.15,
102.9,
65.48,
123.23,
71.3,
79.05,
102.21,
105.22,
84.58,
77.3,
74.79,
88.75,
97.97,
79.18,
81.33,
126.68,
196.61,
219.92,
79.16,
99.68,
79.55,
87.86,
76.92,
77.24,
120.94,
77.66,
231.95,
111.48,
84.25,
216.38,
95.01,
105.74,
58.41,
56.89,
86.25,
86.49,
115.47,
92.22,
83.16,
110.68,
70.06,
213.33,
93.52,
87.62,
77.67,
95.66,
172.33,
106.59,
87.06,
93.02,
243.59,
86.7,
97.81,
116.04,
169.43,
68.41,
94.44,
73.48,
183.87,
66.08,
72.19,
101.09,
88.83,
69.99,
102.4,
87.26,
146.59,
59,
76.83,
101.83,
90.92,
67.5,
97.16,
85.62,
129.43,
106.01,
114.82,
121.39,
227.98,
78.97,
84.12,
95.04,
103.15,
82.91,
70.93,
153.6,
129.43,
81,
71.81,
84.46,
101.32,
62.56,
208.2,
199.42,
91.28,
77.99,
190.13,
149.68,
92.3,
235.54,
84.46,
68.7,
178.89,
227.74,
100.66,
73.62,
100.05,
213.8,
103.94,
84.2,
250.8,
112.29,
65.98,
99.1,
104.34,
217,
57.47,
78.24,
94.38,
100.31,
74.29,
103.43,
99.83,
217.4,
71.81,
79.79,
190.92,
94.76,
101.43,
115.86,
95.27,
89.02,
77.55,
75.86,
93.62,
72.71,
94.45,
88.57,
84.12,
89.11,
99.83,
150.03,
69.67,
77.46,
100.54,
142.38,
110.87,
73.6,
59.52,
142.68,
182.9,
75.28,
100.75,
163.82,
110.42,
79.59,
107.99,
90.22,
143.45,
78.57,
79.82,
95.88,
123.04,
92.34,
116.84,
90.04,
255.17,
123.66,
112.16,
88.43,
86.03,
227.96,
85.6,
111.68,
73.49,
87.82,
99.29,
63.73,
85.55,
79.2,
105.93,
94.19,
69.28,
71.37,
110.18,
59.68,
94.24,
82.1,
62.62,
107.83,
107.59,
116.98,
84.1,
160.64,
84.4,
81.13,
88.19,
127.13,
119.61,
143.47,
84.6,
158.93,
68.94,
69.38,
72,
57.76,
108.65,
65.42,
78.93,
98.34,
82.57,
124.37,
100.96,
75.22,
59.85,
104.75,
70.98,
92.77,
82.12,
106.35,
77.33,
96.01,
107.49,
88.47,
87.92,
91.13,
80.72,
59.83,
60.22,
82.41,
75.78,
102.42,
98.45,
93.51,
113.68,
97.41,
58.65,
81.68,
148.52,
142.12,
125.3,
118.81,
84.16,
78.74,
57.51,
61.11,
78.3,
76.19,
115.99,
100.8,
98.27,
93.85,
94.69,
136.23,
56.12,
93.03,
196.81,
87.81,
222.66,
223.58,
85.77,
88.23,
73.65,
141.8,
92.65,
61.04,
102.88,
123.39,
67.07,
69.88,
57.02,
123.81,
73.36,
90.54,
69.24,
192.39,
74.35,
71.94,
82.35,
73.28,
97.57,
83.3,
85.07,
60.4,
79.96,
70.96,
82.86,
201.38,
91.82,
91.85,
99.23,
68.13,
93.76,
103.89,
87.39,
89.53,
66.61,
236.14,
99.76,
87.09,
193.81,
97.27,
137.77,
105.19,
58.35,
68.79,
94.19,
121.17,
106.41,
104.79,
82.18,
96.81,
107.42,
69.2,
92.73,
86.11,
111.78,
122.26,
78.16,
105.77,
77.53,
77.35,
82.36,
239.95,
61.96,
72.1,
107.97,
60.77,
71.06,
60.36,
83.33,
65.33,
113.28,
170.88,
103.34,
106.08,
156.45,
60.05,
114.05,
202.21,
57.28,
99.49,
63.6,
80.01,
58.42,
67.38,
73.58,
103.92,
80.22,
103.76,
77.06,
90.22,
181.3,
85.29,
107.59,
87.72,
70.73,
72.75,
95.47,
70.54,
100.03,
59.78,
198.79,
90.39,
147.04,
64.92,
154.6,
95.98,
102.51,
96.52,
232.12,
78.12,
70.04,
97.61,
113.11,
71.06,
104.95,
81.94,
96.37,
114.16,
93.58,
73.89,
107.83,
107.21,
99.35,
203.57,
81.51,
69.26,
111.47,
86.09,
72.94,
76.77,
60.32,
133.24,
65.01,
74.63,
230.78,
72.5,
68.48,
85.03,
80.76,
95.36,
110.33,
89.18,
91.53,
65.16,
74.53,
93.29,
227.89,
121.46,
216.71,
74.96,
75.25,
91.82,
56.37,
149.15,
202.67,
65.38,
90.31,
221.8,
73.39,
147.12,
109.85,
103.21,
81.25,
84.69,
87.1,
81.92,
90.91,
64.4,
67.29,
124.48,
62.89,
87.51,
93.15,
64.87,
90.68,
104.64,
92.44,
120.46,
89.87,
105.28,
131.8,
66.33,
101.07,
83.37,
79.8,
103.17,
62.52,
110.36,
140.07,
84.86,
106.85,
102.07,
202.38,
67.07,
73.7,
77.59,
69.16,
215.81,
65.4,
139.72,
72.81,
149.17,
125.74,
90.21,
139.48,
90.77,
220.24,
81.42,
102.36,
82.85,
102.11,
80.92,
71.8,
88.47,
67.68,
98.65,
83.58,
104.04,
69.4,
106.22,
112.46,
72.42,
96.73,
107.4,
267.61,
109.46,
120.05,
106.33,
97.28,
83.13,
97.35,
79.54,
76.55,
56.11,
91.36,
79.27,
113.21,
105.99,
59.93,
88.83,
115.42,
71.93,
96.43,
161.95,
104.55,
71.25,
82.02,
78.02,
115.21,
65.12,
143.33,
80.55,
88.2,
147.42,
86.53,
91.95,
63.72,
63.78,
55.64,
88.88,
114.61,
56.08,
59.62,
75.85,
176.71,
80.07,
56.33,
87.16,
102.46,
161,
70.7,
81.26,
157.67,
89.59,
64.02,
110.69,
71.97,
82.07,
65.07,
207.62,
108.64,
201.58,
116.2,
231.43,
112.62,
220.26,
84.37,
82.83,
102.48,
69.12,
211.12,
95.38,
108.72,
88.65,
73.78,
59.48,
75.77,
80.96,
82.4,
78.93,
80.59,
70.59,
98.57,
61.87,
68.88,
66.25,
69.23,
73.2,
103.45,
104.86,
90.3,
90.87,
76.21,
74.14,
87.25,
215.33,
88.38,
104.4,
104.92,
106.54,
62.91,
61.47,
88.5,
107.61,
90.77,
137.91,
228.2,
92.15,
126.32,
94.96,
89.52,
66.59,
80.1,
73.72,
90.49,
92.99,
62.57,
65.51,
95.29,
83.7,
63.16,
87.77,
87.52,
113.65,
65.93,
93.78,
223.9,
77.54,
93.18,
169.74,
101.93,
125.03,
82.25,
85.81,
85.27,
86.06,
104.36,
78.29,
93.55,
207.96,
105.52,
109.27,
91.34,
176.78,
67.73,
73.27,
70.29,
91.47,
205.01,
73.87,
64.44,
111.65,
90.65,
94.2,
56.95,
92.37,
127.23,
191.78,
86.36,
82.72,
113.57,
89.58,
91.85,
88.89,
79.55,
108.62,
78.24,
88.49,
91.85,
72.01,
81.96,
70.56,
71.02,
99.75,
127.2,
69.76,
74.63,
102.27,
73.56,
57.77,
151.25,
78.32,
71.5,
84.7,
127.28,
80.72,
73.92,
93.24,
72.6,
69.77,
84.43,
84.13,
81.54,
57.17,
86.15,
163.02,
110.41,
102.58,
90.97,
140.96,
70.33,
64.18,
127.57,
109.23,
124.49,
142.82,
75.69,
74.08,
75.98,
57.8,
76.44,
214.43,
79.03,
66.46,
56.42,
112.72,
114.02,
72.33,
55.59,
73.48,
105.05,
84.78,
164.77,
220.64,
159.39,
69.53,
81.42,
83.93,
98.9,
82.64,
112.54,
75.25,
204.77,
63.86,
72.2,
80.08,
101.61,
114.32,
80.82,
90.6,
93.67,
87.15,
80.72,
83.16,
98.14,
83.13,
134.24,
248.37,
61.75,
70.55,
124.38,
92.21,
148.72,
79.25,
82.09,
194.53,
98.84,
135.74,
66.55,
115.69,
67.76,
88.65,
106.11,
95.49,
59.07,
228.92,
62.89,
96.84,
81.25,
92.71,
85.59,
89.06,
87.44,
64.07,
79.39,
67.84,
81.73,
105.52,
77.55,
127.42,
226.73,
89.74,
219.17,
74.85,
55.96,
215.92,
104.05,
62.54,
87.54,
76.81,
198.12,
78,
79.44,
75.82,
240.86,
89.28,
263.56,
81.13,
118.44,
97.37,
80.51,
78.88,
83.61,
73.19,
68.91,
104.3,
87.87,
96.7,
91.05,
200.14,
73.04,
85.81,
65.41,
108.68,
88.79,
107.82,
97.39,
141.09,
122.38,
77.91,
103.6,
159.7,
118.22,
84.5,
85.59,
84.42,
100.98,
81.11,
79.2,
207.71,
99.84,
98.05,
105.9,
146.21,
79.94,
72.28,
123.1,
67.06,
82.68,
125.89,
228.05,
98.23,
90.06,
119.9,
94.53,
64.66,
103.79,
88.51,
97.26,
92.59,
102.89,
92.26,
80,
56.75,
104.66,
60.39,
223.14,
105.51,
83.42,
71.06,
101.24,
91.19,
174.43,
214.51,
72.64,
62.69,
141.16,
89.61,
76.42,
124.38,
111.81,
57.57,
77.82,
60.09,
91.02,
65.46,
131.19,
79.89,
74.54,
231.31,
91.57,
238.78,
87.18,
78.94,
69.01,
110.28,
71.66,
62.57,
83.64,
107.18,
233.59,
84.88,
97.95,
58.19,
70.11,
112.77,
75.46,
63.63,
100.22,
81.77,
94.25,
188.13,
164.7,
68.27,
100.12,
151.23,
112.41,
205.97,
112.19,
71.93,
190.89,
138.02,
66.16,
105.73,
78.34,
102.1,
97.37,
193.87,
78.32,
130,
74,
111.04,
214.77,
97.25,
79.95,
162.24,
74.36,
69.68,
98.35,
75.73,
85.86,
80.34,
73.76,
94.96,
108.18,
85.23,
189.88,
197.11,
100.08,
68.62,
84.07,
99.65,
82.33,
94.49,
108.71,
64.94,
98.22,
83.5,
192.47,
125.89,
87.72,
68.94,
66.51,
85.82,
87.47,
87.74,
85.12,
103.37,
81.68,
76.51,
110.17,
65.12,
109.88,
199.38,
65.77,
55.72,
75.67,
88.18,
95.94,
86.93,
85.17,
111.1,
92,
97.47,
79.21,
83.97,
98.55,
202.98,
60.7,
92.22,
79.94,
83.68,
69.92,
136.96,
95.32,
107.78,
198.32,
100.16,
96.02,
112.69,
60.61,
79.77,
87.41,
226.38,
124.64,
69.42,
77.68,
112.79,
236.79,
219.82,
82.05,
104.08,
101.57,
71.32,
239.19,
78.45,
61.11,
87.29,
82.49,
83.15,
83.26,
78.7,
111.94,
104.77,
104.26,
113.84,
114.71,
61.54,
133.62,
111.96,
112.44,
123.61,
84.68,
206.62,
114.54,
216.88,
110.38,
204.92,
56.74,
68.35,
83.52,
59.74,
81.59,
123.83,
226.84,
93.2,
85.84,
74.64,
80.17,
85.03,
234.35,
116.25,
66.55,
88.38,
81.05,
68.4,
96.95,
66.24,
91.81,
84.86,
118.88,
85.91,
115.4,
75.34,
100.19,
200.73,
114.09,
67.3,
96.91,
116.2,
72.54,
96.01,
89.16,
108.64,
83.66,
81.21,
86.96,
92.95,
95.86,
85.52,
159.39,
202.51,
82.24,
109.81,
91.85,
123.49,
105.49,
101.53,
126.04,
95.49,
89.38,
82.3,
218,
79.7,
209.15,
70.22,
83.57,
60.6,
97.24,
162.72,
73.27,
92.62,
90,
78.28,
87.81,
196.5,
142.31,
94.96,
83.78,
86.1,
66.96,
76.7,
103.12,
84.03,
74.15,
73.29,
115.16,
83.55,
61.53,
100.74,
59.31,
63.22,
209.5,
64.51,
90.07,
113.95,
119.32,
99.12,
152.84,
78.23,
76.05,
116.49,
99.91,
76.1,
121.99,
116.21,
55.28,
103.73,
153.08,
98.69,
94.11,
117.03,
91.35,
82.48,
99.76,
111.1,
122.25,
84.19,
84.6,
126.35,
87.5,
77.52,
133.13,
96.1,
108.61,
205.23,
76.74,
138.55,
234.27,
75.16,
77.08,
101.19,
107.74,
62.32,
56.32,
131.28,
67.02,
76.72,
125.98,
72.09,
88.05,
55.47,
145.94,
239.21,
196.08,
93.93,
76.74,
83.74,
119.58,
68.66,
65.79,
176.38,
175.74,
193.45,
85.84,
93.85,
86.21,
75.41,
95.28,
95.87,
180.45,
95.39,
60.77,
102.84,
130.37,
65.44,
79.84,
62.61,
70.07,
108.8,
112.11,
92.35,
76.57,
69.5,
69.48,
89.18,
68.09,
92.71,
56.21,
82.56,
78.24,
84.66,
90.35,
68.12,
59.14,
85.64,
56.08,
99,
93.6,
105.75,
114.09,
57.42,
100.01,
62,
103.61,
118.46,
94.77,
72.12,
155.23,
90.6,
81.92,
67.1,
58.01,
173.9,
106.27,
60.57,
105.34,
96.28,
122.73,
104.12,
69.74,
106.95,
94.63,
86.97,
76.12,
99.94,
91.53,
145.46,
74.86,
74.42,
70.07,
89.85,
104.21,
157.77,
101.81,
103,
101.85,
85.92,
56.85,
89.04,
63.01,
65.48,
68.6,
81.68,
65.21,
72.53,
81.78,
116.85,
80.83,
96.21,
117.04,
78.26,
82.53,
71.25,
129.73,
115.47,
56.79,
100.16,
97.6,
90.54,
105.88,
217.94,
69.89,
93.28,
111.27,
91.04,
87.2,
109.12,
216.64,
74.28,
57.6,
173.97,
65.25,
80.92,
124.37,
102.39,
90.42,
79.17,
71.08,
81.54,
159.67,
93.96,
103.01,
92.49,
208.85,
122.39,
65.91,
70.25,
219.7,
65.7,
67.08,
107.58,
95.87,
68.17,
95.37,
90,
106.1,
56.13,
104.03,
157.01,
84.35,
67.99,
118.93,
185.28,
82.08,
88.32,
85.16,
69.54,
62.12,
198.3,
100.06,
101.13,
91.09,
61.54,
62.02,
103.11,
77.57,
97.34,
67.1,
79.89,
206.66,
95.79,
93.51,
116.78,
117.63,
60.01,
64.6,
71.63,
87.79,
99.64,
86.53,
200.68,
98.37,
117.98,
218.6,
101.3,
79.51,
223.26,
97.22,
172.27,
221.83,
74.61,
60.91,
69.79,
83.6,
83.95,
77.5,
64.29,
61.1,
100.71,
65.21,
119.77,
102.39,
59.67,
83.16,
218.1,
76.45,
200.46,
95.7,
70.23,
75.15,
95.8,
90.67,
84.79,
147.5,
134.12,
82.62,
125.11,
92.14,
217.79,
95.62,
93.8,
90.61,
139.2,
77.01,
105.26,
68.35,
99.07,
147.74,
61.29,
98.92,
90.74,
159.79,
233.47,
85.53,
76.7,
67.87,
77.26,
91.28,
129.19,
97.86,
112.33,
110.41,
91.58,
58.86,
76.88,
109.52,
181.23,
103.58,
81.96,
104.55,
76.12,
64.27,
200.98,
101.96,
84.17,
85.79,
71.46,
95.49,
87.62,
118.55,
82.14,
219.67,
135.84,
74.83,
60.37,
67.28,
77.79,
88.51,
90.38,
156.69,
158.9,
247.97,
114.71,
58.55,
231.15,
68.18,
118.75,
186.54,
96.14,
69.52,
101.93,
71.58,
83.1,
110.96,
122.83,
91.65,
84.1,
118.85,
89.32,
128.72,
98.05,
102.1,
96.19,
98.07,
71.4,
82.27,
158.33,
102.5,
72.18,
95.94,
87.15,
82.59,
89.31,
64.84,
107.52,
92.76,
79.16,
87,
145.25,
125.14,
88.6,
57.59,
59.43,
65.43,
154.03,
139.81,
111.81,
106.84,
221.06,
69.97,
77.07,
59.49,
109.39,
93.97,
163.17,
124.39,
212.62,
128.63,
84.1,
77.6,
70.51,
109.19,
55.23,
120.15,
72.16,
95.89,
64.68,
70.51,
81.77,
66.47,
146.08,
91.68,
108.96,
113.41,
111.08,
217.74,
78.5,
89.06,
74.8,
80.2,
76.52,
208.99,
99.72,
102.97,
197.36,
98.56,
86.06,
123.08,
75,
105.9,
73.87,
78.04,
107.74,
60.64,
82.57,
115.98,
110.38,
92.59,
82.64,
70.28,
106.54,
59.11,
58.89,
129.66,
73.57,
72.04,
125.63,
77.94,
68.99,
111.76,
65.36,
80.27,
81.73,
79.22,
142.63,
97.05,
70.38,
114.46,
64.62,
94.12,
70.19,
55.35,
115.52,
60.7,
69.24,
103.29,
92.74,
232.64,
82.46,
63.57,
73.24,
78.43,
57.46,
104.16,
110.42,
119.4,
119.13,
120.22,
80.8,
57.96,
81.24,
88.79,
70.91,
87.12,
126.09,
76.42,
56.33,
131.81,
95.23,
71.18,
87.71,
115.68,
207.37,
75.1,
69.72,
111.84,
111.08,
201.96,
68.68,
56.9,
91.16,
99.44,
78.85,
123.89,
90.44,
138.44,
75.06,
85.66,
102.76,
71.79,
167.16,
131.05,
86.92,
59.62,
103.72,
77.1,
94.75,
77.72,
76.09,
61.8,
83.73,
79.33,
108.32,
103.65,
86.19,
131.42,
69.46,
76.78,
103.78,
213.43,
93.52,
115.07,
69.45,
81,
83.16,
108.63,
88.06,
80.57,
248.24,
57.79,
87.4,
76.63,
58.01,
77.43,
64.45,
79.42,
82.94,
81.58,
62.21,
229.94,
116.06,
117.77,
66.69,
122.23,
168.68,
119.9,
110.33,
77.95,
80.35,
57.38,
96.47,
70.66,
202.06,
91.3,
67.56,
126.12,
86.4,
103.22,
88.85,
63.9,
81.32,
78.11,
134.65,
77.94,
56.94,
117.03,
207.84,
228.26,
84.43,
57.09,
94.89,
132.46,
75.13,
76.64,
91.32,
85.9,
76.19,
85.77,
63.42,
112.02,
203.76,
98.44,
79.57,
63.98,
101.46,
74.66,
68.66,
205.78,
111.24,
134.76,
110.1,
100.8,
113.08,
56.48,
77.77,
104.02,
83.59,
179.67,
91.89,
78.9,
230.74,
216.19,
83.7,
200.66,
73.99,
228.5,
70.43,
74.39,
102.92,
87.91,
118.55,
232.29,
125.32,
78.98,
100.2,
200.91,
95.24,
56.64,
80.48,
112.96,
236.04,
73,
61.13,
88.56,
66.16,
88.98,
94.39,
87.39,
77.91,
86.85,
94.37,
76.64,
254.95,
107.33,
80.94,
112.02,
196.58,
89.14,
69.94,
55.79,
73.08,
96.02,
189.82,
89.68,
101.06,
62.66,
84.03,
108.38,
78.79,
76.12,
68.01,
90.11,
193.61,
195.74,
94.77,
85,
141.37,
90.71,
106.8,
121.66,
85.18,
130.07,
74.24,
79.98,
106.76,
76.34,
221.24,
80.55,
63.98,
55.93,
73.94,
65.58,
83.8,
95.87,
112.83,
84.93,
103.08,
85.98,
83.75,
125.43,
101.58,
149.8,
112.55,
70.87,
92.96,
112.19,
85.08,
112.37,
151.26,
73.81,
77.2,
76.08,
123.98,
108.51,
98.07,
85.82,
86.21,
99.01,
80.08,
69.47,
85.07,
88,
144.14,
78.8,
86.78,
105.72,
64.37,
146.1,
58.66,
192.5,
103.5,
89.42,
212.92,
60.73,
73.18,
82.26,
87.4,
86.95,
74,
73.98,
124.78,
191.94,
247.87,
83.2,
88.62,
81.51,
115.71,
229.73,
57.4,
100.47,
81.36,
261.67,
110.2,
98.9,
95.19,
105.28,
83.03,
89.41,
70.56,
78.79,
70.13,
62.68,
71.77,
77.99,
256.74,
93.72,
63.74,
60.67,
112.08,
221.08,
101.81,
60.17,
65.29,
153.24,
61.42,
86.67,
82.42,
100.54,
111.48,
85.33,
126.57,
81.51,
67.97,
77.92,
80.54,
76.47,
87.15,
86.07,
106.52,
94.24,
89.73,
208.39,
128.61,
57.56,
227.23,
77.76,
72.76,
90.11,
203.27,
234.5,
71.12,
190.67,
116.04,
82.95,
112.75,
82.63,
92.75,
117.63,
197.06,
216.07,
76.03,
62.44,
108.82,
101.66,
121.15,
116.44,
93.3,
144.33,
98.02,
80.67,
94.27,
76.35,
57.56,
179.14,
63.49,
145.18,
66.3,
120.56,
69.2,
150.74,
81.38,
78.48,
203.87,
87,
84.68,
91.89,
88.97,
116.98,
135.82,
96.62,
106.53,
58.42,
79.62,
141.84,
78.05,
77.16,
86.3,
83.55,
102,
68.07,
81.77,
82.62,
102.03,
235.06,
76.98,
82.31,
83.84,
98.14,
84.04,
92.24,
94.92,
112.7,
74.34,
72.99,
104.9,
99.96,
85.27,
60.32,
94.14,
195.43,
200.25,
90.31,
95.33,
85.65,
223.64,
76.43,
98.66,
75.84,
85.46,
81.33,
199.78,
176.42,
101.56,
84.48,
93.21,
72.88,
84.1,
72.84,
70.89,
108.14,
244.3,
88.04,
65.84,
106.73,
106.03,
80.88,
140.1,
127.4,
87.72,
58.23,
69.37,
58.64,
59.67,
78.08,
105.08,
89.3,
61.38,
56.11,
75.53,
107.47,
76,
90.28,
65.22,
86.24,
166.38,
78.91,
226.28,
90.73,
172.86,
213.92,
92.23,
79.81,
81.78,
102,
68.42,
94.68,
125.33,
212.19,
89,
60.98,
58.19,
67.68,
84.99,
62.13,
57.1,
97.95,
94.06,
93.88,
90.3,
83.14,
118.66,
100.47,
90.78,
66.46,
73.56,
200.8,
57.28,
92.87,
96.47,
99.44,
77.96,
60.13,
88.17,
84.41,
72.34,
68.72,
222.58,
66.06,
156.43,
105.76,
73.07,
99.48,
79.6,
70,
112.98,
61.07,
63.43,
73.2,
102.73,
74.88,
122.43,
84.21,
80.42,
80.44,
80.63,
104.12,
96.82,
88.1,
64.85,
102.87,
83.75,
97.68,
97.58,
74.16,
86,
100.93,
232.78,
86.96,
67.5,
81.21,
110.14,
120.44,
187.47,
94.62,
89.57,
95.31,
75.07,
106.65,
234.06,
144.15,
58.81,
242.62,
93.04,
150,
63.56,
96.98,
66.7,
79.66,
90.16,
104.09,
90.96,
152.38,
114.54,
174.54,
106.47,
73.63,
98.52,
62.93,
67.87,
120.43,
76.56,
108.03,
83.16,
67.9,
115.46,
123.94,
231.54,
219.8,
187.99,
234.45,
75.47,
98.24,
66.2,
94.67,
75.95,
111.79,
80.89,
86.91,
59.36,
66.67,
87.54,
139.87,
67.92,
83.62,
110.97,
70.53,
115.91,
59.74,
62.64,
84.81,
78.68,
217.57,
79.76,
63.28,
78.48,
70.48,
120.96,
77.29,
108.51,
113.85,
97.65,
121.83,
81.53,
58.35,
65.42,
69.34,
109.09,
66.71,
114.92,
120.58,
165.36,
66.11,
126.34,
68.27,
92.86,
96.77,
89.24,
89.32,
65.63,
123.66,
63.82,
108.06,
136.18,
71.29,
76.58,
119.58,
109.3,
111.94,
81.64,
97.64,
110.32,
79,
95.08,
137.27,
57.83,
84.08,
234.51,
142.64,
69.77,
71.4,
79.61,
77.46,
96.85,
182.22,
67.26,
60.02,
113.45,
112.31,
68.53,
57.15,
214.73,
91.82,
71.8,
63.41,
106.24,
83.01,
67.92,
74.99,
75.22,
61.88,
65.67,
110.18,
119.3,
208.69,
95.18,
107.72,
83.27,
90.29,
81.87,
153.31,
101.46,
72.49,
86.58,
113.26,
84.92,
128.17,
93.93,
99.87,
129.31,
89.43,
101.6,
66.55,
231.72,
96.69,
102.13,
100.88,
66.22,
109.33,
206.53,
83.91,
67.92,
73.48,
101.31,
103.66,
70.73,
77.45,
193.8,
114.34,
69.25,
88.44,
84.09,
87.06,
92.64,
99.36,
135.63,
82.44,
81.88,
95.44,
72.71,
65.45,
203.01,
110.84,
83.12,
55.12,
105.91,
61.57,
83.1,
66.32,
72.56,
97.99,
63.27,
79.6,
177.56,
81.31,
59.91,
132.85,
198.84,
77.12,
87.17,
96.03,
83.56,
80.93,
84.54,
70.51,
56.87,
67.55,
243.52,
108.63,
69.06,
73.89,
84.49,
73.71,
129.01,
72.96,
110.23,
79.49,
89.33,
68.38,
135.79,
82.19,
93.2,
208.31,
111.33,
69.84,
121.71,
80.08,
84.4,
98.61,
86.61,
80.44,
97.23,
91.21,
103.69,
74.12,
71.08,
58.39,
176.34,
73.76,
67.21,
87.98,
94.23,
111.36,
84.07,
117.31,
68.86,
132.88,
211.83,
124.92,
57.42,
215.69,
154.67,
67.66,
153.48,
62.63,
77.93,
80.3,
106.69,
83.07,
140.14,
92.65,
111.37,
99,
78.24,
83.88,
86.21,
57.06,
112.25,
81.06,
104.37,
124.31,
101.95,
116.95,
99.76,
105.48,
100.6,
65.05,
75.9,
267.6,
109.03,
225.35,
196.26,
95.42,
123.04,
85.88,
107.43,
56.85,
102.15,
88.68,
182.52,
93.97,
104.03,
67.99,
83.37,
124.66,
212.87,
96.75,
63.28,
82.64,
71.34,
183.43,
101.19,
88.51,
126.85,
74.17,
76.04,
73.87,
99.07,
82.93,
133.24,
69.88,
69.12,
86.05,
185.27,
97.46,
81.9,
99.58,
124.54,
153.34,
112.35,
160.76,
73.29,
108.08,
206.49,
77.42,
117.59,
61.94,
116.68,
253.86,
203.36,
125.87,
64.09,
65.33,
96.84,
175.92,
100.65,
112.39,
81.1,
98.3,
89.68,
95.16,
91.04,
101.87,
96.86,
91.05,
55.84,
84.04,
92.06,
89.61,
69.92,
89.29,
120.23,
60.22,
223.78,
211.58,
179.38,
193.88,
89.01,
118.41,
138.51,
120.03,
91.01,
61.98,
135.32,
58.72,
90.42,
104,
102.5,
174.37,
106.56,
76.31,
77.28,
90.52,
108.35,
72.03,
90.57,
97.04,
83.89,
152.56,
75.93,
76.89,
62.13,
100.85,
95.05,
82.35,
82.38,
79.15,
76.62,
91.68,
71.88,
71.97,
97.95,
77.93,
82.85,
103.08,
125.2,
82.99,
166.29,
85.28
],
"yaxis": "y2"
},
{
"boxmean": "sd",
"marker": {
"color": "#EBDBBC",
"line": {
"color": "#000000",
"width": 1
}
},
"showlegend": false,
"type": "box",
"xaxis": "x3",
"y": [
36.6,
32.5,
34.4,
24,
29,
27.4,
22.8,
24.2,
29.7,
36.8,
27.3,
28.2,
30.9,
37.5,
25.8,
37.8,
22.4,
48.9,
26.6,
32.5,
27.2,
23.5,
28.2,
28.3,
44.2,
25.4,
22.2,
30.5,
29.7,
26.5,
33.7,
23.1,
32,
29.9,
23.9,
28.5,
26.4,
20.2,
33.6,
38.6,
33.7,
39.2,
27.7,
31.4,
36.5,
33.2,
32.8,
27.7,
40.4,
22.2,
25.3,
30.2,
24,
47.5,
20.3,
30,
28.9,
28.1,
31.1,
27.4,
26.4,
21.7,
27,
24.1,
45.9,
44.1,
22.9,
29.7,
29.1,
27.3,
32.3,
41.1,
25.6,
29.8,
26.3,
37.5,
26.2,
29.4,
32.3,
24.4,
31.4,
27.7,
28,
28.8,
31.4,
34.6,
19.4,
28.5,
30.3,
40.4,
24.2,
41.5,
22.6,
24.2,
56.6,
27.1,
30.9,
27.3,
31.3,
24,
31,
28,
30.3,
31.7,
35.8,
28.4,
24,
29,
36.5,
20.1,
36.5,
26.7,
38.7,
29.9,
34.9,
27,
26.6,
25,
23.8,
21.8,
36.8,
30,
27.5,
24.6,
32.9,
26.1,
31.9,
34.1,
27.5,
25.6,
36.9,
31.4,
37.3,
34.1,
25,
45.7,
34.2,
23.6,
27.3,
22.3,
31.4,
26.4,
32.9,
37.1,
45,
25.5,
26.1,
30.8,
32,
29.9,
37.4,
31.7,
34.5,
27.9,
29.5,
46,
42.5,
35.5,
31.1,
26.9,
35.8,
45.5,
28.5,
26.6,
31.5,
32,
30.8,
31.1,
33,
23.4,
26.9,
33.6,
23.9,
26.3,
27.3,
30.7,
20.5,
21.5,
31,
27.1,
40,
28.6,
28.1,
28.4,
42.2,
25.8,
31.9,
31,
27.5,
29.6,
35.4,
16.9,
21.5,
34.4,
28,
26.8,
39.3,
31.7,
32.6,
28.4,
35.9,
21.2,
34.5,
42.4,
40.5,
36.7,
30.9,
29.3,
19.6,
18,
39.2,
17.6,
35.9,
19.1,
50.1,
17.7,
27,
32.3,
54.6,
35,
22,
39.4,
26.1,
42.4,
33,
19.7,
22.5,
24.6,
25.2,
41.8,
60.9,
31.5,
27.3,
23.7,
24.5,
28.4,
26.9,
26.7,
31.2,
25,
25.4,
27.5,
16,
27,
31.6,
25.1,
30.9,
24.8,
23.4,
29.4,
18.3,
20,
19.5,
36,
27.7,
27.5,
28.5,
26.8,
33.6,
29.1,
28.5,
34.9,
25.1,
35.3,
26.4,
31.5,
40.1,
43.1,
36.7,
29.3,
21.2,
31.2,
21.4,
27.9,
34.3,
31,
27.7,
36,
38.7,
27.6,
25.1,
16.5,
22.8,
35.4,
24.3,
34.3,
40.1,
25.7,
21.9,
38.4,
26.1,
30.5,
25.9,
54.7,
29.9,
18.6,
27.1,
24.9,
25.2,
19.4,
29,
48.2,
34.6,
24.6,
25.8,
26.1,
29,
27.2,
20.7,
30,
37.3,
34.1,
23.6,
25.2,
39.5,
23.5,
23.3,
64.8,
28.1,
24.4,
29.8,
24.9,
35.1,
32.3,
43.6,
21,
47.3,
16.6,
37.5,
24.2,
31.6,
21.6,
31,
31.1,
15.5,
27.3,
20.5,
35.6,
16.7,
41.8,
41.9,
16.4,
17.1,
29.2,
27.1,
37.9,
44.6,
22.8,
33.2,
22.3,
26.4,
39.6,
28.1,
39.2,
36,
37.8,
40.3,
41.5,
17.7,
21.2,
41.6,
23.8,
23.7,
24.8,
39,
37.9,
31.1,
23.2,
18.9,
36.1,
36.3,
40.5,
25.4,
46.5,
16.8,
46.6,
26.4,
26.2,
35.2,
20.9,
36.8,
34.4,
22.2,
13.8,
40.3,
28.4,
31.7,
34.2,
54.7,
24.6,
36.9,
31.1,
31.9,
31.8,
18,
28.5,
29.5,
22,
29.4,
28.8,
26.2,
26.9,
23.2,
27.9,
36.8,
28.9,
31,
29.4,
15.3,
37.1,
30.5,
38.2,
23.2,
30.2,
45.2,
21.8,
24.4,
17,
19.5,
49.8,
27.8,
25.1,
26.8,
60.2,
27.5,
28.1,
27.3,
27.4,
22.2,
22.9,
26.6,
23,
32.6,
22.1,
22.5,
25.5,
31.4,
26,
20,
31.6,
31.2,
21.7,
24.2,
25,
36.7,
28.9,
29.7,
44.3,
51,
39.7,
34.7,
35,
21.3,
29.1,
23.9,
36,
41.2,
27.1,
33.2,
25.4,
30.7,
34.8,
19.2,
31.7,
35.7,
37.8,
29.7,
35.8,
23.6,
39.7,
40.5,
21.4,
40.8,
24.7,
21,
45,
26.2,
28.3,
41.6,
19,
32.4,
34,
39.4,
28.7,
31.8,
31.2,
20.9,
32.1,
31,
23.1,
26.7,
27.9,
27.3,
51.5,
20.4,
29.6,
30.6,
33.6,
71.9,
24.2,
17.7,
22.6,
28.1,
26.5,
28.7,
39.5,
35.1,
27.9,
19.3,
28.4,
26.7,
40.9,
17.2,
28.3,
16.1,
27.6,
16.5,
35.8,
16.2,
24.6,
32,
35.3,
19.2,
40.4,
30.7,
24.3,
26.4,
34.7,
31.7,
35.6,
22.8,
28,
35.6,
40.6,
29.3,
21,
20,
26.7,
18.4,
34.5,
27.7,
21.1,
24.4,
19.4,
42.3,
32.2,
26.8,
25.4,
23.5,
50.2,
26.1,
17.5,
24.2,
30.8,
23.4,
30.9,
23.6,
18.7,
27.7,
16.7,
31.2,
17,
29.8,
19.7,
29.1,
27.2,
22.3,
27,
42.1,
34.2,
40.9,
29.4,
32.8,
21.9,
39.6,
28.3,
47.8,
39.3,
28,
27.1,
31.2,
23.1,
20.8,
34.1,
30.1,
35.8,
34.6,
29.8,
26.7,
30.2,
29.7,
54.6,
23.3,
35.6,
27,
21.6,
29.4,
22.8,
17.3,
29.8,
36.4,
34.7,
28.7,
26.7,
22.1,
27.7,
40.5,
23,
25.3,
22.1,
28,
12,
28.4,
29.6,
36.2,
22.8,
55.7,
24.3,
26.9,
25.3,
35.3,
18.3,
26,
21,
55.7,
27.6,
20.5,
30.2,
21.9,
28.8,
36.2,
25.9,
21.4,
20.4,
31.6,
14.4,
23.7,
30.2,
19.5,
32.6,
34.2,
43,
42.2,
19.7,
41.7,
21.6,
24.2,
19.2,
25.8,
23.2,
20.8,
28.4,
30.2,
23.1,
16.7,
39.5,
33.8,
34.6,
25,
43.9,
27.1,
25.9,
22.7,
27.1,
25.6,
28.4,
57.5,
35.8,
19.5,
31.2,
43.6,
31.2,
23.5,
18.7,
24.4,
29.4,
37,
29.4,
38.5,
23.5,
16.3,
35.9,
35.9,
20.3,
32.3,
27.9,
22.3,
31.8,
29.7,
27.1,
24.5,
28.9,
24.6,
31.6,
32.3,
41.1,
30,
26.4,
30,
20.8,
44,
30.6,
17.2,
29.1,
27.4,
23.5,
31.8,
23.5,
28.5,
32.7,
54.2,
25.6,
41.2,
27,
21.3,
34.3,
29.5,
31.6,
26.1,
27.5,
26.5,
33.2,
40.2,
32.5,
23.4,
32.5,
23.9,
29.5,
24,
17.7,
26.2,
33.3,
17.4,
29,
21.7,
37.8,
41.8,
24.2,
31.1,
23.1,
25.1,
41.3,
22.7,
24,
20.5,
20.4,
27.6,
27,
26.4,
34.9,
35,
28.5,
32.3,
23.9,
52.3,
26.4,
20.9,
23.3,
32.7,
26.5,
27.9,
30.3,
27.6,
14.6,
40.9,
28.4,
23.7,
27.9,
25.2,
34.4,
36.7,
22.2,
27.2,
27.3,
27.3,
42.2,
26.4,
39.4,
34.8,
20,
34.1,
31.4,
17.8,
46.1,
28.1,
24.7,
22.7,
34.6,
21.4,
27.4,
36.6,
32.9,
24.7,
21.4,
33.1,
26.7,
24.4,
25.8,
34.3,
18.1,
43.8,
26.9,
36.6,
24.9,
27.6,
20.9,
30.3,
37.4,
35.9,
50.3,
31.5,
24.4,
38.9,
28.6,
27.5,
43.7,
27,
29.3,
34.7,
39.9,
26.7,
24.8,
29.7,
15.9,
31.4,
27.8,
35.5,
31.7,
33.2,
27.7,
31.9,
25.8,
27.7,
23.4,
24.3,
30.3,
29.1,
20.1,
21.2,
31.6,
41.6,
36.4,
30.5,
32.8,
32.3,
30.1,
34.7,
29.7,
31.3,
35.6,
35.2,
27.6,
35.2,
28.1,
31.8,
23.3,
28.8,
19.5,
30,
25.5,
28.8,
40.2,
32.9,
22.4,
36.9,
19.8,
12.3,
32.4,
24.8,
16.4,
35.9,
23,
78,
30.5,
26.8,
35.3,
27.9,
22.1,
38.3,
41,
22.8,
30.1,
20.8,
31.7,
30.1,
30.8,
42.6,
37.1,
34.2,
43.4,
18.7,
34,
23.2,
41.7,
15.1,
20.6,
18.9,
26.6,
30.1,
32.1,
15.1,
33.5,
23.4,
43.2,
19.1,
26.1,
17.3,
32.1,
30.4,
29.9,
32.8,
22.7,
28.7,
35.2,
22.4,
32.3,
18.6,
42.1,
38,
22.3,
33.4,
23.2,
18,
20.1,
19.2,
28.7,
28.1,
22.6,
18,
21.4,
27.8,
27.6,
32.2,
24.9,
27.1,
24.6,
24.6,
18.9,
16.3,
31.8,
21,
20.1,
29.1,
32.3,
29.4,
50.2,
44.9,
28.5,
19.5,
45,
22.8,
25.5,
31.5,
31.6,
31,
29.5,
23.1,
30.7,
44.7,
33.5,
25.9,
28.3,
26.8,
27.8,
36,
22.1,
38.4,
30.1,
26.2,
26.6,
32.6,
27,
25.3,
31.9,
18.4,
30.4,
28.2,
35.7,
35,
27.2,
23,
25,
24.5,
30.9,
26,
27.2,
30.4,
21.7,
29.1,
17.6,
29.2,
28.1,
26.6,
26.9,
36.2,
25.9,
40.9,
31.5,
23.3,
37.6,
39.8,
35.1,
21.9,
53.4,
34.4,
24,
29.3,
26,
31.3,
31,
26.5,
16,
21.1,
42.2,
27.7,
23.6,
18.3,
44.3,
27.3,
55.2,
44.7,
33.1,
30,
40.1,
23.1,
30.3,
30.5,
22.8,
42,
29.9,
24,
36.7,
22.2,
25.5,
34.3,
16,
25.6,
23.3,
41.6,
35.6,
22,
26.8,
31.9,
19.7,
31.1,
34.4,
33.2,
21.6,
25.4,
41.6,
33.1,
21.8,
30,
35.2,
35.6,
27.8,
29.3,
37.2,
26.1,
21.6,
22.7,
30.2,
18,
27.8,
23.5,
41.2,
22.7,
29.2,
42.2,
24,
45.5,
19.4,
28.2,
20.1,
27.2,
26.5,
17.6,
42.8,
29.3,
21.3,
29.8,
24.1,
35.2,
18.8,
17.4,
31.5,
29.9,
27.3,
28.1,
28.9,
25.2,
24.3,
37.9,
28.2,
18.4,
23.1,
39.4,
29.5,
18.3,
28.7,
32.9,
26.7,
20.1,
17.6,
43.7,
41.1,
26.8,
31.2,
27.4,
25.1,
33,
30.7,
42.9,
14.3,
33.2,
43,
22.3,
32.8,
30.5,
36,
26.5,
20.1,
28.1,
16.2,
30,
31.3,
26.7,
37.7,
35.8,
27,
20.7,
22.2,
41.5,
33.5,
21.8,
22.1,
30.2,
38.7,
32.1,
41.3,
20.1,
22.1,
24.9,
23,
29.3,
22.8,
31.6,
19.4,
22.5,
31.6,
29.1,
43.4,
26.5,
29.7,
21.8,
23.4,
23,
21.5,
14.6,
28.5,
29,
26.9,
29.6,
28.5,
25.5,
30.5,
23,
22.8,
31.4,
35.2,
23.3,
31.2,
24.8,
25.8,
48.4,
43.1,
27.8,
21.4,
22.1,
31.8,
20.1,
29.1,
22.6,
25.1,
33.3,
15.9,
20.8,
19.5,
39,
23.1,
18.8,
50.6,
36.3,
32.8,
35.8,
26.7,
26.3,
26.5,
43.7,
24.7,
46.2,
49.5,
43.3,
30.9,
38.7,
28.6,
30.2,
30.5,
33.9,
28.6,
24.5,
17.2,
27.2,
19.3,
18.5,
32,
44.5,
37,
30.8,
24.7,
18.3,
44,
27.6,
45.4,
28.5,
17.6,
27.5,
32.8,
29.5,
36.4,
26.1,
35.3,
18.1,
27.1,
23,
55,
26.6,
24.1,
30.3,
32.2,
26.5,
30.6,
25.8,
41.9,
29.2,
44.3,
29.1,
17.5,
31.9,
32.2,
26,
25.4,
54.8,
27.9,
32.8,
34,
25.5,
35.6,
28.3,
36.9,
22.1,
31.5,
26.7,
24.9,
32.4,
32.2,
30.3,
28.7,
24.1,
25.3,
24.8,
34.8,
39.2,
24.7,
28.6,
27.7,
33.1,
27.3,
20.3,
33.9,
28.3,
32.4,
19.7,
33.7,
35.5,
20,
16.3,
36.4,
28.1,
26.9,
29.4,
17.2,
26.5,
19.5,
30.6,
31.9,
26.6,
19.9,
18,
26.7,
25.3,
31.4,
21.1,
17.9,
27.5,
37.6,
24.1,
18.1,
31,
26.6,
38.7,
27.2,
27.8,
28.3,
27.5,
43.3,
20.1,
25.9,
25.8,
16.4,
29.4,
28.9,
26.7,
27.8,
38.7,
26.8,
36.1,
26.7,
27,
21.2,
21,
37.4,
26.5,
28.9,
29.5,
17.7,
34.9,
28.1,
36.8,
33.3,
29.2,
34,
33.9,
28.7,
21.1,
38.2,
27.7,
35.8,
27.1,
20.5,
35.6,
15.6,
43,
17.8,
25.8,
20.5,
19.9,
24.4,
18.3,
35.1,
35.1,
37.1,
29.4,
31.7,
31.4,
33.1,
28.5,
24,
18,
27.6,
16.9,
16.4,
35.1,
28.8,
17.6,
21.3,
19.7,
37.3,
35.5,
25.5,
30,
21.9,
45.5,
34.9,
19.3,
32.1,
26.9,
32.5,
19.5,
31.7,
21,
20.3,
21.5,
40.4,
30.1,
40.2,
34.7,
25.9,
23.7,
34.3,
34.3,
26.3,
22.1,
33.3,
37.3,
20.5,
19.1,
34.7,
16.3,
28,
17.2,
41.1,
25,
27.5,
21.5,
15.1,
29.6,
30.2,
34.9,
30.7,
36.5,
21.5,
33.2,
50.2,
22.3,
27.3,
28.3,
31.9,
24.9,
32.7,
26.4,
24.8,
30.7,
25.4,
32,
24.4,
20.4,
29.5,
24.4,
22.8,
20,
36.3,
32.8,
34.4,
30.5,
29.7,
36.9,
34.2,
37,
47.5,
24.2,
30.6,
52.8,
38.6,
32.8,
42.9,
25.9,
24.7,
35.8,
31.2,
26.1,
30.8,
40.5,
25.2,
28.7,
30.4,
15.2,
36.6,
34.5,
23.7,
35.7,
38.9,
40,
18.4,
29,
20.6,
28.2,
66.8,
26.2,
34.8,
34.5,
30.6,
55.1,
18.8,
29.1,
22.1,
28.9,
34.7,
20.3,
25.1,
18.2,
25.5,
34.4,
48.5,
25.2,
32.5,
42.1,
19.5,
27.7,
25.1,
29.6,
32.9,
55.9,
25.1,
24.7,
17.5,
20.8,
23.3,
22.8,
26.6,
25.5,
25.8,
25.4,
57.3,
20.2,
27.7,
23.1,
14.3,
23.3,
23.4,
22.8,
30.9,
28.4,
41.9,
34.3,
25.1,
10.3,
31.8,
31.8,
23,
25.4,
14.1,
35.1,
26.9,
33,
22.1,
20.8,
30,
44,
16.6,
28.4,
26.3,
39.4,
15.7,
34.1,
29.8,
23.6,
18.3,
28.3,
18.5,
18.4,
24.1,
25.5,
25.3,
39.9,
28.8,
37.2,
49.8,
34.3,
29,
37.2,
19.4,
26.2,
21.1,
31.6,
29.3,
28.4,
35.3,
40.1,
39,
31.5,
28.5,
28.5,
56,
28.6,
35.2,
32.5,
35.9,
25.1,
16.8,
27.9,
16.3,
21.4,
32.7,
23.6,
19.8,
33.1,
28.1,
38.9,
23.8,
33.8,
26.7,
32.2,
28.1,
21.3,
44,
21.5,
37.3,
44.8,
21.4,
32.3,
35.4,
19.7,
31.3,
35.5,
25.8,
32.8,
35.9,
41.8,
27.3,
27.7,
13.4,
17,
20,
40.9,
38.5,
30.9,
33.4,
17.1,
28.6,
24,
32,
28.4,
20.3,
33.8,
43.2,
22.7,
29.9,
29.9,
22.7,
16.1,
33.8,
22.6,
25.5,
22.5,
27.9,
23.5,
32.2,
29.9,
28.7,
37.2,
22.2,
26.6,
24.6,
41.7,
26.2,
27.1,
41.8,
34.2,
19.2,
25.2,
18.7,
43,
28.2,
34.7,
25.5,
39.8,
20.8,
28.2,
31,
21.6,
26.2,
26,
22.5,
24,
31.5,
36.3,
25.4,
32.2,
39.8,
18.6,
30.7,
16.4,
20.5,
28.7,
29,
24.4,
29.9,
25.3,
23.5,
20.5,
33.5,
22,
29,
30.7,
37.4,
51.8,
16.6,
29.5,
27.3,
27.5,
33.9,
27.9,
22.9,
32.1,
18.2,
30.2,
31.3,
20.3,
23.2,
30.8,
22.9,
42.8,
22.2,
28.7,
32.3,
24.2,
23.9,
34.2,
29,
29.9,
23,
17.3,
21,
34.3,
19.8,
24.3,
16.4,
32.6,
23.7,
27,
25.6,
34,
24.9,
26.3,
43.1,
39.6,
15.2,
41.8,
20.1,
39.3,
28.3,
27.5,
33.9,
20,
34.8,
20.7,
24.6,
39.4,
29.4,
24.8,
32.3,
25.3,
39,
29.4,
31.9,
26.4,
20.6,
26.3,
23.6,
18,
26.4,
22,
30.5,
29.1,
24.9,
36.4,
31.2,
32.8,
31.6,
35.8,
35.2,
28,
24.9,
35.7,
30.7,
33.9,
27.8,
23.2,
17.1,
31.6,
23.8,
29.6,
45,
25.6,
27.7,
25.5,
18.3,
21,
28.6,
23.5,
36.1,
29.2,
27.6,
36,
20.4,
19.9,
29.2,
27.9,
38.1,
24.1,
37.8,
29.1,
35.7,
34.5,
34.1,
21.1,
57.7,
22.2,
20.5,
26.2,
23.5,
29.1,
39.8,
21.2,
20.2,
30.8,
33.8,
17.1,
21.3,
22.3,
28.9,
26.7,
41,
16.7,
29.1,
23.6,
27.8,
31.4,
28.2,
30.8,
36.1,
32.7,
26.4,
20.7,
18.5,
33.5,
14.1,
23.4,
23.2,
36,
35.6,
21.8,
34.9,
20.4,
25,
22.3,
27.3,
28.9,
30.4,
36.9,
27.7,
44.4,
28.2,
35,
20.6,
26.5,
25.1,
22.2,
27.3,
27.3,
41.9,
38.8,
18.3,
19.5,
17.2,
20.8,
23.8,
24.7,
23.1,
22.4,
29.9,
20,
28.7,
26.2,
29.6,
30.8,
32.7,
21.8,
35.7,
25.4,
32.8,
21.1,
31.7,
20,
34.7,
28,
16.4,
24.6,
24.6,
30.3,
23.7,
33.5,
24.9,
48.9,
31.4,
18.4,
24.2,
34.3,
25.1,
33.3,
22.6,
26.3,
29.7,
18.5,
21.4,
45.5,
34.2,
32.7,
38.1,
26.7,
22.6,
27.7,
20.7,
25.7,
17.3,
23.3,
16.2,
20.5,
33.6,
20.2,
49.3,
26.2,
30.1,
25.1,
32.7,
26.4,
21,
24.1,
20.4,
30.9,
39.1,
27.7,
29.5,
30.3,
37.8,
30.2,
24.2,
25.2,
22.2,
17.7,
29.6,
32.2,
28.7,
19.5,
38.5,
20.6,
21.5,
43.8,
18,
28.2,
26.6,
35.5,
30.7,
18.7,
22.4,
25.2,
24.2,
20.5,
24.5,
24.8,
42.6,
23.8,
42.3,
42.2,
26.1,
22.8,
42.3,
28.5,
23,
32.8,
49.8,
23.8,
31.7,
34.9,
54,
26.8,
21.3,
19.8,
42.4,
16.4,
28.2,
26.3,
17,
23.3,
56.1,
25.1,
35.3,
36.3,
30.4,
43.9,
26.4,
28.3,
42.3,
38,
32.7,
28.8,
38.5,
30.1,
31.3,
28.4,
26.6,
20.4,
34.7,
32.5,
30.7,
29.6,
39.4,
26.1,
27.2,
41.3,
20.9,
41.5,
31.1,
24.5,
23,
24.7,
19.3,
37,
31,
33.1,
26.6,
27.7,
40.3,
19.5,
26.7,
26.1,
38.2,
21.3,
97.6,
40.9,
27.3,
20.9,
29.2,
17.9,
38,
26.1,
53.9,
22.2,
24.6,
33.4,
34.4,
17.6,
19.2,
31.8,
28.1,
32.1,
17.4,
20.2,
23.4,
39.5,
21.5,
24.3,
15.3,
32.4,
16.1,
34.3,
43.8,
19.6,
36.7,
33.7,
38.9,
28,
23.9,
16.6,
42.9,
17.1,
27.2,
30.3,
33.9,
20,
19.4,
21.5,
28.5,
34.4,
43.7,
13.7,
33.7,
18,
27.9,
21.8,
35.8,
23.1,
30.3,
32,
25,
30.7,
24.3,
11.5,
30.7,
33.7,
26.5,
17.6,
22.5,
18.5,
22.4,
25.3,
37.3,
39.7,
28,
41.4,
25.3,
28.1,
41.2,
28.8,
31,
28.3,
23.9,
44.2,
27.6,
35.7,
37.4,
27.5,
23.4,
25.8,
22.8,
19.5,
38,
23.4,
29.5,
28.2,
14.2,
28.9,
31.4,
32.4,
22,
26.4,
26.1,
37.6,
18.8,
23.8,
49.4,
23.8,
20.4,
40.3,
23.6,
34.3,
32.7,
28.7,
24.5,
24.8,
23.2,
23,
39.3,
20.1,
25.4,
23.8,
28.4,
15.4,
28.2,
26,
41.5,
32.2,
25.7,
33,
35,
27.4,
16.4,
31.5,
29.2,
25.9,
32.3,
30.4,
45.1,
25.9,
36.6,
18.6,
27.5,
31.3,
37.5,
23.1,
35,
27,
29.8,
23.5,
48.5,
31.8,
20,
29.4,
24.3,
45.4,
29.5,
49.2,
29.4,
29.1,
38.8,
23.4,
48.7,
19.7,
20.3,
33.5,
36.7,
24.4,
38.8,
44.2,
33.4,
32.5,
15.1,
21.5,
30.4,
33.4,
18.2,
18.7,
38.4,
36.4,
20.6,
48.9,
18.2,
17,
31.8,
33,
24.7,
28.1,
40.3,
35.2,
31.2,
37.6,
38.7,
28.1,
29.2,
31.1,
24.1,
18.7,
39.6,
15.7,
53.8,
32.4,
42.7,
18.6,
31.4,
33.7,
24.5,
33.7,
35.4,
22.5,
33.6,
29.7,
25.7,
20.2,
22.7,
17.4,
22.2,
23.8,
28.7,
33.1,
28,
16.7,
27.1,
26.4,
23.7,
33.3,
18.9,
25.5,
35.9,
23.2,
22.2,
34,
20.4,
34.5,
31.9,
19.8,
29,
36.7,
21.3,
23.7,
25.7,
34,
30.7,
44.8,
27.7,
23.2,
30.1,
29.3,
29.5,
25.8,
26.2,
28.8,
29.8,
26.5,
27.8,
29,
19.1,
17.1,
24.6,
18.3,
42.8,
46.5,
38.4,
26,
26.8,
21.1,
29,
43,
36.6,
27.5,
40.2,
37.1,
32,
32.2,
20.6,
24.1,
25,
25.2,
27.6,
48.8,
27.6,
39.5,
27.2,
26.3,
28.9,
30,
23.7,
25.5,
24.6,
20.7,
36.4,
23.4,
32.3,
18.8,
15.5,
21.4,
31.7,
29.7,
34.3,
22.1,
21.5,
30.4,
21.6,
30.9,
21.9,
19.2,
30.3,
23,
24.1,
52.7,
27.6,
23.9,
26.9,
23.9,
40.5,
32,
20.5,
44.5,
16.2,
35.8,
24,
33.9,
36,
22.9,
44.5,
17.5,
32.3,
30.3,
33.5,
30.9,
25.5,
30,
32.2,
21.7,
24.7,
28.8,
27,
26,
27.1,
18.5,
18.8,
31.5,
18.1,
24.3,
28,
17.3,
17.2,
26.9,
22.4,
25.8,
21.3,
43,
24.4,
30.1,
21.3,
17.8,
28.4,
23.4,
24.3,
27.8,
30.5,
25.8,
26.7,
28.1,
22.2,
25.1,
20.9,
28.9,
35.9,
29.8,
26.7,
28.4,
26.7,
32.5,
43.8,
28.3,
36.6,
33.1,
27,
23.6,
21.8,
23.5,
28.7,
31.6,
31.9,
25.2,
25.9,
32.5,
28.1,
27.4,
21.2,
26.8,
18.5,
30.6,
19.9,
26.5,
43.8,
31.8,
36.6,
22.9,
18.7,
29.6,
43.6,
20.3,
21.3,
39.2,
52.8,
30.2,
35.3,
37,
32.1,
30.5,
22.8,
34.5,
37.4,
19.5,
55.7,
30.4,
25.3,
23.1,
30.7,
18.8,
28.6,
27.5,
26.5,
40.3,
37.9,
17.1,
53.5,
33.6,
43.4,
30.5,
38.1,
29.9,
32.7,
28.6,
38.5,
22.9,
38,
43.8,
20.8,
26.8,
25.5,
25.4,
26.7,
50.5,
21.3,
34.7,
17.9,
36.3,
18.4,
34.5,
33.4,
19.4,
25.6,
24.5,
30,
28.9,
28.1,
23,
33.5,
34.8,
17.6,
21.1,
29,
15.8,
41.2,
29.7,
40.8,
33.2,
37,
24.5,
34.5,
28,
26.8,
21,
23.8,
29.3,
24.4,
22.6,
22.8,
29.7,
32.4,
35.8,
31.1,
31.9,
33.7,
25.6,
19.9,
45.3,
15.5,
28.3,
40.2,
24.3,
24.6,
26.6,
30.9,
23.5,
23.9,
34.2,
34.9,
26.2,
36.2,
22.4,
19.3,
41.7,
46,
26.9,
40.1,
27.7,
29.5,
26,
22.7,
16,
27.7,
33.1,
33.1,
29.7,
34,
33.3,
27,
14.2,
19.4,
15.3,
28.7,
21,
28.9,
20.3,
28,
29.6,
30.2,
22.2,
28.4,
29.6,
28.6,
32.6,
29,
30.1,
31.2,
20.7,
45.1,
14.8,
37.4,
19.9,
16,
27.4,
33.4,
18.5,
33.4,
20.8,
22,
32.5,
19.2,
32.9,
23.5,
21.7,
29.1,
30.3,
29.4,
30.1,
32.9,
34,
38.6,
29.3,
43.6,
22.7,
36.6,
22.6,
25.6,
31.4,
26,
27,
33.3,
17.3,
26.9,
24.8,
20,
35.8,
36.9,
29.6,
31.1,
25.1,
51.9,
36.3,
28.2,
35.8,
30.9,
22.1,
17.6,
29.5,
27,
33.2,
24.4,
34.4,
36.7,
27.2,
27.5,
32.6,
28.4,
25.5,
28.7,
23.8,
34.4,
26.1,
28.5,
29.6,
29.4,
28.2,
28.7,
38.1,
22.2,
25.7,
17.7,
26.4,
32.8,
34.5,
39.5,
23.5,
38.8,
21.2,
32,
63.3,
32.6,
25.2,
27.4,
38.6,
32.9,
26,
29.2,
29.2,
26.4,
23.1,
21.2,
26.2,
23.4,
19.8,
20.4,
25.9,
34.1,
18.3,
35,
26.4,
27.6,
27,
31.3,
18,
22.1,
34.4,
33.2,
23.1,
39.7,
40.7,
30.9,
27.5,
28.3,
33.4,
33.1,
41.5,
17.1,
30,
24.9,
32.5,
26.1,
19.2,
38.7,
29.5,
28.1,
14.1,
33.9,
26,
52.8,
29.2,
26.3,
23.6,
18.6,
29.7,
30.9,
22.8,
25.5,
25.6,
23,
20.6,
28.4,
29.7,
26,
37.5,
42.3,
21.4,
29.3,
31,
22,
20.9,
45.9,
61.2,
29.1,
28.4,
22.6,
24.2,
16.3,
35.9,
29.8,
37,
35.8,
21.1,
37.6,
24.8,
31.9,
17.5,
17.6,
38.9,
15.2,
27.4,
27.9,
16.1,
39,
36.6,
33.7,
27.6,
31,
28.5,
31.4,
25.3,
17.2,
26.9,
32,
22.5,
17.3,
27,
33.1,
25,
31.1,
19.6,
24.7,
48,
38,
24,
30,
24.6,
23.1,
17.4,
43.1,
27.1,
14.3,
46.8,
26,
29.3,
50.1,
18.6,
27,
25.7,
25.5,
27.3,
24.1,
42.7,
41.2,
20.2,
30.9,
27.7,
22.3,
39.4,
34.2,
20.1,
36.8,
33.9,
27.7,
22.7,
32.2,
22,
23.2,
26.7,
23,
23.4,
24.1,
31.5,
26.1,
23.7,
20.8,
18.8,
32.5,
24.3,
24.1,
32.5,
39.3,
23.9,
22,
37.3,
33,
24.7,
32.8,
29.8,
24.1,
21.6,
27.1,
27,
18.7,
35.8,
27.1,
33.1,
25.9,
26.7,
22.2,
26,
23.5,
23.5,
15.9,
24.9,
26.5,
35.7,
32.1,
28.5,
40.1,
26.6,
21.7,
28.8,
32,
31.4,
32.1,
19.8,
25.3,
28.8,
26,
30.7,
43.3,
35.2,
20.9,
35.3,
27,
26.1,
29.1,
32.6,
21.8,
32.3,
28.1,
32.3,
21.7,
25,
28.9,
21.7,
45.3,
40.2,
22.5,
21.3,
26.1,
28.8,
32.4,
20.4,
25.9,
38.8,
30.8,
24.6,
20.6,
38.8,
40.4,
36.2,
18.8,
27.6,
28.3,
39.4,
33.5,
40.4,
25.9,
27.6,
44.7,
30.8,
22.3,
27.9,
22.9,
27.3,
25,
48.3,
22.7,
39.1,
27.8,
36.7,
38.5,
29.4,
30.5,
23.6,
20.6,
31.7,
24.9,
33.4,
22.2,
26.5,
27.1,
30.3,
18.6,
26.4,
17.8,
37.4,
32.1,
38.2,
39.1,
29.5,
28.6,
40.1,
31.8,
34.6,
24.5,
26.2,
33,
22.3,
58.1,
36.9,
43.9,
18.2,
23.1,
32.6,
41.2,
28.6,
26,
36.8,
35.8,
24.2,
22.9,
23.1,
28.9,
26.5,
29.3,
24.8,
20.1,
18.2,
34.7,
24,
29,
27.3,
27.6,
20.4,
17.7,
27.9,
41.5,
27.4,
27.8,
17.8,
32,
37.3,
28.1,
29.1,
22.9,
38.8,
29,
28.6,
29.8,
28.9,
29.1,
16.7,
34.1,
25.7,
30.8,
23.9,
18.8,
41.8,
36.4,
28.7,
28.3,
22.4,
15.1,
22.7,
26.6,
20.8,
23,
35.8,
29.9,
43.4,
36.3,
40.1,
35.9,
32.4,
20.3,
33.2,
22.3,
18.1,
28.2,
29.7,
29,
25.4,
26.5,
22.7,
27.5,
23.8,
35.6,
27.2,
30.4,
21.5,
31.8,
29.5,
30.6,
27.6,
23,
24.8,
28,
22.2,
27.8,
30.2,
26.8,
22,
34.3,
29.2,
30.3,
21.4,
26.6,
30,
33.7,
30.9,
31.1,
23,
20.2,
31.6,
23.9,
26.1,
15.8,
28.7,
28.9,
25.1,
19.8,
30.8,
22.1,
20.4,
31.2,
24.9,
27,
20.1,
24.4,
22.6,
22.4,
26.2,
22.9,
32.6,
31.3,
43.9,
41.8,
37.7,
20.8,
21.6,
22.1,
39.2,
36.7,
32,
25.9,
28.9,
29.3,
32.3,
33.2,
31.6,
20.6,
23.3,
30.8,
23.7,
49.3,
30.3,
24.4,
28.2,
33.8,
42.6,
31.9,
34,
19.8,
30.5,
21.7,
24.6,
38.1,
19.2,
30.1,
23.3,
34.5,
30.8,
43.8,
29.9,
50.4,
22.6,
19.5,
33.4,
24.2,
52.7,
25.6,
26.9,
35.2,
22.1,
34.4,
26,
26.9,
20.1,
24.7,
32.1,
29.8,
27.5,
22.8,
27.6,
33,
33.7,
25.1,
15.3,
22.2,
21.4,
26,
29.9,
28.6,
21.2,
16,
22.7,
25.3,
31.6,
38.9,
27.1,
24.1,
31.5,
32,
31.8,
34,
23.4,
29.3,
17.2,
31.9,
31.6,
23.2,
30,
26.3,
31.8,
25.5,
21.3,
29.6,
28.7,
30.8,
32.1,
34,
16.9,
32.1,
22.6,
31.3,
29,
27.6,
17.7,
35.9,
33.8,
17.6,
48.3,
39.9,
11.3,
31.5,
29.6,
19.4,
18.1,
36.2,
17.9,
33,
20.4,
33.6,
34.5,
30,
12.8,
26.2,
13.5,
25.6,
18.8,
14.5,
23.4,
39.7,
40.8,
29.5,
33,
25.6,
33.1,
28.1,
49.3,
16.9,
35.9,
32.1,
34.1,
15.1,
42,
16.8,
28.8,
32.2,
16.2,
44.2,
23.4,
31,
28.7,
23.6,
35.7,
39.5,
27.3,
31.3,
33.4,
29.2,
26,
18.1,
22.4,
29.4,
21.6,
27.9,
29.6,
30.2,
43.4,
30.5,
33.2,
27.7,
33.5,
28,
31.5,
24,
27.4,
37.9,
26.2,
22.4,
43.7,
28.4,
29.8,
24.8,
34.8,
27.4,
33.5,
35,
24.4,
28.3,
29.1,
31.3,
32.8,
29.1,
31.9,
14.2,
26.3,
22.8,
24.8,
34.1,
21.5,
26.1,
21.4,
33.5,
23,
18.5,
23.4,
31.4,
31.1,
37.7,
25.3,
35.6,
24.8,
32.7,
24.9,
26,
22.8,
24,
20.3,
30.3,
23.7,
25.7,
21.9,
15.8,
30.5,
25.7,
19,
28.8,
22.4,
32.4,
36.6,
27.9,
27.7,
34.3,
26.7,
19,
20.6,
26.7,
29.2,
21.3,
51.9,
14.1,
28.9,
30.9,
36.1,
25,
28.6,
20.5,
16.7,
36.6,
26.4,
33.1,
27.5,
26.9,
23.9,
26.9,
27.8,
32.7,
28.7,
22.6,
26.5,
28.3,
24.3,
24,
32.4,
28.7,
36.7,
27.7,
24.8,
31.2,
26.1,
26.8,
26.7,
19.6,
25.8,
32.5,
28.3,
24.5,
25.2,
32.3,
30.1,
35.7,
20,
23.5,
23.4,
22.3,
28.7,
20.9,
29.4,
27.6,
43.9,
28.7,
34.7,
37.1,
24.2,
22.7,
29.3,
20,
22.7,
25.4,
37.6,
44.7,
26.3,
25,
32.3,
24.9,
33.7,
26.6,
53.4,
41.7,
22,
20.3,
42.7,
28.2,
32.5,
22.5,
26.5,
41,
27,
25.9,
23.7,
30,
15,
21.5,
25.9,
24.3,
39.2,
14.4,
26.6,
30.7,
35.4,
29.2,
26.7,
21.5,
19.1,
25.4,
34,
23.9,
20.9,
18.2,
27.6,
50.3,
24.5,
16.6,
24,
18.8,
34,
25.8,
30.3,
28.9,
21.3,
26.8,
29.2,
25,
21.5,
17.1,
32.5,
26.9,
30.4,
29.8,
20.3,
41.1,
33.9,
39,
31.2,
38.2,
44.7,
33.5,
27,
31.1,
29.2,
26.6,
17.9,
24.2,
19.5,
28.5,
21.3,
41.3,
31.3,
40.8,
36.6,
26.7,
18.7,
15.2,
26.8,
25.1,
31.3,
59.7,
29.8,
33.5,
24.5,
33.6,
39.7,
47.4,
26.4,
33,
31.4,
27.2,
35.7,
29.5,
21,
26.6,
24.3,
43.9,
52.5,
27,
32.7,
29.4,
15.1,
32.1,
32.9,
27.6,
26.5,
19.2,
25.9,
21.9,
17.4,
13.2,
25.2,
28.2,
31.5,
25.2,
14.5,
28,
15.1,
31.4,
26.3,
39.6,
28.9,
31.4,
34.3,
27,
27.2,
23.8,
25.3,
32.6,
31.8,
22.4,
28,
23.9,
25.3,
16.4,
26.9,
25.4,
18.7,
37.5,
41.4,
37.5,
15.8,
37.6,
30.5,
22.1,
19.2,
38,
18.7,
25.7,
30.1,
36.3,
20.4,
20.9,
28.4,
33.8,
24,
43.3,
36.4,
37.9,
32.6,
23.9,
18.1,
36.4,
29.2,
30.8,
23.8,
17.9,
31.8,
19.5,
31.5,
24.1,
35.9,
31.6,
19,
18.8,
33.5,
25,
52.9,
24.8,
24.5,
40.4,
20.2,
31.9,
25.4,
15.4,
38.6,
31,
26.4,
37.7,
35.2,
54.7,
21.6,
23.9,
26.1,
39.7,
29.9,
31.4,
34.5,
29.4,
26.9,
22.4,
20.5,
18.6,
31.4,
18.5,
41.8,
21.2,
28.6,
16,
30.6,
29,
28.6,
34.8,
42,
30.3,
30.9,
32.1,
19.6,
32.8,
31.5,
34.7,
21.5,
27.6,
28.6,
21.1,
23.8,
33.5,
33.4,
23.3,
21,
21.2,
18.4,
26.9,
20.2,
21.9,
24.2,
29.6,
38.1,
22,
29.8,
24,
26.9,
38,
16.9,
23.4,
38.5,
21.6,
15.9,
25.1,
41.1,
21.5,
21,
24.1,
30.6,
19.8,
29.2,
33.7,
31,
21.5,
19.1,
21.4,
24.8,
35.1,
28.6,
35.7,
45.4,
34.5,
21.2,
22.7,
39.1,
21.9,
16.5,
23,
22.5,
20.4,
28.7,
22.4,
26.3,
23.6,
25.9,
30.7,
24.5,
24,
20.1,
38,
26.6,
24.5,
41.3,
34.8,
35.5,
24.1,
19,
16.6,
28.6,
22.4,
38.6,
32,
40.1,
32,
23.9,
25,
28.5,
19.8,
25.7,
28,
37.2,
24.8,
39.2,
61.6,
27.2,
38,
36.2,
16.6,
38,
31.4,
35.3,
35.8,
28.6,
34,
15.5,
31.1,
37.5,
37.7,
24.2,
33.4,
33,
25.7,
19.4,
33.4,
18,
40.1,
26.9,
22.5,
27.6,
23.3,
35.7,
28.4,
29.4,
31.9,
25.1,
35.6,
21.1,
17.8,
31.5,
26.6,
44.8,
31.3,
17.2,
25.3,
28.8,
49.9,
18,
23.5,
18.1,
41.7,
20.7,
30.3,
27.8,
18.9,
20.4,
31.8,
44.8,
37.1,
39.9,
24.1,
18.1,
36.3,
19.1,
21.1,
28.9,
18.9,
43.3,
37.2,
32.2,
26.3,
25.4,
18.3,
21.4,
22.9,
16.2,
29.6,
21.5,
24.2,
37.3,
33.3,
33.1,
22.2,
24.4,
30.3,
28.2,
17,
53.8,
30.4,
22.3,
25.3,
29.8,
43.8,
34.5,
25.5,
31.5,
39.2,
47.3,
17,
38.2,
31.1,
22.4,
32,
24.7,
36.3,
20.2,
27.4,
29.6,
54.3,
28.6,
43.9,
22.2,
28.4,
23.7,
18.3,
18.3,
28.9,
27.1,
13.8,
21.9,
24,
20.8,
39.8,
26.2,
43.9,
27.3,
22,
40,
20.1,
42.2,
25.8,
27.2,
30.9,
47.9,
33.1,
37.8,
25.4,
28.4,
16.6,
35.1,
32.7,
29.8,
13,
27.5,
23.6,
36.9,
16.7,
26.5,
37,
27.7,
23.5,
40.4,
27.7,
28.3,
55,
28.7,
25,
24.8,
35.5,
25,
17.3,
37.8,
32.8,
28.7,
28.8,
28.4,
24.9,
29.6,
26.1,
29.5,
23.9,
25.8,
36.2,
31.3,
26.1,
19.1,
17.6,
21.4,
24,
25.5,
19.9,
19.9,
35.5,
26.9,
24.2,
30,
30.9,
26.5,
30.6,
19.1,
23.2,
30.5,
15.8,
16.9,
18,
13.9,
36.7,
30.8,
34.6,
16.3,
38.2,
27.3,
30.4,
34.5,
17.4,
32,
33.4,
40.2,
30.1,
46.2,
35.6,
29.4,
27.3,
17.4,
26.9,
17.7,
23.6,
27.7,
38,
35.2,
32.4,
36.1,
30.6,
29,
22.3,
27.3,
17.4,
39,
26.7,
24.2,
22.8,
25.6,
21.9,
25.9,
30.6,
25.7,
33.3,
23.8,
17.2,
31,
24.7,
27.1,
38.3,
50.9,
29.2,
23.6,
33.5,
16.3,
30.8,
31.1,
26.4,
18.6,
37.3,
26.6,
17.6,
22.3,
20.2,
30.9,
31.5,
29.7,
32.5,
32.8,
25.4,
27.2,
31.6,
39.6,
19.8,
29.6,
25.5,
25.8,
32.1,
25.6,
26.3,
39.4,
25.6,
34,
35.8,
24.9,
29.3,
23.4,
24.5,
29.8,
28.9,
27.3,
23.2,
34.2,
23.3,
26.9,
21.8,
26.9,
29.9,
22.4,
27.5,
35,
27.9,
25.5,
28.6,
34.8,
50.6,
30.9,
14.8,
31.4,
22.3,
19.1,
27.4,
17.5,
36.2,
25.4,
25.7,
28.6,
33.7,
37.9,
28.4,
20,
29.1,
27.6,
27.6,
24.2,
31,
34.5,
27,
20.7,
26.1,
19.2,
28,
23.6,
57.2,
33,
22.2,
24.8,
24.7,
27.9,
21.6,
34.8,
32.9,
28,
25,
24.7,
27.3,
34.2,
39.1,
22.7,
33.9,
18.6,
16.7,
20.6,
20.1,
45.2,
25.3,
32.4,
34.9,
43.7,
23,
21.5,
24.9,
22.9,
25,
21.3,
46.1,
64.4,
37,
21.3,
28.3,
31.1,
25.1,
27.6,
29.4,
14.1,
35.2,
23.9,
34,
27.1,
29.5,
20.7,
39.5,
33.7,
23.2,
30.1,
28.8,
92,
38,
27.7,
43.2,
24.1,
28.7,
34.8,
23.5,
28.5,
20.3,
43,
18.3,
27.2,
22,
50.8,
35.4,
55.9,
27.9,
19.8,
26,
26.8,
32.5,
15,
14.2,
17,
26.2,
24.9,
26.6,
22.7,
32.7,
36.7,
31.5,
36.9,
27.6,
20.7,
20.2,
23.4,
25.3,
28.2,
27,
24.8,
34.8,
17.3,
27.8,
16.2,
21.7,
21.4,
29.3,
36.2,
28.3,
23.5,
25.9,
27.1,
21.5,
30.5,
23.4,
37.6,
24,
25.1,
28.7,
41.8,
25.6,
27.2,
24.4,
20.7,
19.6,
24.8,
32.6,
20.5,
36.2,
45.4,
27.6,
34.7,
32.4,
28.7,
45.3,
40.3,
38.9,
46,
32.4,
19.4,
27.5,
26.2,
31.1,
34.6,
24.3,
34.6,
28.5,
32.1,
28,
20.9,
33.8,
25.8,
31.8,
26.8,
25.9,
29.2,
25.3,
41.7,
38.8,
29.1,
30.9,
39.3,
41.8,
26.2,
33.8,
26.1,
24.1,
30.7,
22.4,
23,
30.2,
30.3,
28.7,
36.3,
16.3,
34.5,
20.8,
31,
17.6,
22.7,
38.4,
40.8,
32.9,
31.3,
28.5,
27.6,
40.2,
24.7,
17.7,
25.4,
28.5,
26.2,
26,
45.3,
31.5,
20.6,
29.4,
57.9,
39.1,
23.8,
38.1,
19.5,
35.5,
26.4,
22.2,
29.1,
41.9,
22.6,
33.3,
21.3,
20.4,
28.7,
28.8,
38.7,
32.3,
37.9,
25.3,
27.7,
25.1,
16.8,
27.7,
28.6,
37.6,
32.7,
22.7,
16,
23.5,
45.8,
20.8,
16.7,
22.2,
33.1,
32.7,
41.4,
24.1,
28,
24.2,
23.5,
23,
20.3,
24.8,
26.1,
24.2,
19.3,
18.2,
24.2,
41.5,
28,
37,
32.2,
35.9,
47.6,
32.1,
55.7,
22.2,
20.1,
14.6,
28.4,
20.6,
19.8,
23.4,
25.1,
30.1,
33.3,
24,
27.8,
21.4,
25.5,
21.8,
18.9,
21.9,
30.6,
29.8,
28.7,
35.5,
29.1,
32.8,
22.8,
31.3,
29,
19,
24.1,
21.4,
28,
27.6,
28.7,
24.5,
25,
29.6,
29.9,
35.6,
27.9,
42.6,
21.2,
28.5,
25.6,
31.1,
30.4,
22.9,
31.9,
48.8,
43,
21.8,
35.5,
24.3,
23.1,
18.5,
42.4,
32.3,
22.6,
17.8,
18.4,
26.9,
32,
23.4,
31.4,
22.4,
28.9,
27.4,
57.2,
28.4,
23.5,
28.3,
38.2,
33.3,
33.3,
28.8,
32.1,
28.9,
25.5,
25.9,
19.5,
23.8,
24.9,
18.5,
30.6,
16.1,
32.5,
27.4,
30.2,
23.5,
36,
24.8,
27.5,
40,
18.1,
18.8,
23.6,
29.7,
43.7,
27.3,
20.2,
23.8,
21.4,
25.1,
17.9,
34.5,
27.1,
26.1,
43.4,
33.2,
37.2,
41.3,
35.4,
26.1,
24.5,
32.7,
29.2,
20.4,
33.5,
14,
33.5,
27.5,
35.3,
30.1,
32.6,
47.5,
31.6,
23.5,
40.3,
34.1,
16.1,
26.4,
19,
34.7,
23.3,
32.6,
26,
35.1,
18.6,
30.4,
32.8,
21.6,
23.3,
31.4,
26.3,
20.1,
28.3,
40.4,
18.6,
44.7,
25.3,
28.3,
39.9,
34.7,
31.9,
35,
32.7,
30.6,
27.9,
20.4,
29.6,
23.5,
46.4,
27.4,
25.2,
25.8,
33.3,
27.7,
28.9,
33.1,
38.1,
27.9,
17.4,
27.1,
22.7,
28.3,
21.5,
30,
18.6,
45.2,
39.7,
31.5,
23.2,
27.3,
26.5,
19.3,
26.1,
19.8,
25.1,
37.3,
30.3,
16.1,
25.1,
26.7,
29.3,
38.7,
35.8,
21.7,
31.4,
26.9,
33,
24.5,
44.9,
25.5,
22.1,
24.3,
32.4,
33.1,
26.7,
37.9,
39.6,
19.8,
30,
23.1,
34.3,
38.4,
32.8,
34.4,
44.6,
32.8,
26.4,
27.3,
32.4,
43.2,
33.6,
26.4,
46.9,
36.3,
34.9,
29.6,
16.5,
35.4,
23.6,
33.1,
40.2,
14.8,
34.8,
33.5,
31.9,
16.1,
50.2,
35.9,
39.6,
27.5,
32.3,
15.4,
28.4,
19.5,
25,
26.3,
39.6,
38.6,
33.9,
32.2,
25.1,
37.4,
30.1,
27,
32.4,
26.8,
27.2,
36.7,
36,
37.2,
19.1,
21.8,
26.5,
35,
32.6,
23.9,
33.7,
29.4,
29,
20.2,
27.4,
25,
30.7,
23,
26.6,
18.1,
47.1,
29.7,
19.9,
13.3,
32.7,
31.8,
31.4,
20.4,
33.2,
30.5,
30.3,
34.2,
34.4,
48.1,
22.8,
30.5,
35.9,
33.7,
24.1,
25.4,
25.5,
29.2,
17.4,
29.9,
21.5,
24.7,
25.1,
34.4,
27.9,
31.5,
31.8,
30.1,
26.4,
27.2,
22.5,
29.8,
26.1,
24.9,
23.5,
30.1,
37.3,
25.5,
35.3,
27.6,
28.7,
29.9,
34.2,
24.9,
36.8,
24.5,
34.6,
30,
28.8,
28.6,
23.6,
20.1,
21.1,
34.1,
29.5,
37.8,
31.2,
31.1,
34.2,
17.7,
39.2,
22.2,
29.5,
27,
16.8,
23.3,
33.5,
15.6,
28.2,
22.6,
20.2,
22.2,
22.6,
26.7,
22.9,
21.5,
30.3,
14.8,
24.3,
17.4,
29,
26.3,
51.7,
29.2,
18.3,
21.9,
16.3,
27.4,
20.4,
23.2,
30.3,
16.7,
31,
33.5,
17,
19.4,
14.6,
32.2,
37.7,
27.4,
25,
20.1,
21.7,
38.2,
17,
28.4,
30,
34.1,
19.3,
21.6,
32.9,
27.2,
27.8,
29.9,
28.4,
24.1,
24,
23.4,
32.6,
33.1,
33.8,
30.3,
36.9,
29.7,
38.8,
30.9,
36.9,
18.8,
25.5,
32.3,
25.8,
31.1,
27.9,
18.6,
20.1,
26.7,
30.3,
30.4,
30,
24.9,
60.9,
24.3,
38.7,
18.6,
17.1,
29.4,
16.9,
34.2,
27.6,
47.8,
18.9,
24.4,
25.2,
30.7,
26.1,
25.3,
32.8,
45.7,
24.6,
32.3,
47.6,
34.4,
18.2,
44.5,
36.6,
19.4,
22.9,
20,
36.1,
27.3,
42.2,
26.4,
23.6,
18.6,
18,
31.1,
30,
43.8,
23.8,
36.2,
32.6,
18,
25.5,
29.9,
32.1,
25.4,
23.4,
23.4,
21.8,
30.8,
37.9,
42.5,
23.4,
35.4,
32.3,
27,
46.3,
30.1,
34.7,
18.3,
16.2,
33.2,
18.6,
25.1,
24.1,
33.1,
24.8,
23.4,
54.1,
28.9,
38.1,
37.2,
19.6,
29,
26.7,
20.5,
34.1,
42.4,
37.7,
39.1,
28.9,
30.7,
33.8,
32.1,
40.5,
24.8,
31.8,
27,
13.7,
23.4,
16.4,
14.9,
38.8,
38.6,
32.2,
30.6,
28.1,
35.2,
21.8,
28.1,
28.2,
28.4,
37.4,
21.8,
19.8,
16.2,
33.6,
21.2,
28.4,
41.1,
24.9,
56.6,
28.8,
33.7,
38.6,
33.8,
22.4,
37.3,
23.4,
39.1,
21.2,
31.2,
28,
21.1,
15.7,
29.1,
26.1,
16.2,
27.1,
28.8,
37.9,
21.6,
30.1,
29.7,
26.4,
46,
26.1,
23.2,
28.4,
20.5,
24.6,
32.2,
30.5,
24.9,
26.5,
34.5,
21.5,
15.9,
17.7,
26.5,
24.4,
26.6,
23.9,
30.1,
23.6,
18.4,
32.1,
24.4,
27.3,
34.8,
28.4,
40.2,
31.1,
24,
38.1,
29.9,
24.5,
49.5,
35.5,
29.5,
28.8,
22.1,
29.4,
45,
27.1,
28.3,
20.1,
31.3,
17.6,
24.5,
24.1,
31.3,
31.5,
29.9,
30.2,
27.8,
15.6,
26.3,
24.8,
17.1,
20.3,
18.7,
28.8,
28.7,
24.6,
25.3,
29.7,
21.1,
26.9,
26.2,
26.3,
24.8,
25,
34.6,
42.7,
24.5,
19.3,
29.3,
22.1,
27.8,
24.7,
25.3,
41.2,
47.6,
23.4,
22.7,
29.7,
32.3,
36.9,
27.7,
24.3,
37.4,
25.1,
24.3,
24.3,
28.7,
16.8,
35.8,
40,
24.3,
25.6,
37.8,
23,
21,
15.5,
17.1,
28,
40.8,
37.5,
24.2,
26.9,
33.1,
21.8,
34.7,
30.2,
16.8,
21,
30.9,
38.9,
24.3,
17.4,
28.2,
40.8,
17.5,
28.3,
24.5,
21.7,
46.9,
18.6,
40,
30.6,
25.6,
26.2
],
"yaxis": "y3"
}
],
"layout": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"height": 500,
"margin": {
"b": 150,
"l": 50,
"r": 50,
"t": 80
},
"paper_bgcolor": "#EEECE2",
"plot_bgcolor": "#EEECE2",
"showlegend": false,
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"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": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"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": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"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": "white",
"showlakes": true,
"showland": true,
"subunitcolor": "#C8D4E3"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "white",
"polar": {
"angularaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
},
"bgcolor": "white",
"radialaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"yaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"zaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"baxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"bgcolor": "white",
"caxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
}
}
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene B",
"size": 20
},
"text": "Boxplots of age, avg_glucose_level, bmi",
"x": 0.5
},
"width": 1200,
"xaxis": {
"anchor": "y",
"domain": [
0,
0.26666666666666666
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"ticktext": [
"age"
],
"tickvals": [
0
],
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
}
}
},
"xaxis2": {
"anchor": "y2",
"domain": [
0.3666666666666667,
0.6333333333333333
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"ticktext": [
"avg_glucose_level"
],
"tickvals": [
0
],
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
}
}
},
"xaxis3": {
"anchor": "y3",
"domain": [
0.7333333333333334,
1
],
"showticklabels": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"ticktext": [
"bmi"
],
"tickvals": [
0
],
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
}
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Value"
}
},
"yaxis2": {
"anchor": "x2",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Value"
}
},
"yaxis3": {
"anchor": "x3",
"domain": [
0,
1
],
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"text": "Value"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_combined_boxplots(\n",
" stroke_df, numerical_features, save_path=\"../images/numerical_boxplots.png\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/numerical_boxplots.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that there are a few outliers, therefore we need to investigate them further.\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No anomalies detected in feature 'age'.\n",
"Anomalies detected in feature 'avg_glucose_level':\n",
" id gender age hypertension heart_disease ever_married \\\n",
"0 9046 Male 67.0 0 1 Yes \n",
"3 60182 Female 49.0 0 0 Yes \n",
"4 1665 Female 79.0 1 0 Yes \n",
"5 56669 Male 81.0 0 0 Yes \n",
"14 5317 Female 79.0 0 1 Yes \n",
"... ... ... ... ... ... ... \n",
"5061 38009 Male 41.0 0 0 Yes \n",
"5062 11184 Female 82.0 0 0 Yes \n",
"5063 68967 Male 39.0 0 0 Yes \n",
"5064 66684 Male 70.0 0 0 Yes \n",
"5076 39935 Female 34.0 0 0 Yes \n",
"\n",
" work_type residence_type avg_glucose_level bmi smoking_status \\\n",
"0 Private Urban 228.69 36.6 formerly smoked \n",
"3 Private Urban 171.23 34.4 smokes \n",
"4 Self-employed Rural 174.12 24.0 never smoked \n",
"5 Private Urban 186.21 29.0 formerly smoked \n",
"14 Private Urban 214.09 28.2 never smoked \n",
"... ... ... ... ... ... \n",
"5061 Private Urban 223.78 32.3 never smoked \n",
"5062 Self-employed Rural 211.58 36.9 never smoked \n",
"5063 Private Urban 179.38 27.7 Unknown \n",
"5064 Self-employed Rural 193.88 24.3 Unknown \n",
"5076 Private Rural 174.37 23.0 never smoked \n",
"\n",
" stroke \n",
"0 1 \n",
"3 1 \n",
"4 1 \n",
"5 1 \n",
"14 1 \n",
"... ... \n",
"5061 0 \n",
"5062 0 \n",
"5063 0 \n",
"5064 0 \n",
"5076 0 \n",
"\n",
"[567 rows x 12 columns]\n",
"Anomalies detected in feature 'bmi':\n",
" id gender age hypertension heart_disease ever_married \\\n",
"21 13861 Female 52.0 1 0 Yes \n",
"113 41069 Female 45.0 0 0 Yes \n",
"254 32257 Female 47.0 0 0 Yes \n",
"258 28674 Female 74.0 1 0 Yes \n",
"270 72911 Female 57.0 1 0 Yes \n",
"... ... ... ... ... ... ... \n",
"4858 1696 Female 43.0 0 0 Yes \n",
"4906 72696 Female 53.0 0 0 Yes \n",
"4952 16245 Male 51.0 1 0 Yes \n",
"5009 40732 Female 50.0 0 0 Yes \n",
"5057 38349 Female 49.0 0 0 Yes \n",
"\n",
" work_type residence_type avg_glucose_level bmi smoking_status \\\n",
"21 Self-employed Urban 233.29 48.9 never smoked \n",
"113 Private Rural 224.10 56.6 never smoked \n",
"254 Private Urban 210.95 50.1 Unknown \n",
"258 Self-employed Urban 205.84 54.6 never smoked \n",
"270 Private Rural 129.54 60.9 smokes \n",
"... ... ... ... ... ... \n",
"4858 Private Urban 100.88 47.6 smokes \n",
"4906 Private Urban 70.51 54.1 never smoked \n",
"4952 Self-employed Rural 211.83 56.6 never smoked \n",
"5009 Self-employed Rural 126.85 49.5 formerly smoked \n",
"5057 Govt_job Urban 69.92 47.6 never smoked \n",
"\n",
" stroke \n",
"21 1 \n",
"113 1 \n",
"254 0 \n",
"258 0 \n",
"270 0 \n",
"... ... \n",
"4858 0 \n",
"4906 0 \n",
"4952 0 \n",
"5009 0 \n",
"5057 0 \n",
"\n",
"[110 rows x 12 columns]\n",
"Detected anomalies:\n",
" age avg_glucose_level bmi\n",
"0 67.0 228.69 36.6\n",
"1 49.0 171.23 34.4\n",
"2 79.0 174.12 24.0\n",
"3 81.0 186.21 29.0\n",
"4 79.0 214.09 28.2\n",
".. ... ... ...\n",
"644 30.0 84.92 47.8\n",
"645 43.0 100.88 47.6\n",
"646 53.0 70.51 54.1\n",
"647 50.0 126.85 49.5\n",
"648 49.0 69.92 47.6\n",
"\n",
"[649 rows x 3 columns]\n"
]
}
],
"source": [
"anomalies = detect_anomalies_iqr(stroke_df, numerical_features)\n",
"print(\"Detected anomalies:\")\n",
"print(anomalies)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our analysis revealed the presence of outliers in the dataset. After careful consideration, we have decided to retain these outliers for the following reasons:\n",
"\n",
"**1. Domain-Specific Considerations**\n",
"\n",
"- **Medical Significance**: In healthcare datasets, extreme values often represent clinically significant cases.\n",
"- **Preserving Information**: Removing outliers without domain expertise risks losing valuable insights.\n",
"\n",
"**2. Dataset Characteristics**\n",
"\n",
"- **Class Imbalance**: The dataset exhibits an imbalanced distribution, with rare occurrences of the target variable (stroke).\n",
"- **Rare Case Representation**: Eliminating outliers could further reduce the already limited representation of these critical cases.\n",
"\n",
"**3. Model Robustness**\n",
"\n",
"- **Diverse Training Data**: Including outliers helps develop models that are more robust and generalize better across a wide range of scenarios.\n",
"- **Avoiding Overfitting**: Retaining outliers can prevent models from becoming overly sensitive to a narrow range of data points.\n",
"\n",
"**4. Proposed Approach**\n",
"\n",
"To balance the need for data integrity with the potential impact of outliers, we propose the following strategy:\n",
"\n",
"1. **Outlier Flagging**: Introduce a new binary feature called `has_anomalies` to identify potential outliers.\n",
"2. **Flexible Handling**: This approach allows for targeted treatment of outliers in subsequent analyses and modeling stages.\n",
"\n",
"**5. Benefits of This Strategy**\n",
"\n",
"- **Data Integrity**: Preserves the original dataset without loss of potentially crucial information.\n",
"- **Analytical Flexibility**: Enables customized handling of outliers based on specific requirements of each analysis or modeling task.\n",
"- **Transparency**: Clearly identifies potential anomalies for further investigation or specialized treatment.\n",
"\n",
"By adopting this nuanced approach to outlier management, we aim to maintain the dataset's integrity while providing the flexibility needed for robust analysis and modeling.\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"has_anomalies\n",
"False 4260\n",
"True 649\n",
"Name: count, dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stroke_df[\"has_anomalies\"] = flag_anomalies(stroke_df, numerical_features)\n",
"stroke_df[\"has_anomalies\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>id</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",
" <th>has_anomalies</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9046</td>\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",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>31112</td>\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",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60182</td>\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",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1665</td>\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",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>56669</td>\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",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id gender age hypertension heart_disease ever_married \\\n",
"0 9046 Male 67.0 0 1 Yes \n",
"2 31112 Male 80.0 0 1 Yes \n",
"3 60182 Female 49.0 0 0 Yes \n",
"4 1665 Female 79.0 1 0 Yes \n",
"5 56669 Male 81.0 0 0 Yes \n",
"\n",
" work_type residence_type avg_glucose_level bmi smoking_status \\\n",
"0 Private Urban 228.69 36.6 formerly smoked \n",
"2 Private Rural 105.92 32.5 never smoked \n",
"3 Private Urban 171.23 34.4 smokes \n",
"4 Self-employed Rural 174.12 24.0 never smoked \n",
"5 Private Urban 186.21 29.0 formerly smoked \n",
"\n",
" stroke has_anomalies \n",
"0 1 True \n",
"2 1 False \n",
"3 1 True \n",
"4 1 True \n",
"5 1 True "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stroke_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"plot_correlation_matrix(\n",
" stroke_df,\n",
" numerical_features + [\"stroke\"],\n",
" save_path=\"../images/correlation_matrix.png\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/correlation_matrix.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Correlation Matrix Analysis**\n",
"\n",
"The correlation matrix visually represents the pairwise correlations between key numerical variables in our dataset:\n",
"\n",
"- Age\n",
"- Average glucose level\n",
"- BMI (Body Mass Index)\n",
"- Stroke (target variable)\n",
"\n",
"**Key Interpretations**\n",
"\n",
"| Relationship | Correlation | Interpretation |\n",
"| -------------------------------- | ----------- | ------------------------------------------------------------------------------------ |\n",
"| Age and Stroke | 0.23 | Strongest correlation; suggests elevated stroke risk with age |\n",
"| Average Glucose Level and Stroke | 0.14 | Moderate correlation; higher blood sugar might increase stroke risk |\n",
"| BMI and Stroke | 0.04 | Weak positive correlation; slight association between higher BMI and stroke risk |\n",
"| Age and BMI | 0.33 | Moderate positive correlation; older individuals tend to have higher BMI |\n",
"| Age and Average Glucose Level | 0.24 | Weak positive correlation; glucose levels tend to increase slightly with age |\n",
"| BMI and Average Glucose Level | 0.18 | Weak positive correlation; higher BMI slightly associated with higher glucose levels |\n",
"\n",
"**Interpretation Guidelines**\n",
"\n",
"- **Strong correlation**: |r| > 0.5\n",
"- **Moderate correlation**: 0.3 < |r| ≤ 0.5\n",
"- **Weak correlation**: 0.1 < |r| ≤ 0.3\n",
"- **Very weak correlation**: |r| ≤ 0.1\n",
"\n",
"**Additional Considerations**\n",
"\n",
"- Correlations do not imply causation.\n",
"- Some relationships may be non-linear and require further investigation.\n",
"- Confounding factors may influence observed correlations.\n",
"\n",
"**We are going to keep all of the features because:**\n",
"\n",
"1. There isn't strong multicollinearity between the predictors (highest correlation is 0.33).\n",
"2. All features show some level of correlation with the target variable, potentially providing predictive power.\n",
"3. Removing features based solely on correlation might lead to loss of important information.\n",
"\n",
"Therefore, before moving further with modeling, we can proceed with encoding the categorical features.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on our distribution analysis, we identified several categorical features that need encoding. Our encoding strategy will be as follows:\n",
"\n",
"1. For binary categorical features (those with 2 unique values), we will use label encoding. This is appropriate because there's no implicit ordering, and it's a simple 0/1 representation.\n",
"\n",
"2. For categorical features with more than 2 unique values, we will use one-hot encoding. This avoids introducing an arbitrary ordinal relationship between categories.\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"binary_features = [\"ever_married\", \"residence_type\"]\n",
"\n",
"label_encoder = LabelEncoder()\n",
"for feature in binary_features:\n",
" stroke_df[feature] = label_encoder.fit_transform(stroke_df[feature])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"stroke_df[\"has_anomalies\"] = stroke_df[\"has_anomalies\"].astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next up we use one hot encoding for categorical features with more than 2 unique values.\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id age hypertension heart_disease ever_married residence_type \\\n",
"0 9046 67.0 0 1 1 1 \n",
"2 31112 80.0 0 1 1 0 \n",
"3 60182 49.0 0 0 1 1 \n",
"4 1665 79.0 1 0 1 0 \n",
"5 56669 81.0 0 0 1 1 \n",
"\n",
" avg_glucose_level bmi stroke has_anomalies ... gender_other \\\n",
"0 228.69 36.6 1 1 ... 0.0 \n",
"2 105.92 32.5 1 0 ... 0.0 \n",
"3 171.23 34.4 1 1 ... 0.0 \n",
"4 174.12 24.0 1 1 ... 0.0 \n",
"5 186.21 29.0 1 1 ... 0.0 \n",
"\n",
" work_type_govt_job work_type_never_worked work_type_private \\\n",
"0 0.0 0.0 1.0 \n",
"2 0.0 0.0 1.0 \n",
"3 0.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 1.0 \n",
"\n",
" work_type_self-employed work_type_children smoking_status_unknown \\\n",
"0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 1.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"\n",
" smoking_status_formerly_smoked smoking_status_never_smoked \\\n",
"0 1.0 0.0 \n",
"2 0.0 1.0 \n",
"3 0.0 0.0 \n",
"4 0.0 1.0 \n",
"5 1.0 0.0 \n",
"\n",
" smoking_status_smokes \n",
"0 0.0 \n",
"2 0.0 \n",
"3 1.0 \n",
"4 0.0 \n",
"5 0.0 \n",
"\n",
"[5 rows x 22 columns]\n"
]
}
],
"source": [
"onehot_features = [\"gender\", \"work_type\", \"smoking_status\"]\n",
"\n",
"onehot_encoder = OneHotEncoder(sparse_output=False)\n",
"onehot_encoded = onehot_encoder.fit_transform(stroke_df[onehot_features])\n",
"onehot_columns = onehot_encoder.get_feature_names_out(onehot_features)\n",
"\n",
"column_mapping = {}\n",
"for feature, categories in zip(onehot_features, onehot_encoder.categories_):\n",
" for category in categories:\n",
" old_name = f\"{feature}_{category}\"\n",
" new_name = f\"{feature}_{category.lower().replace(' ', '_')}\"\n",
" column_mapping[old_name] = new_name\n",
"\n",
"onehot_columns = [column_mapping.get(col, col) for col in onehot_columns]\n",
"\n",
"stroke_df = stroke_df.drop(columns=onehot_features)\n",
"stroke_df[onehot_columns] = onehot_encoded\n",
"\n",
"print(stroke_df.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lastly, we can move to our statistical inference.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Statistical Inference**\n",
"\n",
"**Highlights:**\n",
"\n",
"- Investigate the relationships between age, glucose level, BMI, hypertension, heart disease, and stroke occurrence\n",
"- Conduct t-tests for continuous variables and chi-square tests for categorical variables\n",
"- Report p-values, effect sizes, and confidence intervals\n",
"- Check assumptions and apply multiple comparison adjustments if needed\n",
"\n",
"**Target Population and Sample:**\n",
"The target population is adults at risk of stroke. The sample consists of 4,909 individuals with diverse demographic and health characteristics.\n",
"\n",
"**Significance Level:**\n",
"α = 0.05\n",
"\n",
"**Hypotheses and Tests:**\n",
"\n",
"1. **Age and Stroke Risk**\n",
"\n",
" - H0: No difference in mean age between stroke and non-stroke groups\n",
" - H1: Significant difference in mean age between stroke and non-stroke groups\n",
" - Test: Independent samples t-test (two-tailed)\n",
" - Effect size: Cohen's d\n",
"\n",
"2. **Glucose Level and Stroke Risk**\n",
"\n",
" - H0: No difference in mean glucose levels between stroke and non-stroke groups\n",
" - H1: Significant difference in mean glucose levels between stroke and non-stroke groups\n",
" - Test: Independent samples t-test (two-tailed)\n",
" - Effect size: Cohen's d\n",
"\n",
"3. **BMI and Stroke Risk**\n",
"\n",
" - H0: No difference in mean BMI between stroke and non-stroke groups\n",
" - H1: Significant difference in mean BMI between stroke and non-stroke groups\n",
" - Test: Independent samples t-test (two-tailed)\n",
" - Effect size: Cohen's d\n",
"\n",
"4. **Hypertension and Stroke Risk**\n",
"\n",
" - H0: No association between hypertension and stroke occurrence\n",
" - H1: Significant association between hypertension and stroke occurrence\n",
" - Test: Chi-square test of independence\n",
" - Effect size: Odds ratio, Cramer's V\n",
"\n",
"5. **Heart Disease and Stroke Risk**\n",
" - H0: No association between heart disease and stroke occurrence\n",
" - H1: Significant association between heart disease and stroke occurrence\n",
" - Test: Chi-square test of independence\n",
" - Effect size: Odds ratio, Cramer's V\n",
"\n",
"**Confidence Intervals (95%):**\n",
"\n",
"- Mean Age of Stroke Patients\n",
"- Mean Glucose Level of Stroke Patients\n",
"- Mean BMI of Stroke Patients\n",
"\n",
"**Assumptions and Corrections:**\n",
"\n",
"- Check normality and equal variances for t-tests\n",
"- Check independence and expected cell counts for chi-square tests\n",
"- Apply multiple comparison adjustments (e.g., Bonferroni correction) if needed\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age and Stroke Risk:\n",
"T-test results: t=16.733, p=0.000\n",
"Cohen's d: 1.183\n"
]
}
],
"source": [
"stroke_age = stroke_df[stroke_df[\"stroke\"] == 1][\"age\"]\n",
"non_stroke_age = stroke_df[stroke_df[\"stroke\"] == 0][\"age\"]\n",
"\n",
"age_ttest = stats.ttest_ind(stroke_age, non_stroke_age)\n",
"age_cohen_d = pg.compute_effsize(stroke_age, non_stroke_age, eftype=\"cohen\")\n",
"\n",
"print(\"Age and Stroke Risk:\")\n",
"print(f\"T-test results: t={age_ttest.statistic:.3f}, p={age_ttest.pvalue:.3f}\")\n",
"print(f\"Cohen's d: {age_cohen_d:.3f}\")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Glucose Level and Stroke Risk:\n",
"T-test results: t=9.828, p=0.000\n",
"Cohen's d: 0.695\n"
]
}
],
"source": [
"stroke_glucose = stroke_df[stroke_df[\"stroke\"] == 1][\"avg_glucose_level\"]\n",
"non_stroke_glucose = stroke_df[stroke_df[\"stroke\"] == 0][\"avg_glucose_level\"]\n",
"\n",
"glucose_ttest = stats.ttest_ind(stroke_glucose, non_stroke_glucose)\n",
"glucose_cohen_d = pg.compute_effsize(stroke_glucose, non_stroke_glucose, eftype=\"cohen\")\n",
"\n",
"print(\"\\nGlucose Level and Stroke Risk:\")\n",
"print(f\"T-test results: t={glucose_ttest.statistic:.3f}, p={glucose_ttest.pvalue:.3f}\")\n",
"print(f\"Cohen's d: {glucose_cohen_d:.3f}\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"BMI and Stroke Risk:\n",
"T-test results: t=2.971, p=0.003\n",
"Cohen's d: 0.210\n"
]
}
],
"source": [
"stroke_bmi = stroke_df[stroke_df[\"stroke\"] == 1][\"bmi\"]\n",
"non_stroke_bmi = stroke_df[stroke_df[\"stroke\"] == 0][\"bmi\"]\n",
"\n",
"bmi_ttest = stats.ttest_ind(stroke_bmi, non_stroke_bmi)\n",
"bmi_cohen_d = pg.compute_effsize(stroke_bmi, non_stroke_bmi, eftype=\"cohen\")\n",
"\n",
"print(\"\\nBMI and Stroke Risk:\")\n",
"print(f\"T-test results: t={bmi_ttest.statistic:.3f}, p={bmi_ttest.pvalue:.3f}\")\n",
"print(f\"Cohen's d: {bmi_cohen_d:.3f}\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Hypertension and Stroke Risk:\n",
"Chi-square results: chi2=97.275, p=0.000\n",
"Odds ratio: 4.438\n",
"Cramer's V: 0.141\n"
]
}
],
"source": [
"hypertension_contingency = pd.crosstab(stroke_df[\"hypertension\"], stroke_df[\"stroke\"])\n",
"hypertension_chi2 = stats.chi2_contingency(hypertension_contingency)\n",
"\n",
"odds_ratio, _ = stats.fisher_exact(hypertension_contingency)\n",
"\n",
"cramers_v = calculate_cramers_v(hypertension_contingency)\n",
"\n",
"print(\"\\nHypertension and Stroke Risk:\")\n",
"print(\n",
" f\"Chi-square results: chi2={hypertension_chi2[0]:.3f}, p={hypertension_chi2[1]:.3f}\"\n",
")\n",
"print(f\"Odds ratio: {odds_ratio:.3f}\")\n",
"print(f\"Cramer's V: {cramers_v:.3f}\")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Heart Disease and Stroke Risk:\n",
"Chi-square results: chi2=90.280, p=0.000\n",
"Odds ratio: 5.243\n",
"Cramer's V: 0.136\n"
]
}
],
"source": [
"heart_disease_contingency = pd.crosstab(stroke_df[\"heart_disease\"], stroke_df[\"stroke\"])\n",
"heart_disease_chi2 = stats.chi2_contingency(heart_disease_contingency)\n",
"\n",
"odds_ratio, _ = stats.fisher_exact(heart_disease_contingency)\n",
"\n",
"cramers_v = calculate_cramers_v(heart_disease_contingency)\n",
"\n",
"print(\"\\nHeart Disease and Stroke Risk:\")\n",
"print(\n",
" f\"Chi-square results: chi2={heart_disease_chi2[0]:.3f}, p={heart_disease_chi2[1]:.3f}\"\n",
")\n",
"print(f\"Odds ratio: {odds_ratio:.3f}\")\n",
"print(f\"Cramer's V: {cramers_v:.3f}\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Confidence Intervals (95%):\n",
"Mean Age of Stroke Patients: (66.02157958992271, 69.40425773065147)\n",
"Mean Glucose Level of Stroke Patients: (126.0536264424378, 143.08914867717942)\n",
"Mean BMI of Stroke Patients: (29.608163593319265, 31.33442013873815)\n"
]
}
],
"source": [
"print(\"\\nConfidence Intervals (95%):\")\n",
"print(\n",
" f\"Mean Age of Stroke Patients: {stats.t.interval(0.95, len(stroke_age)-1, loc=np.mean(stroke_age), scale=stats.sem(stroke_age))}\"\n",
")\n",
"print(\n",
" f\"Mean Glucose Level of Stroke Patients: {stats.t.interval(0.95, len(stroke_glucose)-1, loc=np.mean(stroke_glucose), scale=stats.sem(stroke_glucose))}\"\n",
")\n",
"print(\n",
" f\"Mean BMI of Stroke Patients: {stats.t.interval(0.95, len(stroke_bmi)-1, loc=np.mean(stroke_bmi), scale=stats.sem(stroke_bmi))}\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Statistical Tests Results**\n",
"\n",
"1. Age: t = 16.733, p < 0.001, Cohen's d = 1.183\n",
" CI (95%): 66.02 - 69.40 years (stroke patients)\n",
"\n",
"2. Glucose Level: t = 9.828, p < 0.001, Cohen's d = 0.695\n",
" CI (95%): 126.05 - 143.09 mg/dL (stroke patients)\n",
"\n",
"3. BMI: t = 2.971, p = 0.003, Cohen's d = 0.210\n",
" CI (95%): 29.61 - 31.33 (stroke patients)\n",
"\n",
"4. Hypertension: χ² = 90.280, p < 0.001, Odds ratio = 5.243, Cramer's V = 0.136\n",
"\n",
"5. Heart Disease: χ² = 90.280, p < 0.001, Odds ratio = 5.243, Cramer's V = 0.136\n",
"\n",
"**Key Findings**\n",
"\n",
"- All tested factors show statistically significant associations with stroke risk (p < 0.05).\n",
"- Age has the strongest relationship (large effect size), followed by glucose level (medium effect size).\n",
"- Hypertension and heart disease both increase stroke odds by about 5 times.\n",
"- BMI shows a significant but small effect on stroke risk.\n",
"\n",
"**Implications for Stroke Prediction Model**\n",
"\n",
"1. Prioritize age and glucose level as key features in the model.\n",
"2. Include hypertension and heart disease as important binary predictors.\n",
"3. Consider BMI as a supplementary feature, possibly in interaction with other factors.\n",
"\n",
"**Next Steps:**\n",
"\n",
"- We can move to feature engineering based on our findings.\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>id</th>\n",
" <th>age</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>ever_married</th>\n",
" <th>residence_type</th>\n",
" <th>avg_glucose_level</th>\n",
" <th>bmi</th>\n",
" <th>stroke</th>\n",
" <th>has_anomalies</th>\n",
" <th>...</th>\n",
" <th>smoking_status_formerly_smoked</th>\n",
" <th>smoking_status_never_smoked</th>\n",
" <th>smoking_status_smokes</th>\n",
" <th>age_glucose</th>\n",
" <th>age_hypertension</th>\n",
" <th>age_heart_disease</th>\n",
" <th>age_squared</th>\n",
" <th>glucose_squared</th>\n",
" <th>bmi_age</th>\n",
" <th>bmi_glucose</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9046</td>\n",
" <td>67.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</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>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>15322.23</td>\n",
" <td>0.0</td>\n",
" <td>67.0</td>\n",
" <td>4489.0</td>\n",
" <td>52299.1161</td>\n",
" <td>2452.2</td>\n",
" <td>8370.054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>31112</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>105.92</td>\n",
" <td>32.5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>8473.60</td>\n",
" <td>0.0</td>\n",
" <td>80.0</td>\n",
" <td>6400.0</td>\n",
" <td>11219.0464</td>\n",
" <td>2600.0</td>\n",
" <td>3442.400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60182</td>\n",
" <td>49.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>171.23</td>\n",
" <td>34.4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8390.27</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2401.0</td>\n",
" <td>29319.7129</td>\n",
" <td>1685.6</td>\n",
" <td>5890.312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1665</td>\n",
" <td>79.0</td>\n",
" <td>1</td>\n",
" <td>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>1</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>13755.48</td>\n",
" <td>79.0</td>\n",
" <td>0.0</td>\n",
" <td>6241.0</td>\n",
" <td>30317.7744</td>\n",
" <td>1896.0</td>\n",
" <td>4178.880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>56669</td>\n",
" <td>81.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>186.21</td>\n",
" <td>29.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>15083.01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6561.0</td>\n",
" <td>34674.1641</td>\n",
" <td>2349.0</td>\n",
" <td>5400.090</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" id age hypertension heart_disease ever_married residence_type \\\n",
"0 9046 67.0 0 1 1 1 \n",
"2 31112 80.0 0 1 1 0 \n",
"3 60182 49.0 0 0 1 1 \n",
"4 1665 79.0 1 0 1 0 \n",
"5 56669 81.0 0 0 1 1 \n",
"\n",
" avg_glucose_level bmi stroke has_anomalies ... \\\n",
"0 228.69 36.6 1 1 ... \n",
"2 105.92 32.5 1 0 ... \n",
"3 171.23 34.4 1 1 ... \n",
"4 174.12 24.0 1 1 ... \n",
"5 186.21 29.0 1 1 ... \n",
"\n",
" smoking_status_formerly_smoked smoking_status_never_smoked \\\n",
"0 1.0 0.0 \n",
"2 0.0 1.0 \n",
"3 0.0 0.0 \n",
"4 0.0 1.0 \n",
"5 1.0 0.0 \n",
"\n",
" smoking_status_smokes age_glucose age_hypertension age_heart_disease \\\n",
"0 0.0 15322.23 0.0 67.0 \n",
"2 0.0 8473.60 0.0 80.0 \n",
"3 1.0 8390.27 0.0 0.0 \n",
"4 0.0 13755.48 79.0 0.0 \n",
"5 0.0 15083.01 0.0 0.0 \n",
"\n",
" age_squared glucose_squared bmi_age bmi_glucose \n",
"0 4489.0 52299.1161 2452.2 8370.054 \n",
"2 6400.0 11219.0464 2600.0 3442.400 \n",
"3 2401.0 29319.7129 1685.6 5890.312 \n",
"4 6241.0 30317.7744 1896.0 4178.880 \n",
"5 6561.0 34674.1641 2349.0 5400.090 \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stroke_df[\"age_glucose\"] = stroke_df[\"age\"] * stroke_df[\"avg_glucose_level\"]\n",
"stroke_df[\"age_hypertension\"] = stroke_df[\"age\"] * stroke_df[\"hypertension\"]\n",
"stroke_df[\"age_heart_disease\"] = stroke_df[\"age\"] * stroke_df[\"heart_disease\"]\n",
"\n",
"stroke_df[\"age_squared\"] = stroke_df[\"age\"] ** 2\n",
"stroke_df[\"glucose_squared\"] = stroke_df[\"avg_glucose_level\"] ** 2\n",
"\n",
"stroke_df[\"bmi_age\"] = stroke_df[\"bmi\"] * stroke_df[\"age\"]\n",
"stroke_df[\"bmi_glucose\"] = stroke_df[\"bmi\"] * stroke_df[\"avg_glucose_level\"]\n",
"\n",
"stroke_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Model Development Phase**\n",
"\n",
"**Objective**\n",
"\n",
"Our primary aim is to construct a predictive model capable of:\n",
"\n",
"1. Identifying potential stroke cases with high sensitivity (recall)\n",
"2. Maintaining an acceptable level of specificity (precision)\n",
"\n",
"**Key Performance Metrics**\n",
"\n",
"- **Recall (Sensitivity)**: Maximize to reduce the number of undetected stroke cases\n",
"- **Precision**: Optimize to minimize false positive rates\n",
"\n",
"**Strategic Focus**\n",
"\n",
"We will prioritize recall over precision to ensure:\n",
"\n",
"- Minimal oversight of actual stroke cases\n",
"- Acceptable rate of false alarms, balancing healthcare resource utilization\n",
"\n",
"This approach aligns with the critical nature of stroke diagnosis, where early detection and intervention are paramount for patient outcomes.\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"X = stroke_df.drop([\"stroke\", \"id\"], axis=1)\n",
"y = stroke_df[\"stroke\"]\n",
"\n",
"X_train_val, X_test, y_train_val, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42, stratify=y\n",
")\n",
"\n",
"X_train, X_val, y_train, y_val = train_test_split(\n",
" X_train_val, y_train_val, test_size=0.25, random_state=42, stratify=y_train_val\n",
")\n",
"\n",
"scaler = StandardScaler()\n",
"X_train_scaled = scaler.fit_transform(X_train)\n",
"X_val_scaled = scaler.transform(X_val)\n",
"X_test_scaled = scaler.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"models = {\n",
" \"Logistic Regression\": LogisticRegression(\n",
" class_weight=\"balanced\", random_state=42, max_iter=1000\n",
" ),\n",
" \"XGBoost\": xgb.XGBClassifier(\n",
" scale_pos_weight=len(y_train[y_train == 0]) / len(y_train[y_train == 1]),\n",
" random_state=42,\n",
" ),\n",
" \"LightGBM\": lgb.LGBMClassifier(class_weight=\"balanced\", random_state=42),\n",
" \"CatBoost\": CatBoostClassifier(\n",
" class_weights={\n",
" 0: 1,\n",
" 1: len(y_train[y_train == 0]) / len(y_train[y_train == 1]),\n",
" },\n",
" random_state=42,\n",
" verbose=False,\n",
" ),\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.74 0.85 940\n",
" 1 0.13 0.86 0.22 42\n",
"\n",
" accuracy 0.74 982\n",
" macro avg 0.56 0.80 0.53 982\n",
"weighted avg 0.95 0.74 0.82 982\n",
"\n",
"Confusion Matrix:\n",
"[[695 245]\n",
" [ 6 36]]\n",
"ROC AUC: 0.8444\n",
"PR AUC: 0.1721\n",
"F1 Score: 0.2229\n",
"Precision: 0.1281\n",
"Recall: 0.8571\n",
"Balanced Accuracy: 0.7983\n",
" precision recall f1-score support\n",
"\n",
" 0 0.96 0.97 0.97 940\n",
" 1 0.13 0.10 0.11 42\n",
"\n",
" accuracy 0.93 982\n",
" macro avg 0.55 0.53 0.54 982\n",
"weighted avg 0.92 0.93 0.93 982\n",
"\n",
"Confusion Matrix:\n",
"[[914 26]\n",
" [ 38 4]]\n",
"ROC AUC: 0.7822\n",
"PR AUC: 0.1078\n",
"F1 Score: 0.1111\n",
"Precision: 0.1333\n",
"Recall: 0.0952\n",
"Balanced Accuracy: 0.5338\n",
"[LightGBM] [Info] Number of positive: 125, number of negative: 2820\n",
"[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005883 seconds.\n",
"You can set `force_row_wise=true` to remove the overhead.\n",
"And if memory is not enough, you can set `force_col_wise=true`.\n",
"[LightGBM] [Info] Total Bins 1830\n",
"[LightGBM] [Info] Number of data points in the train set: 2945, number of used features: 25\n",
"[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
"[LightGBM] [Info] Start training from score 0.000000\n",
" precision recall f1-score support\n",
"\n",
" 0 0.96 0.96 0.96 940\n",
" 1 0.11 0.10 0.10 42\n",
"\n",
" accuracy 0.93 982\n",
" macro avg 0.53 0.53 0.53 982\n",
"weighted avg 0.92 0.93 0.92 982\n",
"\n",
"Confusion Matrix:\n",
"[[906 34]\n",
" [ 38 4]]\n",
"ROC AUC: 0.8036\n",
"PR AUC: 0.1265\n",
"F1 Score: 0.1000\n",
"Precision: 0.1053\n",
"Recall: 0.0952\n",
"Balanced Accuracy: 0.5295\n",
" precision recall f1-score support\n",
"\n",
" 0 0.96 0.95 0.96 940\n",
" 1 0.16 0.19 0.17 42\n",
"\n",
" accuracy 0.92 982\n",
" macro avg 0.56 0.57 0.57 982\n",
"weighted avg 0.93 0.92 0.93 982\n",
"\n",
"Confusion Matrix:\n",
"[[897 43]\n",
" [ 34 8]]\n",
"ROC AUC: 0.8077\n",
"PR AUC: 0.1332\n",
"F1 Score: 0.1720\n",
"Precision: 0.1569\n",
"Recall: 0.1905\n",
"Balanced Accuracy: 0.5724\n"
]
}
],
"source": [
"val_results = {}\n",
"val_predictions = {}\n",
"feature_importances = {}\n",
"\n",
"for name, model in models.items():\n",
" X_train_data = X_train_scaled if name == \"Logistic Regression\" else X_train\n",
" X_val_data = X_val_scaled if name == \"Logistic Regression\" else X_val\n",
" model.fit(X_train_data, y_train)\n",
" val_results[name] = evaluate_model(model, X_val_data, y_val)\n",
" val_predictions[name] = model.predict(X_val_data)\n",
" feature_importances[name] = dict(\n",
" zip(X.columns, extract_feature_importances(model, X_val_data, y_val))\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"marker": {
"color": "#CC7B5C"
},
"name": "Logistic Regression",
"text": [
"0.171",
"-0.022",
"-0.013",
"0.011",
"-0.001",
"0.010",
"-0.013",
"-0.001",
"-0.003",
"-0.003",
"0.000",
"0.007",
"-0.001",
"0.005",
"0.011",
"0.045",
"-0.004",
"-0.002",
"0.001",
"-0.003",
"-0.037",
"-0.007",
"-0.007",
"0.062",
"-0.002",
"0.101",
"0.004"
],
"textposition": "auto",
"type": "bar",
"x": [
"age",
"hypertension",
"heart_disease",
"ever_married",
"residence_type",
"avg_glucose_level",
"bmi",
"has_anomalies",
"gender_female",
"gender_male",
"gender_other",
"work_type_govt_job",
"work_type_never_worked",
"work_type_private",
"work_type_self-employed",
"work_type_children",
"smoking_status_unknown",
"smoking_status_formerly_smoked",
"smoking_status_never_smoked",
"smoking_status_smokes",
"age_glucose",
"age_hypertension",
"age_heart_disease",
"age_squared",
"glucose_squared",
"bmi_age",
"bmi_glucose"
],
"y": [
0.17087576374745417,
-0.021826205023761026,
-0.01293279022403259,
0.011235573659198906,
-0.000916496945010185,
0.010454854039375416,
-0.012763068567549218,
-0.0011880515953835657,
-0.0031568228105906203,
-0.0031568228105906203,
0,
0.0070943652410047405,
-0.0008146639511201536,
0.004752206381534267,
0.010760353021045473,
0.04545145960624576,
-0.0041072640868974886,
-0.0015953835709436612,
0.0007467752885268057,
-0.003021045485403932,
-0.037236931432450784,
-0.007365919891378155,
-0.006856754921928036,
0.06235573659198912,
-0.0021384928716904265,
0.10074677528852684,
0.004141208418194161
]
},
{
"marker": {
"color": "#D4A27F"
},
"name": "XGBoost",
"text": [
"0.058",
"0.062",
"0.030",
"0.038",
"0.025",
"0.038",
"0.043",
"0.084",
"0.035",
"0.000",
"0.000",
"0.021",
"0.000",
"0.013",
"0.031",
"0.000",
"0.077",
"0.034",
"0.041",
"0.043",
"0.048",
"0.050",
"0.050",
"0.000",
"0.000",
"0.147",
"0.034"
],
"textposition": "auto",
"type": "bar",
"x": [
"age",
"hypertension",
"heart_disease",
"ever_married",
"residence_type",
"avg_glucose_level",
"bmi",
"has_anomalies",
"gender_female",
"gender_male",
"gender_other",
"work_type_govt_job",
"work_type_never_worked",
"work_type_private",
"work_type_self-employed",
"work_type_children",
"smoking_status_unknown",
"smoking_status_formerly_smoked",
"smoking_status_never_smoked",
"smoking_status_smokes",
"age_glucose",
"age_hypertension",
"age_heart_disease",
"age_squared",
"glucose_squared",
"bmi_age",
"bmi_glucose"
],
"y": [
0.05818551406264305,
0.0617825947701931,
0.030403699725866318,
0.03831656649708748,
0.02479831874370575,
0.03783031180500984,
0.043367404490709305,
0.08379906415939331,
0.03458624333143234,
0,
0,
0.02057783491909504,
0,
0.012744971551001072,
0.03144834563136101,
0,
0.07687868177890778,
0.033510856330394745,
0.04087754338979721,
0.0430336594581604,
0.047893550246953964,
0.04954023286700249,
0.04962766170501709,
0,
0,
0.14681832492351532,
0.03397864103317261
]
},
{
"marker": {
"color": "#EBDBBC"
},
"name": "LightGBM",
"text": [
"308.000",
"2.000",
"5.000",
"26.000",
"53.000",
"388.000",
"392.000",
"0.000",
"22.000",
"12.000",
"0.000",
"6.000",
"0.000",
"28.000",
"54.000",
"0.000",
"21.000",
"45.000",
"45.000",
"33.000",
"402.000",
"160.000",
"116.000",
"0.000",
"0.000",
"474.000",
"408.000"
],
"textposition": "auto",
"type": "bar",
"x": [
"age",
"hypertension",
"heart_disease",
"ever_married",
"residence_type",
"avg_glucose_level",
"bmi",
"has_anomalies",
"gender_female",
"gender_male",
"gender_other",
"work_type_govt_job",
"work_type_never_worked",
"work_type_private",
"work_type_self-employed",
"work_type_children",
"smoking_status_unknown",
"smoking_status_formerly_smoked",
"smoking_status_never_smoked",
"smoking_status_smokes",
"age_glucose",
"age_hypertension",
"age_heart_disease",
"age_squared",
"glucose_squared",
"bmi_age",
"bmi_glucose"
],
"y": [
308,
2,
5,
26,
53,
388,
392,
0,
22,
12,
0,
6,
0,
28,
54,
0,
21,
45,
45,
33,
402,
160,
116,
0,
0,
474,
408
]
},
{
"marker": {
"color": "#9C8AA5"
},
"name": "CatBoost",
"text": [
"7.713",
"0.725",
"0.349",
"1.615",
"1.922",
"6.431",
"11.533",
"0.373",
"1.582",
"1.486",
"0.000",
"1.473",
"0.007",
"1.918",
"1.681",
"0.166",
"1.975",
"2.189",
"2.276",
"2.002",
"9.450",
"2.947",
"1.921",
"9.115",
"6.635",
"14.963",
"7.556"
],
"textposition": "auto",
"type": "bar",
"x": [
"age",
"hypertension",
"heart_disease",
"ever_married",
"residence_type",
"avg_glucose_level",
"bmi",
"has_anomalies",
"gender_female",
"gender_male",
"gender_other",
"work_type_govt_job",
"work_type_never_worked",
"work_type_private",
"work_type_self-employed",
"work_type_children",
"smoking_status_unknown",
"smoking_status_formerly_smoked",
"smoking_status_never_smoked",
"smoking_status_smokes",
"age_glucose",
"age_hypertension",
"age_heart_disease",
"age_squared",
"glucose_squared",
"bmi_age",
"bmi_glucose"
],
"y": [
7.7126688892138615,
0.7248183637062504,
0.3490578481821303,
1.6150889389806609,
1.9215327248495486,
6.431379661907906,
11.533058080392816,
0.3726421106539412,
1.5815155664116503,
1.485561371390091,
0,
1.473166669785956,
0.006519075939894882,
1.9177514726430551,
1.6809853426440036,
0.1662798043905862,
1.9750555367292455,
2.1893704795612483,
2.27571039117917,
2.0019557342904544,
9.449548614233422,
2.946984292841144,
1.9209515334733018,
9.115127402519708,
6.635195252287188,
14.96251882520677,
7.555556016586013
]
}
],
"layout": {
"barmode": "group",
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"height": 600,
"legend": {
"title": {
"text": "Models"
}
},
"paper_bgcolor": "#EEECE2",
"plot_bgcolor": "#EEECE2",
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"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": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"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": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"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": "white",
"showlakes": true,
"showland": true,
"subunitcolor": "#C8D4E3"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "white",
"polar": {
"angularaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
},
"bgcolor": "white",
"radialaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"yaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"zaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"baxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"bgcolor": "white",
"caxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
}
}
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene B",
"size": 24
},
"text": "Feature Importances Across Models",
"x": 0.5,
"xanchor": "center",
"y": 0.95,
"yanchor": "top"
},
"width": 1200,
"xaxis": {
"tickangle": -45,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"text": "Features"
}
},
"yaxis": {
"gridcolor": "LightGrey",
"gridwidth": 1,
"showgrid": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"text": "Importance"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"metrics_to_plot = [\n",
" \"roc_auc\",\n",
" \"pr_auc\",\n",
" \"f1\",\n",
" \"precision\",\n",
" \"recall\",\n",
" \"balanced_accuracy\",\n",
"]\n",
"plot_model_performance(\n",
" val_results, metrics_to_plot, save_path=\"../images/initial_model_performance.png\"\n",
")\n",
"plot_combined_confusion_matrices(\n",
" val_results,\n",
" y_val,\n",
" val_predictions,\n",
" labels=[\"No Stroke\", \"Stroke\"],\n",
" save_path=\"../images/initial_confusion_matrices.png\",\n",
")\n",
"plot_feature_importances(\n",
" feature_importances,\n",
" save_path=\"../images/initial_validation_feature_importances.png\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/initial_model_performance.png\")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/initial_confusion_matrices.png\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/initial_feature_importances.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Model Performance Comparison**\n",
"\n",
"Based on the performance metrics:\n",
"\n",
"1. **Logistic Regression** shows the highest recall (0.86) and ROC AUC (0.84), aligning best with our primary objective of maximizing sensitivity.\n",
"2. **CatBoost** offers the best balance between precision (0.16) and recall (0.19), resulting in the highest F1 score (0.17).\n",
"3. **XGBoost** and **LightGBM** have high precision but low recall, which doesn't align with our primary goal.\n",
"\n",
"**Confusion Matrices Analysis**\n",
"\n",
"From the confusion matrices:\n",
"\n",
"1. **Logistic Regression** correctly identifies the most stroke cases (36 TP out of 42), aligning with our goal of high sensitivity.\n",
"2. **CatBoost** shows a more balanced performance, with 8 true positives and 43 false positives.\n",
"3. **XGBoost** and **LightGBM** have poor sensitivity (4 TP out of 42), which doesn't meet our primary objective.\n",
"\n",
"**Feature Importance**\n",
"\n",
"Key findings:\n",
"\n",
"1. **Age** is consistently the most important feature across all models.\n",
"2. **Average glucose level** and **BMI** are also significant predictors.\n",
"3. **Hypertension** and **heart disease** show moderate importance, particularly in tree-based models.\n",
"\n",
"**Initial Conclusions**\n",
"\n",
"1. Logistic Regression aligns best with our primary goal of maximizing recall.\n",
"2. CatBoost offers a good balance between recall and precision, which could be valuable for minimizing false alarms while maintaining high sensitivity.\n",
"3. The dataset imbalance significantly affects model performance, particularly for tree-based models.\n",
"\n",
"**Next Steps**\n",
"\n",
"1. **Focus on Logistic Regression and CatBoost:** These two models show the most promise for our objective. We'll optimize them further.\n",
"2. **Hyperparameter Tuning:** Use RandomizedSearchCV to find better hyperparameters for both models, with a focus on maximizing recall.\n",
"3. **Threshold Adjustment:** After tuning, adjust the decision threshold to further improve recall, aiming for at least 90% while monitoring the impact on precision.\n",
"4. **False Negative Analysis:** Examine the characteristics of false negatives to gain insights for potential improvements and to understand what types of cases are being missed.\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" warnings.warn(\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [4, 465, 9.064439200562722, 0.024620299741838842, 22.56]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [5, 217, 6.688649649210669, 0.09081260669094816, 1]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [7, 263, 2.4112605015261037, 0.10853080074390413, 22.56]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [5, 158, 4.124427547844691, 0.22225481336000566, 22.56]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [10, 299, 6.134353016552677, 0.24866188432050645, 1]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [9, 398, 8.363611768281118, 0.012977072160663721, 22.56]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression Results:\n",
"Adjusted threshold: 0.3733\n",
"\n",
"Results on Validation set:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.63 0.77 940\n",
" 1 0.10 0.90 0.18 42\n",
"\n",
" accuracy 0.64 982\n",
" macro avg 0.55 0.77 0.48 982\n",
"weighted avg 0.96 0.64 0.75 982\n",
"\n",
"Confusion Matrix:\n",
"[[595 345]\n",
" [ 4 38]]\n",
"ROC AUC: 0.8425\n",
"PR AUC: 0.1680\n",
"F1 Score: 0.1788\n",
"Precision: 0.0992\n",
"Recall: 0.9048\n",
"Balanced Accuracy: 0.7689\n",
"\n",
"CatBoost Results:\n",
"Adjusted threshold: 0.5211\n",
"\n",
"Results on Validation set:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.73 0.84 940\n",
" 1 0.13 0.90 0.22 42\n",
"\n",
" accuracy 0.73 982\n",
" macro avg 0.56 0.82 0.53 982\n",
"weighted avg 0.96 0.73 0.81 982\n",
"\n",
"Confusion Matrix:\n",
"[[682 258]\n",
" [ 4 38]]\n",
"ROC AUC: 0.8613\n",
"PR AUC: 0.1692\n",
"F1 Score: 0.2249\n",
"Precision: 0.1284\n",
"Recall: 0.9048\n",
"Balanced Accuracy: 0.8151\n",
"\n",
"CatBoost selected as the best model.\n"
]
}
],
"source": [
"n_negative = np.sum(y_train == 0)\n",
"n_positive = np.sum(y_train == 1)\n",
"class_weight = {0: 1, 1: n_negative / n_positive}\n",
"\n",
"scaler = StandardScaler()\n",
"X_train_scaled = scaler.fit_transform(X_train)\n",
"X_val_scaled = scaler.transform(X_val)\n",
"\n",
"scoring = {\n",
" \"recall\": \"recall\",\n",
" \"precision\": \"precision\",\n",
" \"roc_auc\": \"roc_auc\",\n",
" \"avg_precision\": \"average_precision\",\n",
"}\n",
"\n",
"lr_param_space = {\n",
" \"C\": Real(0.1, 10, prior=\"log-uniform\"),\n",
" \"class_weight\": Categorical([\"balanced\", \"custom\"]),\n",
" \"solver\": Categorical([\"newton-cg\", \"lbfgs\", \"saga\"]),\n",
" \"max_iter\": Integer(1000, 50000),\n",
"}\n",
"\n",
"\n",
"class CustomLogisticRegression(LogisticRegression):\n",
" def set_params(self, **params):\n",
" if \"class_weight\" in params:\n",
" if params[\"class_weight\"] == \"custom\":\n",
" params[\"class_weight\"] = class_weight\n",
" return super().set_params(**params)\n",
"\n",
"\n",
"lr_bayes = BayesSearchCV(\n",
" CustomLogisticRegression(random_state=42),\n",
" lr_param_space,\n",
" n_iter=50,\n",
" cv=5,\n",
" scoring=scoring,\n",
" refit=\"recall\",\n",
" random_state=42,\n",
" n_jobs=-1,\n",
")\n",
"\n",
"cat_param_space = {\n",
" \"iterations\": Integer(100, 500),\n",
" \"depth\": Integer(4, 10),\n",
" \"learning_rate\": Real(0.01, 0.3, prior=\"log-uniform\"),\n",
" \"l2_leaf_reg\": Real(1, 10),\n",
" \"scale_pos_weight\": Categorical([1, n_negative / n_positive]),\n",
"}\n",
"\n",
"cat_bayes = BayesSearchCV(\n",
" CatBoostClassifier(random_state=42, verbose=False),\n",
" cat_param_space,\n",
" n_iter=50,\n",
" cv=5,\n",
" scoring=scoring,\n",
" refit=\"recall\",\n",
" random_state=42,\n",
" n_jobs=-1,\n",
")\n",
"\n",
"lr_bayes.fit(X_train_scaled, y_train)\n",
"cat_bayes.fit(X_train, y_train)\n",
"\n",
"best_lr = lr_bayes.best_estimator_\n",
"best_cat = cat_bayes.best_estimator_\n",
"\n",
"print(\"Logistic Regression Results:\")\n",
"lr_results = evaluate_model(\n",
" best_lr, X_val_scaled, y_val, dataset_name=\"Validation\", target_recall=0.9\n",
")\n",
"\n",
"print(\"\\nCatBoost Results:\")\n",
"cat_results = evaluate_model(\n",
" best_cat, X_val, y_val, dataset_name=\"Validation\", target_recall=0.9\n",
")\n",
"\n",
"if lr_results[\"roc_auc\"] > cat_results[\"roc_auc\"]:\n",
" best_model = best_lr\n",
" print(\"\\nLogistic Regression selected as the best model.\")\n",
"else:\n",
" best_model = best_cat\n",
" print(\"\\nCatBoost selected as the best model.\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"marker": {
"color": "#CC7B5C"
},
"name": "Logistic Regression",
"text": [
"6.803",
"0.715",
"0.482",
"0.157",
"0.068",
"1.370",
"1.188",
"0.032",
"0.065",
"0.065",
"0.000",
"0.442",
"0.133",
"0.408",
"0.386",
"1.463",
"0.142",
"0.110",
"0.058",
"0.141",
"1.234",
"0.439",
"0.395",
"2.395",
"0.111",
"2.629",
"0.833"
],
"textposition": "auto",
"type": "bar",
"x": [
"age",
"hypertension",
"heart_disease",
"ever_married",
"residence_type",
"avg_glucose_level",
"bmi",
"has_anomalies",
"gender_female",
"gender_male",
"gender_other",
"work_type_govt_job",
"work_type_never_worked",
"work_type_private",
"work_type_self-employed",
"work_type_children",
"smoking_status_unknown",
"smoking_status_formerly_smoked",
"smoking_status_never_smoked",
"smoking_status_smokes",
"age_glucose",
"age_hypertension",
"age_heart_disease",
"age_squared",
"glucose_squared",
"bmi_age",
"bmi_glucose"
],
"y": [
6.8031493939728485,
0.7148589013995387,
0.4823762325532518,
0.15677653657722748,
0.06796766088906134,
1.370302990435968,
1.187878369943353,
0.03191557559217683,
0.06509230005779808,
0.06509230005780382,
0,
0.44229558614737907,
0.1330504709140443,
0.4079936170963455,
0.3864288490408511,
1.4626484305716767,
0.14167284457798077,
0.11048460193241913,
0.0579469954951135,
0.14075431163921043,
1.2341867178467352,
0.4390191627364451,
0.3951347711204997,
2.3948770358588245,
0.11062861166780051,
2.6289536591771494,
0.8329649229271684
]
},
{
"marker": {
"color": "#D4A27F"
},
"name": "CatBoost",
"text": [
"15.173",
"1.898",
"0.144",
"0.614",
"0.419",
"5.176",
"5.094",
"0.305",
"0.000",
"0.056",
"0.000",
"0.651",
"0.000",
"1.012",
"0.000",
"0.834",
"1.128",
"0.722",
"0.533",
"0.466",
"10.637",
"4.664",
"0.882",
"18.731",
"2.027",
"24.227",
"4.606"
],
"textposition": "auto",
"type": "bar",
"x": [
"age",
"hypertension",
"heart_disease",
"ever_married",
"residence_type",
"avg_glucose_level",
"bmi",
"has_anomalies",
"gender_female",
"gender_male",
"gender_other",
"work_type_govt_job",
"work_type_never_worked",
"work_type_private",
"work_type_self-employed",
"work_type_children",
"smoking_status_unknown",
"smoking_status_formerly_smoked",
"smoking_status_never_smoked",
"smoking_status_smokes",
"age_glucose",
"age_hypertension",
"age_heart_disease",
"age_squared",
"glucose_squared",
"bmi_age",
"bmi_glucose"
],
"y": [
15.173084074020684,
1.897654125377911,
0.14447245870076317,
0.6140666564396703,
0.419388592570004,
5.176083782615482,
5.093773587297792,
0.30473245542626093,
0,
0.05607081629662262,
0,
0.6510697394610454,
0,
1.0117898188715806,
0,
0.8341893938715835,
1.1282687365246873,
0.7221354403498457,
0.5330862256011637,
0.4663886530591854,
10.637453901462637,
4.6635789761411965,
0.882459519187513,
18.730710672625783,
2.0269298675126795,
24.226756189174548,
4.605856317411379
]
}
],
"layout": {
"barmode": "group",
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"height": 600,
"legend": {
"title": {
"text": "Models"
}
},
"paper_bgcolor": "#EEECE2",
"plot_bgcolor": "#EEECE2",
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"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": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"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": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"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": "white",
"showlakes": true,
"showland": true,
"subunitcolor": "#C8D4E3"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "white",
"polar": {
"angularaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
},
"bgcolor": "white",
"radialaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"yaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"zaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"baxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"bgcolor": "white",
"caxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
}
}
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene B",
"size": 24
},
"text": "Feature Importances Across Models",
"x": 0.5,
"xanchor": "center",
"y": 0.95,
"yanchor": "top"
},
"width": 1200,
"xaxis": {
"tickangle": -45,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"text": "Features"
}
},
"yaxis": {
"gridcolor": "LightGrey",
"gridwidth": 1,
"showgrid": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"text": "Importance"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model_results = {\"Logistic Regression\": lr_results, \"CatBoost\": cat_results}\n",
"\n",
"plot_model_performance(\n",
" model_results,\n",
" [\"roc_auc\", \"pr_auc\", \"f1\", \"precision\", \"recall\", \"balanced_accuracy\"],\n",
" \"../images/tuned_model_performance.png\",\n",
")\n",
"\n",
"y_pred_dict = {\n",
" \"Logistic Regression\": lr_results[\"y_pred\"],\n",
" \"CatBoost\": cat_results[\"y_pred\"],\n",
"}\n",
"plot_combined_confusion_matrices(\n",
" model_results,\n",
" y_val,\n",
" y_pred_dict,\n",
" labels=[\"No Stroke\", \"Stroke\"],\n",
" save_path=\"../images/tuned_confusion_matrices.png\",\n",
")\n",
"\n",
"lr_importances = np.abs(best_lr.coef_[0])\n",
"cat_importances = best_cat.feature_importances_\n",
"\n",
"feature_importances = {\n",
" \"Logistic Regression\": dict(zip(X_train.columns, lr_importances)),\n",
" \"CatBoost\": dict(zip(X_train.columns, cat_importances)),\n",
"}\n",
"\n",
"plot_feature_importances(\n",
" feature_importances, save_path=\"../images/tuned_feature_importances.png\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/tuned_model_performance.png\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/tuned_confusion_matrices.png\")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/tuned_feature_importances.png\")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Logistic Regression False Negative Analysis:\n",
" age hypertension heart_disease ever_married residence_type \\\n",
"count 4.000000 4.0 4.0 4.00 4.00 \n",
"mean 49.750000 0.0 0.0 0.75 0.25 \n",
"std 7.804913 0.0 0.0 0.50 0.50 \n",
"min 39.000000 0.0 0.0 0.00 0.00 \n",
"25% 46.500000 0.0 0.0 0.75 0.00 \n",
"50% 52.000000 0.0 0.0 1.00 0.00 \n",
"75% 55.250000 0.0 0.0 1.00 0.25 \n",
"max 56.000000 0.0 0.0 1.00 1.00 \n",
"\n",
" avg_glucose_level bmi has_anomalies gender_female \\\n",
"count 4.000000 4.000000 4.0 4.00 \n",
"mean 114.457500 28.600000 0.0 0.75 \n",
"std 32.220145 2.743477 0.0 0.50 \n",
"min 92.980000 25.600000 0.0 0.00 \n",
"25% 96.565000 26.875000 0.0 0.75 \n",
"50% 101.310000 28.450000 0.0 1.00 \n",
"75% 119.202500 30.175000 0.0 1.00 \n",
"max 162.230000 31.900000 0.0 1.00 \n",
"\n",
" gender_male ... smoking_status_formerly_smoked \\\n",
"count 4.00 ... 4.0 \n",
"mean 0.25 ... 0.0 \n",
"std 0.50 ... 0.0 \n",
"min 0.00 ... 0.0 \n",
"25% 0.00 ... 0.0 \n",
"50% 0.00 ... 0.0 \n",
"75% 0.25 ... 0.0 \n",
"max 1.00 ... 0.0 \n",
"\n",
" smoking_status_never_smoked smoking_status_smokes age_glucose \\\n",
"count 4.00 4.00000 4.000000 \n",
"mean 0.25 0.50000 5787.390000 \n",
"std 0.50 0.57735 2283.870414 \n",
"min 0.00 0.00000 3812.640000 \n",
"25% 0.00 0.00000 4788.585000 \n",
"50% 0.00 0.50000 5126.020000 \n",
"75% 0.25 1.00000 6124.825000 \n",
"max 1.00 1.00000 9084.880000 \n",
"\n",
" age_hypertension age_heart_disease age_squared glucose_squared \\\n",
"count 4.0 4.0 4.000000 4.000000 \n",
"mean 0.0 0.0 2520.750000 13879.122625 \n",
"std 0.0 0.0 740.864529 8349.215714 \n",
"min 0.0 0.0 1521.000000 8645.280400 \n",
"25% 0.0 0.0 2181.000000 9329.083300 \n",
"50% 0.0 0.0 2713.000000 10276.318600 \n",
"75% 0.0 0.0 3052.750000 14826.357925 \n",
"max 0.0 0.0 3136.000000 26318.572900 \n",
"\n",
" bmi_age bmi_glucose \n",
"count 4.000000 4.000000 \n",
"mean 1413.575000 3261.974250 \n",
"std 185.146147 872.078959 \n",
"min 1154.400000 2380.288000 \n",
"25% 1344.600000 2765.344000 \n",
"50% 1468.400000 3119.365000 \n",
"75% 1537.375000 3615.995250 \n",
"max 1563.100000 4428.879000 \n",
"\n",
"[8 rows x 27 columns]\n",
"\n",
"CatBoost False Negative Analysis:\n",
" age hypertension heart_disease ever_married residence_type \\\n",
"count 4.000000 4.0 4.0 4.00000 4.00000 \n",
"mean 50.750000 0.0 0.0 0.50000 0.50000 \n",
"std 9.032349 0.0 0.0 0.57735 0.57735 \n",
"min 39.000000 0.0 0.0 0.00000 0.00000 \n",
"25% 46.500000 0.0 0.0 0.00000 0.00000 \n",
"50% 52.000000 0.0 0.0 0.50000 0.50000 \n",
"75% 56.250000 0.0 0.0 1.00000 1.00000 \n",
"max 60.000000 0.0 0.0 1.00000 1.00000 \n",
"\n",
" avg_glucose_level bmi has_anomalies gender_female \\\n",
"count 4.000000 4.000000 4.0 4.00 \n",
"mean 96.205000 31.225000 0.0 0.75 \n",
"std 6.745811 5.097957 0.0 0.50 \n",
"min 89.220000 25.600000 0.0 0.00 \n",
"25% 92.040000 28.600000 0.0 0.75 \n",
"50% 95.370000 30.750000 0.0 1.00 \n",
"75% 99.535000 33.375000 0.0 1.00 \n",
"max 104.860000 37.800000 0.0 1.00 \n",
"\n",
" gender_male ... smoking_status_formerly_smoked \\\n",
"count 4.00 ... 4.0 \n",
"mean 0.25 ... 0.0 \n",
"std 0.50 ... 0.0 \n",
"min 0.00 ... 0.0 \n",
"25% 0.00 ... 0.0 \n",
"50% 0.00 ... 0.0 \n",
"75% 0.25 ... 0.0 \n",
"max 1.00 ... 0.0 \n",
"\n",
" smoking_status_never_smoked smoking_status_smokes age_glucose \\\n",
"count 4.00000 4.00000 4.000000 \n",
"mean 0.50000 0.50000 4854.470000 \n",
"std 0.57735 0.57735 702.830932 \n",
"min 0.00000 0.00000 3812.640000 \n",
"25% 0.00000 0.00000 4788.585000 \n",
"50% 0.50000 0.50000 5126.020000 \n",
"75% 1.00000 1.00000 5191.905000 \n",
"max 1.00000 1.00000 5353.200000 \n",
"\n",
" age_hypertension age_heart_disease age_squared glucose_squared \\\n",
"count 4.0 4.0 4.000000 4.000000 \n",
"mean 0.0 0.0 2636.750000 9289.531500 \n",
"std 0.0 0.0 890.517593 1312.052438 \n",
"min 0.0 0.0 1521.000000 7960.208400 \n",
"25% 0.0 0.0 2181.000000 8474.012400 \n",
"50% 0.0 0.0 2713.000000 9101.149000 \n",
"75% 0.0 0.0 3168.750000 9916.668100 \n",
"max 0.0 0.0 3600.000000 10995.619600 \n",
"\n",
" bmi_age bmi_glucose \n",
"count 4.000000 4.000000 \n",
"mean 1598.375000 2997.883500 \n",
"std 477.143727 466.598406 \n",
"min 1154.400000 2380.288000 \n",
"25% 1344.600000 2765.344000 \n",
"50% 1485.550000 3119.365000 \n",
"75% 1739.325000 3351.904500 \n",
"max 2268.000000 3372.516000 \n",
"\n",
"[8 rows x 27 columns]\n"
]
}
],
"source": [
"lr_false_negatives = X_val[(y_val == 1) & (lr_results[\"y_pred\"] == 0)]\n",
"cat_false_negatives = X_val[(y_val == 1) & (cat_results[\"y_pred\"] == 0)]\n",
"\n",
"print(\"\\nLogistic Regression False Negative Analysis:\")\n",
"print(lr_false_negatives.describe())\n",
"\n",
"print(\"\\nCatBoost False Negative Analysis:\")\n",
"print(cat_false_negatives.describe())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Model Performance Comparison**\n",
"\n",
"**Logistic Regression:**\n",
"\n",
"- Recall: 0.9048\n",
"- Precision: 0.1095\n",
"- ROC AUC: 0.8594\n",
"- PR AUC: 0.1984\n",
"- F1 Score: 0.1954\n",
"\n",
"**CatBoost:**\n",
"\n",
"- Recall: 0.9048\n",
"- Precision: 0.1439\n",
"- ROC AUC: 0.8883\n",
"- PR AUC: 0.2314\n",
"- F1 Score: 0.2484\n",
"\n",
"Both models maintain high recall (0.9048), which is crucial for not missing potential stroke cases. CatBoost demonstrates better performance across all metrics, with higher precision (0.1439 vs 0.1095), ROC AUC (0.8883 vs 0.8594), PR AUC (0.2314 vs 0.1984), and F1 score (0.2484 vs 0.1954) compared to Logistic Regression. This indicates CatBoost's superior ability to distinguish between stroke and non-stroke cases in this imbalanced dataset.\n",
"\n",
"**Confusion Matrices Analysis**\n",
"\n",
"1. **Logistic Regression**: 38 true positives, 4 false negatives, 345 false positives\n",
"2. **CatBoost**: 38 true positives, 4 false negatives, 258 false positives\n",
"\n",
"Both models correctly identify 38 out of 42 stroke cases (high recall). CatBoost generates fewer false positives (258 vs 345), resulting in its higher precision.\n",
"\n",
"**False Negative Analysis**\n",
"\n",
"**Logistic Regression:**\n",
"\n",
"- Mean age: 49.75 years\n",
"- No cases with hypertension or heart disease\n",
"- Average glucose level: 114.46\n",
"- Average BMI: 28.60\n",
"\n",
"**CatBoost:**\n",
"\n",
"- Mean age: 50.75 years\n",
"- No cases with hypertension or heart disease\n",
"- Average glucose level: 96.21\n",
"- Average BMI: 31.23\n",
"\n",
"The 4 stroke cases missed by both models are relatively younger patients with no history of hypertension or heart disease, but with moderately elevated BMI levels. Interestingly, CatBoost's false negatives have a lower average glucose level compared to Logistic Regression's.\n",
"\n",
"These borderline cases are challenging for the models as they lack strong predictors like hypertension and heart disease, falling into a gray area between low and high risk. Improving prediction for these edge cases remains an area for further model refinement.\n",
"\n",
"**Conclusions**\n",
"\n",
"1. Both models achieve high recall (0.9048), but CatBoost outperforms Logistic Regression across all key metrics.\n",
"2. CatBoost should be preferred due to its superior performance, especially in reducing false positives.\n",
"3. The models struggle with similar borderline cases, suggesting a common challenge in identifying subtle risk factors.\n",
"\n",
"**Next Steps**\n",
"\n",
"1. **Ensemble Modeling**: Experiment with combining CatBoost and Logistic Regression predictions to create a more robust model that leverages the strengths of both approaches.\n",
"2. **Threshold Optimization**: Fine-tune the decision threshold to strike the optimal balance between recall and precision based on the relative costs of false positives vs false negatives.\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 10 important features based on SHAP values:\n",
" feature importance\n",
"23 age_squared 0.318917\n",
"0 age 0.264264\n",
"20 age_glucose 0.202801\n",
"25 bmi_age 0.156302\n",
"21 age_hypertension 0.071553\n",
"5 avg_glucose_level 0.029351\n",
"6 bmi 0.026249\n",
"1 hypertension 0.025874\n",
"24 glucose_squared 0.021795\n",
"22 age_heart_disease 0.017254\n",
"\n",
"Original Ensemble Model Evaluation (Validation Set):\n",
"Adjusted threshold: 0.4059\n",
"\n",
"Results on Validation set:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.63 0.77 940\n",
" 1 0.10 0.90 0.18 42\n",
"\n",
" accuracy 0.64 982\n",
" macro avg 0.55 0.77 0.47 982\n",
"weighted avg 0.95 0.64 0.74 982\n",
"\n",
"Confusion Matrix:\n",
"[[588 352]\n",
" [ 4 38]]\n",
"ROC AUC: 0.8517\n",
"PR AUC: 0.1657\n",
"F1 Score: 0.1759\n",
"Precision: 0.0974\n",
"Recall: 0.9048\n",
"Balanced Accuracy: 0.7651\n",
"\n",
"Top 10 Features Ensemble Model Evaluation (Validation Set):\n",
"Adjusted threshold: 0.4657\n",
"\n",
"Results on Validation set:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.69 0.81 940\n",
" 1 0.11 0.90 0.20 42\n",
"\n",
" accuracy 0.69 982\n",
" macro avg 0.55 0.79 0.51 982\n",
"weighted avg 0.96 0.69 0.79 982\n",
"\n",
"Confusion Matrix:\n",
"[[644 296]\n",
" [ 4 38]]\n",
"ROC AUC: 0.8577\n",
"PR AUC: 0.1658\n",
"F1 Score: 0.2021\n",
"Precision: 0.1138\n",
"Recall: 0.9048\n",
"Balanced Accuracy: 0.7949\n",
"\n",
"Top 10 Features Ensemble Model selected as the best model.\n",
"\n",
"Best Model Evaluation on Test Set:\n",
"Adjusted threshold: 0.2401\n",
"\n",
"Results on Test set:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.46 0.63 940\n",
" 1 0.07 0.90 0.13 42\n",
"\n",
" accuracy 0.48 982\n",
" macro avg 0.53 0.68 0.38 982\n",
"weighted avg 0.95 0.48 0.61 982\n",
"\n",
"Confusion Matrix:\n",
"[[435 505]\n",
" [ 4 38]]\n",
"ROC AUC: 0.8062\n",
"PR AUC: 0.1930\n",
"F1 Score: 0.1299\n",
"Precision: 0.0700\n",
"Recall: 0.9048\n",
"Balanced Accuracy: 0.6838\n"
]
}
],
"source": [
"class CustomVotingClassifier(VotingClassifier):\n",
" def fit(self, X, y, sample_weight=None):\n",
" return super().fit(X, y)\n",
"\n",
"\n",
"class CustomLogisticRegressionWrapper(BaseEstimator, ClassifierMixin):\n",
" def __init__(self, model, class_weight):\n",
" self.model = model\n",
" self.class_weight = class_weight\n",
"\n",
" def fit(self, X, y, sample_weight=None):\n",
" if sample_weight is None:\n",
" sample_weight = np.ones(len(y))\n",
" sample_weight *= np.array([self.class_weight[yi] for yi in y])\n",
" return self.model.fit(X, y, sample_weight=sample_weight)\n",
"\n",
" def predict(self, X):\n",
" return self.model.predict(X)\n",
"\n",
" def predict_proba(self, X):\n",
" return self.model.predict_proba(X)\n",
"\n",
"\n",
"if hasattr(best_lr, \"class_weight\") and best_lr.class_weight == \"custom\":\n",
" wrapped_lr = CustomLogisticRegressionWrapper(best_lr, class_weight)\n",
"else:\n",
" wrapped_lr = best_lr\n",
"\n",
"ensemble_model = CustomVotingClassifier(\n",
" estimators=[(\"lr\", wrapped_lr), (\"cb\", best_cat)], voting=\"soft\"\n",
")\n",
"\n",
"ensemble_model.fit(X_train_scaled, y_train)\n",
"\n",
"explainer = shap.TreeExplainer(ensemble_model.named_estimators_[\"cb\"])\n",
"shap_values = explainer.shap_values(X_train)\n",
"\n",
"feature_importance = pd.DataFrame(\n",
" {\"feature\": X_train.columns, \"importance\": np.abs(shap_values).mean(0)}\n",
")\n",
"feature_importance = feature_importance.sort_values(\"importance\", ascending=False)\n",
"\n",
"print(\"Top 10 important features based on SHAP values:\")\n",
"print(feature_importance.head(10))\n",
"\n",
"top_features = feature_importance[\"feature\"].head(10).tolist()\n",
"\n",
"X_train_top = X_train[top_features]\n",
"X_val_top = X_val[top_features]\n",
"X_test_top = X_test[top_features]\n",
"\n",
"scaler_top = StandardScaler()\n",
"X_train_top_scaled = scaler_top.fit_transform(X_train_top)\n",
"X_val_top_scaled = scaler_top.transform(X_val_top)\n",
"X_test_top_scaled = scaler_top.transform(X_test_top)\n",
"\n",
"if hasattr(best_lr, \"class_weight\") and best_lr.class_weight == \"custom\":\n",
" lr_model_top = CustomLogisticRegressionWrapper(\n",
" LogisticRegression(**best_lr.get_params()), class_weight\n",
" )\n",
"else:\n",
" lr_model_top = LogisticRegression(**best_lr.get_params())\n",
"\n",
"cb_model_top = CatBoostClassifier(**best_cat.get_params())\n",
"\n",
"ensemble_model_top = CustomVotingClassifier(\n",
" estimators=[(\"lr\", lr_model_top), (\"cb\", cb_model_top)], voting=\"soft\"\n",
")\n",
"\n",
"ensemble_model_top.fit(X_train_top_scaled, y_train)\n",
"\n",
"print(\"\\nOriginal Ensemble Model Evaluation (Validation Set):\")\n",
"original_val_results = evaluate_model(\n",
" ensemble_model, X_val_scaled, y_val, dataset_name=\"Validation\", target_recall=0.9\n",
")\n",
"\n",
"print(\"\\nTop 10 Features Ensemble Model Evaluation (Validation Set):\")\n",
"top_features_val_results = evaluate_model(\n",
" ensemble_model_top,\n",
" X_val_top_scaled,\n",
" y_val,\n",
" dataset_name=\"Validation\",\n",
" target_recall=0.9,\n",
")\n",
"\n",
"if top_features_val_results[\"roc_auc\"] > original_val_results[\"roc_auc\"]:\n",
" best_model = ensemble_model_top\n",
" best_X_test = X_test_top_scaled\n",
" print(\"\\nTop 10 Features Ensemble Model selected as the best model.\")\n",
"else:\n",
" best_model = ensemble_model\n",
" best_X_test = X_test_scaled\n",
" print(\"\\nOriginal Ensemble Model selected as the best model.\")\n",
"\n",
"print(\"\\nBest Model Evaluation on Test Set:\")\n",
"test_results = evaluate_model(\n",
" best_model, best_X_test, y_test, dataset_name=\"Test\", target_recall=0.9\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"marker": {
"color": "#CC7B5C"
},
"name": "Original Ensemble",
"text": [
"15.173",
"1.898",
"0.144",
"0.614",
"0.419",
"5.176",
"5.094",
"0.305",
"0.000",
"0.056",
"0.000",
"0.651",
"0.000",
"1.012",
"0.000",
"0.834",
"1.128",
"0.722",
"0.533",
"0.466",
"10.637",
"4.664",
"0.882",
"18.731",
"2.027",
"24.227",
"4.606"
],
"textposition": "auto",
"type": "bar",
"x": [
"age",
"hypertension",
"heart_disease",
"ever_married",
"residence_type",
"avg_glucose_level",
"bmi",
"has_anomalies",
"gender_female",
"gender_male",
"gender_other",
"work_type_govt_job",
"work_type_never_worked",
"work_type_private",
"work_type_self-employed",
"work_type_children",
"smoking_status_unknown",
"smoking_status_formerly_smoked",
"smoking_status_never_smoked",
"smoking_status_smokes",
"age_glucose",
"age_hypertension",
"age_heart_disease",
"age_squared",
"glucose_squared",
"bmi_age",
"bmi_glucose"
],
"y": [
15.173084074020684,
1.897654125377911,
0.14447245870076317,
0.6140666564396703,
0.419388592570004,
5.176083782615482,
5.093773587297792,
0.30473245542626093,
0,
0.05607081629662262,
0,
0.6510697394610454,
0,
1.0117898188715806,
0,
0.8341893938715835,
1.1282687365246873,
0.7221354403498457,
0.5330862256011637,
0.4663886530591854,
10.637453901462637,
4.6635789761411965,
0.882459519187513,
18.730710672625783,
2.0269298675126795,
24.226756189174548,
4.605856317411379
]
},
{
"marker": {
"color": "#D4A27F"
},
"name": "Top 10 Features Ensemble",
"text": [
"16.699",
"21.670",
"14.002",
"26.662",
"3.465",
"2.449",
"2.396",
"9.618",
"1.736",
"1.304"
],
"textposition": "auto",
"type": "bar",
"x": [
"age_squared",
"age",
"age_glucose",
"bmi_age",
"age_hypertension",
"avg_glucose_level",
"bmi",
"hypertension",
"glucose_squared",
"age_heart_disease"
],
"y": [
16.698804598419944,
21.670238532383483,
14.001726986357959,
26.661523336251914,
3.4647727889574247,
2.4488384806693952,
2.3956496329982446,
9.618170249019752,
1.7362343903280963,
1.3040410046137674
]
}
],
"layout": {
"barmode": "group",
"font": {
"color": "#191919",
"family": "Styrene A",
"size": 14
},
"height": 600,
"legend": {
"title": {
"text": "Models"
}
},
"paper_bgcolor": "#EEECE2",
"plot_bgcolor": "#EEECE2",
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "white",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "#C8D4E3",
"linecolor": "#C8D4E3",
"minorgridcolor": "#C8D4E3",
"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": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"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": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"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": "white",
"showlakes": true,
"showland": true,
"subunitcolor": "#C8D4E3"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "white",
"polar": {
"angularaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
},
"bgcolor": "white",
"radialaxis": {
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"yaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
},
"zaxis": {
"backgroundcolor": "white",
"gridcolor": "#DFE8F3",
"gridwidth": 2,
"linecolor": "#EBF0F8",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#EBF0F8"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"baxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
},
"bgcolor": "white",
"caxis": {
"gridcolor": "#DFE8F3",
"linecolor": "#A2B1C6",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "#EBF0F8",
"linecolor": "#EBF0F8",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#EBF0F8",
"zerolinewidth": 2
}
}
},
"title": {
"font": {
"color": "#191919",
"family": "Styrene B",
"size": 24
},
"text": "Feature Importances Across Models",
"x": 0.5,
"xanchor": "center",
"y": 0.95,
"yanchor": "top"
},
"width": 1200,
"xaxis": {
"tickangle": -45,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"text": "Features"
}
},
"yaxis": {
"gridcolor": "LightGrey",
"gridwidth": 1,
"showgrid": true,
"tickfont": {
"color": "#191919",
"family": "Styrene A",
"size": 12
},
"title": {
"text": "Importance"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model_results = {\n",
" \"Original Ensemble\": original_val_results,\n",
" \"Top 10 Features Ensemble\": top_features_val_results,\n",
"}\n",
"\n",
"y_pred_dict = {\n",
" \"Original Ensemble\": original_val_results[\"y_pred\"],\n",
" \"Top 10 Features Ensemble\": top_features_val_results[\"y_pred\"],\n",
"}\n",
"\n",
"plot_model_performance(\n",
" model_results,\n",
" [\"roc_auc\", \"pr_auc\", \"f1\", \"precision\", \"recall\", \"balanced_accuracy\"],\n",
" save_path=\"../images/ensemble_model_performance_comparison.png\",\n",
")\n",
"\n",
"plot_combined_confusion_matrices(\n",
" model_results,\n",
" y_test,\n",
" y_pred_dict,\n",
" labels=[\"No Stroke\", \"Stroke\"],\n",
" save_path=\"../images/ensemble_confusion_matrices_comparison.png\",\n",
")\n",
"\n",
"original_importances = ensemble_model.named_estimators_[\"cb\"].get_feature_importance()\n",
"top_features_importances = ensemble_model_top.named_estimators_[\n",
" \"cb\"\n",
"].get_feature_importance()\n",
"\n",
"feature_importances = {\n",
" \"Original Ensemble\": dict(zip(X.columns, original_importances)),\n",
" \"Top 10 Features Ensemble\": dict(zip(top_features, top_features_importances)),\n",
"}\n",
"\n",
"plot_feature_importances(\n",
" feature_importances,\n",
" save_path=\"../images/ensemble_feature_importances_comparison.png\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/ensemble_model_performance_comparison.png\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/ensemble_confusion_matrices_comparison.png\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"../images/ensemble_feature_importances_comparison.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Final Model Selection: CatBoost**\n",
"\n",
"Based on the results provided of the ensemble model, we select the CatBoost model as our final model for stroke prediction. Here's why:\n",
"\n",
"1. **Superior Performance**: CatBoost outperforms both Logistic Regression and the ensemble model across key metrics:\n",
"\n",
" - ROC AUC: 0.8883 (highest among all models)\n",
" - PR AUC: 0.2314 (highest)\n",
" - F1 Score: 0.2484 (highest)\n",
" - Precision: 0.1439 (highest)\n",
" - Recall: 0.9048 (matches the target recall of other models)\n",
" - Balanced Accuracy: 0.8322 (highest)\n",
"\n",
"2. **Effective Handling of Class Imbalance**: CatBoost maintains high recall (0.9048) on the positive class while achieving better precision than other models, crucial for imbalanced medical datasets.\n",
"\n",
"3. **Reduced False Positives**: CatBoost produces fewer false positives (226) compared to Logistic Regression (309), which is important for minimizing unnecessary follow-ups or treatments.\n",
"\n",
"4. **Gradient Boosting Advantages**: As a gradient boosting model, CatBoost can capture complex, non-linear relationships in the data, which may be particularly beneficial for stroke prediction given the intricate interplay of risk factors.\n",
"\n",
"**Key Performance Metrics (Validation Set):**\n",
"\n",
"- Recall: 0.9048\n",
"- Precision: 0.1439\n",
"- ROC AUC: 0.8883\n",
"- PR AUC: 0.2314\n",
"- F1 Score: 0.2484\n",
"- Balanced Accuracy: 0.8322\n",
"\n",
"**Model Behavior**\n",
"\n",
"- The model maintains the target high recall (0.9048) while achieving better precision than other approaches.\n",
"- The adjusted threshold of 0.5726 indicates a well-balanced decision boundary for this imbalanced dataset.\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [5, 217, 6.688649649210669, 0.09081260669094816, 1]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [7, 263, 2.4112605015261037, 0.10853080074390413, 22.56]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [10, 299, 6.134353016552677, 0.24866188432050645, 1]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [9, 398, 8.363611768281118, 0.012977072160663721, 22.56]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [8, 154, 9.723569763096634, 0.19113341639259593, 1]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [10, 115, 1.2379536766948178, 0.14337174451197832, 1]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [6, 317, 8.962340030644103, 0.17773349759654689, 1]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [8, 220, 3.3061483134978777, 0.04615737815148972, 22.56]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [6, 210, 1.4129630611802109, 0.011040989233230441, 1]\n",
"\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/Users/vytautasbunevicius/stroke-risk-predictor/venv/lib/python3.12/site-packages/skopt/optimizer/optimizer.py:517: UserWarning:\n",
"\n",
"The objective has been evaluated at point [4, 100, 10.0, 0.01, 22.56] before, using random point [5, 192, 3.984298822980576, 0.015806690046424116, 22.56]\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"CatBoost Results on Validation Set:\n",
"Adjusted threshold: 0.5104\n",
"\n",
"Results on Validation set:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.71 0.83 940\n",
" 1 0.12 0.90 0.21 42\n",
"\n",
" accuracy 0.71 982\n",
" macro avg 0.56 0.81 0.52 982\n",
"weighted avg 0.96 0.71 0.80 982\n",
"\n",
"Confusion Matrix:\n",
"[[663 277]\n",
" [ 4 38]]\n",
"ROC AUC: 0.8621\n",
"PR AUC: 0.1668\n",
"F1 Score: 0.2129\n",
"Precision: 0.1206\n",
"Recall: 0.9048\n",
"Balanced Accuracy: 0.8050\n",
"\n",
"CatBoost model and feature names saved successfully.\n",
"\n",
"CatBoost Results on Test Set:\n",
"Adjusted threshold: 0.1686\n",
"\n",
"Results on Test set:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.99 0.44 0.61 940\n",
" 1 0.07 0.90 0.12 42\n",
"\n",
" accuracy 0.46 982\n",
" macro avg 0.53 0.67 0.37 982\n",
"weighted avg 0.95 0.46 0.59 982\n",
"\n",
"Confusion Matrix:\n",
"[[411 529]\n",
" [ 4 38]]\n",
"ROC AUC: 0.8096\n",
"PR AUC: 0.1533\n",
"F1 Score: 0.1248\n",
"Precision: 0.0670\n",
"Recall: 0.9048\n",
"Balanced Accuracy: 0.6710\n"
]
}
],
"source": [
"n_negative = np.sum(y_train == 0)\n",
"n_positive = np.sum(y_train == 1)\n",
"class_weight = {0: 1, 1: n_negative / n_positive}\n",
"\n",
"scoring = {\n",
" \"recall\": \"recall\",\n",
" \"precision\": \"precision\",\n",
" \"roc_auc\": \"roc_auc\",\n",
" \"avg_precision\": \"average_precision\",\n",
"}\n",
"\n",
"cat_param_space = {\n",
" \"iterations\": Integer(100, 500),\n",
" \"depth\": Integer(4, 10),\n",
" \"learning_rate\": Real(0.01, 0.3, prior=\"log-uniform\"),\n",
" \"l2_leaf_reg\": Real(1, 10),\n",
" \"scale_pos_weight\": Categorical([1, n_negative / n_positive]),\n",
"}\n",
"\n",
"stratified_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
"cat_bayes = BayesSearchCV(\n",
" CatBoostClassifier(random_state=42, verbose=False),\n",
" cat_param_space,\n",
" n_iter=50,\n",
" cv=stratified_cv,\n",
" scoring=scoring,\n",
" refit=\"recall\",\n",
" random_state=42,\n",
" n_jobs=-1,\n",
")\n",
"\n",
"cat_bayes.fit(X_train, y_train)\n",
"\n",
"best_cat = cat_bayes.best_estimator_\n",
"\n",
"print(\"\\nCatBoost Results on Validation Set:\")\n",
"cat_results = evaluate_model(\n",
" best_cat, X_val, y_val, dataset_name=\"Validation\", target_recall=0.9\n",
")\n",
"\n",
"joblib.dump(best_cat, \"../models/catboost_final_model.joblib\")\n",
"joblib.dump(X_train.columns.tolist(), \"../models/feature_names.joblib\")\n",
"joblib.dump(\"catboost\", \"../models/best_model_type.joblib\")\n",
"\n",
"print(\"\\nCatBoost model and feature names saved successfully.\")\n",
"\n",
"print(\"\\nCatBoost Results on Test Set:\")\n",
"cat_test_results = evaluate_model(\n",
" best_cat, X_test, y_test, dataset_name=\"Test\", target_recall=0.9\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summary\n",
"\n",
"**Overview**\n",
"This project focused on developing a machine learning model to predict the likelihood of stroke occurrence based on various patient attributes. Using the Stroke Prediction Dataset, we aimed to create a tool that could assist healthcare providers in identifying high-risk patients and potentially reduce the impact of this serious medical condition.\n",
"\n",
"**Key Steps**\n",
"1. **Data Exploration and Preprocessing**: We analyzed the dataset, handled missing values, and encoded categorical variables.\n",
"2. **Feature Engineering**: Created interaction terms and polynomial features to capture complex relationships in the data.\n",
"3. **Statistical Analysis**: Conducted tests to understand the relationships between various factors and stroke risk.\n",
"4. **Model Development**: Experimented with multiple algorithms including Logistic Regression, XGBoost, LightGBM, and CatBoost.\n",
"5. **Model Optimization**: Used Bayesian optimization for hyperparameter tuning and explored ensemble methods.\n",
"6. **Performance Evaluation**: Focused on maximizing recall while maintaining acceptable precision, given the critical nature of stroke prediction.\n",
"\n",
"**Final Model**\n",
"After extensive experimentation, we selected the CatBoost model as our final predictor due to its superior performance across key metrics:\n",
"\n",
"- **Recall**: 0.9048 (on validation set)\n",
"- **Precision**: 0.1206\n",
"- **ROC AUC**: 0.8621\n",
"- **PR AUC**: 0.1668\n",
"- **F1 Score**: 0.2129\n",
"- **Balanced Accuracy**: 0.8050\n",
"\n",
"**Key Findings**\n",
"- Age and glucose levels were consistently the most important predictors of stroke risk.\n",
"- The model successfully maintains high recall, crucial for identifying potential stroke cases.\n",
"- There's a trade-off between precision and recall due to the imbalanced nature of the dataset.\n",
"\n",
"**Challenges and Future Work**\n",
"- Dealing with class imbalance remained a significant challenge throughout the project.\n",
"- Future work could focus on gathering more data, especially for the minority class, to improve model performance.\n",
"- Exploring more advanced techniques like anomaly detection or semi-supervised learning could potentially yield better results.\n",
"\n",
"**Conclusion**\n",
"This project demonstrates the potential of machine learning in healthcare, particularly for risk prediction. While the model shows promising results in identifying high-risk patients, it's important to note that it should be used as a supportive tool in conjunction with clinical expertise, not as a standalone diagnostic system."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"%run -i ../src/utils/stroke_risk_utils.py"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}