Datasets
Models
Downloads: 1
[c33074]:
/
main.ipynb
7245 lines (7244 with data), 1.6 MB
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predicting Diabetes Patient Readmission"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Background\n",
"It was reported that in 2011 more than 3.3 million patients were readmitted in the US within 30 days of being ### discharged, and they were associated with about \n",
"41 billion in hospital costs. The need for readmission indicates that inadequate care #### was provided to the #### patient at the time of first admission. The readmission rate has become an important metric measuring the overall quality of a hospital.\n",
"\n",
"Diabetes is the 7th leading cause of death and affects about 23.6 million people in the US. 1.4 million Americans are diagnosed with diabetes every year. Hospital readmission being a major concern in diabetes care, over 250 million dollars was spent on treatment of readmitted diabetic patients in 2011. Early identification of patients facing a high risk of readmission can enable healthcare providers to conduct additional investigations and possibly prevent future readmissions.\n",
"\n",
"In this project, I build a machine learning classifier model to predict diabetes patients with high risk of readmission. Note that higher sensitivity (recall) is more desirable for hospitals because it is more crucial to correctly identify \"high risk\" patients who are likely to be readmitted than identifying \"low risk\" patients.\n",
"\n",
"# Dataset Description\n",
"The dataset represents 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes 50 features representing 101766 diabetes patients and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria:\n",
"\n",
"It is an inpatient encounter (a hospital admission).\n",
"It is a diabetic encounter, that is, one during which any kind of diabetes was entered to the system as a diagnosis.\n",
"The length of stay was at least 1 day and at most 14 days.\n",
"Laboratory tests were performed during the encounter.\n",
"Medications were administered during the encounter.\n",
"The data contains such attributes as patient number, race, gender, age, admission type, time in hospital, medical specialty of admitting physician, number of lab test performed, HbA1c test result, diagnosis, number of medication, diabetic medications, number of outpatient, inpatient, and emergency visits in the year before the hospitalization, etc.\n",
"\n",
"Source: UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008\n",
"\n",
"\n",
"In this project I will demonstrate how to build a model predicting readmission for patients with diabetes in Python using the following steps\n",
"\n",
"-data exploration\n",
"\n",
"-feature engineering\n",
"\n",
"-building training/validation/test samples\n",
"\n",
"-model selection\n",
"\n",
"-model evaluation\n",
"\n",
"#### Project Definition\n",
"\n",
"Predict if a patient with diabetes will be readmitted to the hospital within 30 days.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import time\n",
"import seaborn as sns\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/sonalisreedhar\n"
]
}
],
"source": [
"cd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/sonalisreedhar/Documents\n"
]
}
],
"source": [
"cd Documents"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/sonalisreedhar/Documents/MACHINE LEARNING\n"
]
}
],
"source": [
"cd MACHINE LEARNING"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/sonalisreedhar/Documents/MACHINE LEARNING/FINAL_PROJECT\n"
]
}
],
"source": [
"cd /Users/sonalisreedhar/Documents/MACHINE LEARNING/FINAL_PROJECT"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Errno 2] No such file or directory: 'FINAL_PROJECT'\n",
"/Users/sonalisreedhar/Documents/MACHINE LEARNING/FINAL_PROJECT\n"
]
}
],
"source": [
"cd FINAL_PROJECT"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Errno 2] No such file or directory: '\\u2068'\n",
"/Users/sonalisreedhar/Documents/MACHINE LEARNING/FINAL_PROJECT\n"
]
}
],
"source": [
"cd "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the dataset\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# load the csv file\n",
"df = pd.read_csv('diabetic_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of samples: 101766\n"
]
}
],
"source": [
"print('Number of samples:',len(df))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Overview of the dataset\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 101766 entries, 0 to 101765\n",
"Data columns (total 50 columns):\n",
"encounter_id 101766 non-null int64\n",
"patient_nbr 101766 non-null int64\n",
"race 101766 non-null object\n",
"gender 101766 non-null object\n",
"age 101766 non-null object\n",
"weight 101766 non-null object\n",
"admission_type_id 101766 non-null int64\n",
"discharge_disposition_id 101766 non-null int64\n",
"admission_source_id 101766 non-null int64\n",
"time_in_hospital 101766 non-null int64\n",
"payer_code 101766 non-null object\n",
"medical_specialty 101766 non-null object\n",
"num_lab_procedures 101766 non-null int64\n",
"num_procedures 101766 non-null int64\n",
"num_medications 101766 non-null int64\n",
"number_outpatient 101766 non-null int64\n",
"number_emergency 101766 non-null int64\n",
"number_inpatient 101766 non-null int64\n",
"diag_1 101766 non-null object\n",
"diag_2 101766 non-null object\n",
"diag_3 101766 non-null object\n",
"number_diagnoses 101766 non-null int64\n",
"max_glu_serum 101766 non-null object\n",
"A1Cresult 101766 non-null object\n",
"metformin 101766 non-null object\n",
"repaglinide 101766 non-null object\n",
"nateglinide 101766 non-null object\n",
"chlorpropamide 101766 non-null object\n",
"glimepiride 101766 non-null object\n",
"acetohexamide 101766 non-null object\n",
"glipizide 101766 non-null object\n",
"glyburide 101766 non-null object\n",
"tolbutamide 101766 non-null object\n",
"pioglitazone 101766 non-null object\n",
"rosiglitazone 101766 non-null object\n",
"acarbose 101766 non-null object\n",
"miglitol 101766 non-null object\n",
"troglitazone 101766 non-null object\n",
"tolazamide 101766 non-null object\n",
"examide 101766 non-null object\n",
"citoglipton 101766 non-null object\n",
"insulin 101766 non-null object\n",
"glyburide-metformin 101766 non-null object\n",
"glipizide-metformin 101766 non-null object\n",
"glimepiride-pioglitazone 101766 non-null object\n",
"metformin-rosiglitazone 101766 non-null object\n",
"metformin-pioglitazone 101766 non-null object\n",
"change 101766 non-null object\n",
"diabetesMed 101766 non-null object\n",
"readmitted 101766 non-null object\n",
"dtypes: int64(13), object(37)\n",
"memory usage: 38.8+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" encounter_id patient_nbr time_in_hospital num_lab_procedures \\\n",
"count 9.934300e+04 9.934300e+04 99343.000000 99343.000000 \n",
"mean 1.649689e+08 5.426117e+07 4.379332 42.906929 \n",
"std 1.026535e+08 3.873426e+07 2.968409 19.610032 \n",
"min 1.252200e+04 1.350000e+02 1.000000 1.000000 \n",
"25% 8.469034e+07 2.338675e+07 2.000000 31.000000 \n",
"50% 1.522321e+08 4.541774e+07 4.000000 44.000000 \n",
"75% 2.301018e+08 8.756007e+07 6.000000 57.000000 \n",
"max 4.438672e+08 1.895026e+08 14.000000 132.000000 \n",
"\n",
" num_procedures num_medications number_outpatient number_emergency \\\n",
"count 99343.000000 99343.000000 99343.000000 99343.000000 \n",
"mean 1.334236 15.979062 0.369246 0.198444 \n",
"std 1.702786 8.094909 1.265142 0.937734 \n",
"min 0.000000 1.000000 0.000000 0.000000 \n",
"25% 0.000000 10.000000 0.000000 0.000000 \n",
"50% 1.000000 15.000000 0.000000 0.000000 \n",
"75% 2.000000 20.000000 0.000000 0.000000 \n",
"max 6.000000 81.000000 42.000000 76.000000 \n",
"\n",
" number_inpatient number_diagnoses ... med_spec_InternalMedicine \\\n",
"count 99343.000000 99343.000000 ... 99343.000000 \n",
"mean 0.630935 7.401709 ... 0.143312 \n",
"std 1.260428 1.941013 ... 0.350392 \n",
"min 0.000000 1.000000 ... 0.000000 \n",
"25% 0.000000 6.000000 ... 0.000000 \n",
"50% 0.000000 8.000000 ... 0.000000 \n",
"75% 1.000000 9.000000 ... 0.000000 \n",
"max 21.000000 16.000000 ... 1.000000 \n",
"\n",
" med_spec_Nephrology med_spec_Orthopedics \\\n",
"count 99343.000000 99343.000000 \n",
"mean 0.015492 0.014012 \n",
"std 0.123499 0.117541 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 1.000000 1.000000 \n",
"\n",
" med_spec_Orthopedics-Reconstructive med_spec_Other \\\n",
"count 99343.000000 99343.000000 \n",
"mean 0.012381 0.155532 \n",
"std 0.110581 0.362413 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 1.000000 1.000000 \n",
"\n",
" med_spec_Radiologist med_spec_Surgery-General med_spec_UNK \\\n",
"count 99343.000000 99343.000000 99343.000000 \n",
"mean 0.011284 0.030792 0.489375 \n",
"std 0.105626 0.172755 0.499890 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 1.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" age_group has_weight \n",
"count 99343.000000 99343.000000 \n",
"mean 55.878602 0.031457 \n",
"std 25.049986 0.174549 \n",
"min 0.000000 0.000000 \n",
"25% 50.000000 0.000000 \n",
"50% 60.000000 0.000000 \n",
"75% 70.000000 0.000000 \n",
"max 90.000000 1.000000 \n",
"\n",
"[8 rows x 217 columns]\n"
]
}
],
"source": [
"#print(dataoriginal.info())\n",
"print(df.describe())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>encounter_id</th>\n",
" <td>2278392</td>\n",
" <td>149190</td>\n",
" <td>64410</td>\n",
" <td>500364</td>\n",
" <td>16680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>patient_nbr</th>\n",
" <td>8222157</td>\n",
" <td>55629189</td>\n",
" <td>86047875</td>\n",
" <td>82442376</td>\n",
" <td>42519267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>race</th>\n",
" <td>Caucasian</td>\n",
" <td>Caucasian</td>\n",
" <td>AfricanAmerican</td>\n",
" <td>Caucasian</td>\n",
" <td>Caucasian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gender</th>\n",
" <td>Female</td>\n",
" <td>Female</td>\n",
" <td>Female</td>\n",
" <td>Male</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>age</th>\n",
" <td>[0-10)</td>\n",
" <td>[10-20)</td>\n",
" <td>[20-30)</td>\n",
" <td>[30-40)</td>\n",
" <td>[40-50)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>weight</th>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>admission_type_id</th>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>discharge_disposition_id</th>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>admission_source_id</th>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time_in_hospital</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>payer_code</th>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>medical_specialty</th>\n",
" <td>Pediatrics-Endocrinology</td>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>num_lab_procedures</th>\n",
" <td>41</td>\n",
" <td>59</td>\n",
" <td>11</td>\n",
" <td>44</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>num_procedures</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>num_medications</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_outpatient</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_emergency</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_inpatient</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diag_1</th>\n",
" <td>250.83</td>\n",
" <td>276</td>\n",
" <td>648</td>\n",
" <td>8</td>\n",
" <td>197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diag_2</th>\n",
" <td>?</td>\n",
" <td>250.01</td>\n",
" <td>250</td>\n",
" <td>250.43</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diag_3</th>\n",
" <td>?</td>\n",
" <td>255</td>\n",
" <td>V27</td>\n",
" <td>403</td>\n",
" <td>250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_diagnoses</th>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max_glu_serum</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A1Cresult</th>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>metformin</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>repaglinide</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>nateglinide</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chlorpropamide</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glimepiride</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>acetohexamide</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glipizide</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Steady</td>\n",
" <td>No</td>\n",
" <td>Steady</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glyburide</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tolbutamide</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pioglitazone</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rosiglitazone</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>acarbose</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>miglitol</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>troglitazone</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tolazamide</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>examide</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>citoglipton</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>insulin</th>\n",
" <td>No</td>\n",
" <td>Up</td>\n",
" <td>No</td>\n",
" <td>Up</td>\n",
" <td>Steady</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glyburide-metformin</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glipizide-metformin</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glimepiride-pioglitazone</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>metformin-rosiglitazone</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>metformin-pioglitazone</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>change</th>\n",
" <td>No</td>\n",
" <td>Ch</td>\n",
" <td>No</td>\n",
" <td>Ch</td>\n",
" <td>Ch</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diabetesMed</th>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>readmitted</th>\n",
" <td>NO</td>\n",
" <td>>30</td>\n",
" <td>NO</td>\n",
" <td>NO</td>\n",
" <td>NO</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 \\\n",
"encounter_id 2278392 149190 \n",
"patient_nbr 8222157 55629189 \n",
"race Caucasian Caucasian \n",
"gender Female Female \n",
"age [0-10) [10-20) \n",
"weight ? ? \n",
"admission_type_id 6 1 \n",
"discharge_disposition_id 25 1 \n",
"admission_source_id 1 7 \n",
"time_in_hospital 1 3 \n",
"payer_code ? ? \n",
"medical_specialty Pediatrics-Endocrinology ? \n",
"num_lab_procedures 41 59 \n",
"num_procedures 0 0 \n",
"num_medications 1 18 \n",
"number_outpatient 0 0 \n",
"number_emergency 0 0 \n",
"number_inpatient 0 0 \n",
"diag_1 250.83 276 \n",
"diag_2 ? 250.01 \n",
"diag_3 ? 255 \n",
"number_diagnoses 1 9 \n",
"max_glu_serum None None \n",
"A1Cresult None None \n",
"metformin No No \n",
"repaglinide No No \n",
"nateglinide No No \n",
"chlorpropamide No No \n",
"glimepiride No No \n",
"acetohexamide No No \n",
"glipizide No No \n",
"glyburide No No \n",
"tolbutamide No No \n",
"pioglitazone No No \n",
"rosiglitazone No No \n",
"acarbose No No \n",
"miglitol No No \n",
"troglitazone No No \n",
"tolazamide No No \n",
"examide No No \n",
"citoglipton No No \n",
"insulin No Up \n",
"glyburide-metformin No No \n",
"glipizide-metformin No No \n",
"glimepiride-pioglitazone No No \n",
"metformin-rosiglitazone No No \n",
"metformin-pioglitazone No No \n",
"change No Ch \n",
"diabetesMed No Yes \n",
"readmitted NO >30 \n",
"\n",
" 2 3 4 \n",
"encounter_id 64410 500364 16680 \n",
"patient_nbr 86047875 82442376 42519267 \n",
"race AfricanAmerican Caucasian Caucasian \n",
"gender Female Male Male \n",
"age [20-30) [30-40) [40-50) \n",
"weight ? ? ? \n",
"admission_type_id 1 1 1 \n",
"discharge_disposition_id 1 1 1 \n",
"admission_source_id 7 7 7 \n",
"time_in_hospital 2 2 1 \n",
"payer_code ? ? ? \n",
"medical_specialty ? ? ? \n",
"num_lab_procedures 11 44 51 \n",
"num_procedures 5 1 0 \n",
"num_medications 13 16 8 \n",
"number_outpatient 2 0 0 \n",
"number_emergency 0 0 0 \n",
"number_inpatient 1 0 0 \n",
"diag_1 648 8 197 \n",
"diag_2 250 250.43 157 \n",
"diag_3 V27 403 250 \n",
"number_diagnoses 6 7 5 \n",
"max_glu_serum None None None \n",
"A1Cresult None None None \n",
"metformin No No No \n",
"repaglinide No No No \n",
"nateglinide No No No \n",
"chlorpropamide No No No \n",
"glimepiride No No No \n",
"acetohexamide No No No \n",
"glipizide Steady No Steady \n",
"glyburide No No No \n",
"tolbutamide No No No \n",
"pioglitazone No No No \n",
"rosiglitazone No No No \n",
"acarbose No No No \n",
"miglitol No No No \n",
"troglitazone No No No \n",
"tolazamide No No No \n",
"examide No No No \n",
"citoglipton No No No \n",
"insulin No Up Steady \n",
"glyburide-metformin No No No \n",
"glipizide-metformin No No No \n",
"glimepiride-pioglitazone No No No \n",
"metformin-rosiglitazone No No No \n",
"metformin-pioglitazone No No No \n",
"change No Ch Ch \n",
"diabetesMed Yes Yes Yes \n",
"readmitted NO NO NO "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head().T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is some missing data that are represented with ?. We will deal with this in the feature engineering section.\n",
"\n",
"The most important column here is readmitted, which tells us if a patient was hospitalized within 30 days, greater than 30 days or not readmitted."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"readmitted\n",
"<30 11357\n",
">30 35545\n",
"NO 54864\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('readmitted').size()\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Count')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.groupby('readmitted').size().plot(kind='bar')\n",
"plt.ylabel('Count') "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"discharge_disposition_id\n",
"1 60234\n",
"2 2128\n",
"3 13954\n",
"4 815\n",
"5 1184\n",
"6 12902\n",
"7 623\n",
"8 108\n",
"9 21\n",
"10 6\n",
"11 1642\n",
"12 3\n",
"13 399\n",
"14 372\n",
"15 63\n",
"16 11\n",
"17 14\n",
"18 3691\n",
"19 8\n",
"20 2\n",
"22 1993\n",
"23 412\n",
"24 48\n",
"25 989\n",
"27 5\n",
"28 139\n",
"dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"df.groupby('discharge_disposition_id').size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we look at the IDs_mapping.csv we can see that 11,13,14,19,20,21 are related to death or hospice.\n",
"\n",
"We should remove these samples from the predictive model."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"df = df.loc[~df.discharge_disposition_id.isin([11,13,14,19,20,21])]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"discharge_disposition_id\n",
"1 60234\n",
"2 2128\n",
"3 13954\n",
"4 815\n",
"5 1184\n",
"6 12902\n",
"7 623\n",
"8 108\n",
"9 21\n",
"10 6\n",
"12 3\n",
"15 63\n",
"16 11\n",
"17 14\n",
"18 3691\n",
"22 1993\n",
"23 412\n",
"24 48\n",
"25 989\n",
"27 5\n",
"28 139\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('discharge_disposition_id').size()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"50"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Preparation Summary\n",
"\n",
"\n",
"Recategorize 'age' feature\n",
"\n",
"Reduce levels in 'discharge_disposition_id', 'admission_source_id', and 'admission_type_id'\n",
"\n",
"One-hot-encode on categorical data\n",
"\n",
"Square root transform on right skewed count data\n",
"\n",
"Apply feature standardizing on numerical data"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"readmitted\n",
"<30 11314\n",
">30 35502\n",
"NO 52527\n",
"dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('readmitted').size()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of columns: 50\n"
]
}
],
"source": [
"print('Number of columns:',len(df.columns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's define an output variable for our binary classification. Here we will try to predict if a patient is likely to be re-admitted within 30 days of discharge"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"df['OUTPUT_LABEL'] = (df.readmitted == '<30').astype('int')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Let's define a function to calculate the prevalence of population that is readmitted with 30 days."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def calc_prevalence(y_actual):\n",
" return (sum(y_actual)/len(y_actual))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prevalence:0.114\n"
]
}
],
"source": [
"print('Prevalence:%.3f'%calc_prevalence(df['OUTPUT_LABEL'].values))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Around 11% of the population is rehospitalized. This represented an imbalanced classification problem so we will address that below.\n",
"\n",
"Now we would like to get a feeling of the data for each column in our dataset. Pandas doesn't allow you to see all the columns at once, so let's look at them in groups of 10."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of columns: 51\n"
]
}
],
"source": [
"print('Number of columns:',len(df.columns))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method DataFrame.info of encounter_id patient_nbr race gender age weight \\\n",
"0 2278392 8222157 Caucasian Female [0-10) ? \n",
"1 149190 55629189 Caucasian Female [10-20) ? \n",
"2 64410 86047875 AfricanAmerican Female [20-30) ? \n",
"3 500364 82442376 Caucasian Male [30-40) ? \n",
"4 16680 42519267 Caucasian Male [40-50) ? \n",
"5 35754 82637451 Caucasian Male [50-60) ? \n",
"6 55842 84259809 Caucasian Male [60-70) ? \n",
"7 63768 114882984 Caucasian Male [70-80) ? \n",
"8 12522 48330783 Caucasian Female [80-90) ? \n",
"9 15738 63555939 Caucasian Female [90-100) ? \n",
"10 28236 89869032 AfricanAmerican Female [40-50) ? \n",
"11 36900 77391171 AfricanAmerican Male [60-70) ? \n",
"12 40926 85504905 Caucasian Female [40-50) ? \n",
"13 42570 77586282 Caucasian Male [80-90) ? \n",
"14 62256 49726791 AfricanAmerican Female [60-70) ? \n",
"15 73578 86328819 AfricanAmerican Male [60-70) ? \n",
"16 77076 92519352 AfricanAmerican Male [50-60) ? \n",
"17 84222 108662661 Caucasian Female [50-60) ? \n",
"18 89682 107389323 AfricanAmerican Male [70-80) ? \n",
"19 148530 69422211 ? Male [70-80) ? \n",
"20 150006 22864131 ? Female [50-60) ? \n",
"21 150048 21239181 ? Male [60-70) ? \n",
"22 182796 63000108 AfricanAmerican Female [70-80) ? \n",
"23 183930 107400762 Caucasian Female [80-90) ? \n",
"24 216156 62718876 AfricanAmerican Female [70-80) ? \n",
"25 221634 21861756 Other Female [50-60) ? \n",
"26 236316 40523301 Caucasian Male [80-90) ? \n",
"27 248916 115196778 Caucasian Female [50-60) ? \n",
"28 250872 41606064 Caucasian Male [20-30) ? \n",
"29 252822 18196434 Caucasian Female [80-90) ? \n",
"... ... ... ... ... ... ... \n",
"101735 443739044 106595208 Caucasian Male [70-80) ? \n",
"101736 443739152 90751788 Caucasian Female [60-70) ? \n",
"101737 443775086 125764160 Caucasian Female [40-50) ? \n",
"101738 443775482 95780439 Caucasian Male [70-80) ? \n",
"101739 443775740 30656952 AfricanAmerican Male [70-80) ? \n",
"101740 443778398 134647673 Caucasian Male [40-50) ? \n",
"101741 443787128 58160520 AfricanAmerican Male [90-100) ? \n",
"101742 443787512 52419276 Caucasian Male [70-80) ? \n",
"101744 443793992 43686936 Caucasian Female [80-90) ? \n",
"101745 443797076 183766055 Caucasian Male [50-60) ? \n",
"101746 443797298 89955270 Caucasian Male [70-80) ? \n",
"101747 443804570 33230016 Caucasian Female [70-80) ? \n",
"101748 443811536 189481478 Caucasian Female [40-50) ? \n",
"101749 443816024 106392411 Caucasian Female [70-80) ? \n",
"101750 443824292 138784172 Caucasian Female [80-90) ? \n",
"101751 443835140 175326800 Caucasian Male [70-80) ? \n",
"101752 443835512 139605341 Other Female [40-50) ? \n",
"101753 443841992 184875899 Other Male [40-50) ? \n",
"101754 443842016 183087545 Caucasian Female [70-80) ? \n",
"101755 443842022 188574944 Other Female [40-50) ? \n",
"101756 443842070 140199494 Other Female [60-70) ? \n",
"101757 443842136 181593374 Caucasian Female [70-80) ? \n",
"101758 443842340 120975314 Caucasian Female [80-90) ? \n",
"101759 443842778 86472243 Caucasian Male [80-90) ? \n",
"101760 443847176 50375628 AfricanAmerican Female [60-70) ? \n",
"101761 443847548 100162476 AfricanAmerican Male [70-80) ? \n",
"101762 443847782 74694222 AfricanAmerican Female [80-90) ? \n",
"101763 443854148 41088789 Caucasian Male [70-80) ? \n",
"101764 443857166 31693671 Caucasian Female [80-90) ? \n",
"101765 443867222 175429310 Caucasian Male [70-80) ? \n",
"\n",
" admission_type_id discharge_disposition_id admission_source_id \\\n",
"0 6 25 1 \n",
"1 1 1 7 \n",
"2 1 1 7 \n",
"3 1 1 7 \n",
"4 1 1 7 \n",
"5 2 1 2 \n",
"6 3 1 2 \n",
"7 1 1 7 \n",
"8 2 1 4 \n",
"9 3 3 4 \n",
"10 1 1 7 \n",
"11 2 1 4 \n",
"12 1 3 7 \n",
"13 1 6 7 \n",
"14 3 1 2 \n",
"15 1 3 7 \n",
"16 1 1 7 \n",
"17 1 1 7 \n",
"18 1 1 7 \n",
"19 3 6 2 \n",
"20 2 1 4 \n",
"21 2 1 4 \n",
"22 2 1 4 \n",
"23 2 6 1 \n",
"24 3 1 2 \n",
"25 1 1 7 \n",
"26 1 3 7 \n",
"27 1 1 1 \n",
"28 2 1 2 \n",
"29 1 2 7 \n",
"... ... ... ... \n",
"101735 2 6 7 \n",
"101736 1 3 7 \n",
"101737 3 1 1 \n",
"101738 1 1 7 \n",
"101739 1 1 7 \n",
"101740 3 1 1 \n",
"101741 1 3 7 \n",
"101742 2 6 2 \n",
"101744 1 1 7 \n",
"101745 2 1 1 \n",
"101746 1 1 7 \n",
"101747 1 22 7 \n",
"101748 1 4 7 \n",
"101749 3 6 1 \n",
"101750 3 1 1 \n",
"101751 3 6 1 \n",
"101752 3 1 1 \n",
"101753 1 1 7 \n",
"101754 1 1 7 \n",
"101755 1 1 7 \n",
"101756 1 1 7 \n",
"101757 1 1 7 \n",
"101758 1 1 7 \n",
"101759 1 1 7 \n",
"101760 1 1 7 \n",
"101761 1 3 7 \n",
"101762 1 4 5 \n",
"101763 1 1 7 \n",
"101764 2 3 7 \n",
"101765 1 1 7 \n",
"\n",
" time_in_hospital ... insulin glyburide-metformin \\\n",
"0 1 ... No No \n",
"1 3 ... Up No \n",
"2 2 ... No No \n",
"3 2 ... Up No \n",
"4 1 ... Steady No \n",
"5 3 ... Steady No \n",
"6 4 ... Steady No \n",
"7 5 ... No No \n",
"8 13 ... Steady No \n",
"9 12 ... Steady No \n",
"10 9 ... Steady No \n",
"11 7 ... Steady No \n",
"12 7 ... Down No \n",
"13 10 ... Steady No \n",
"14 1 ... Steady No \n",
"15 12 ... Up No \n",
"16 4 ... Steady No \n",
"17 3 ... No No \n",
"18 5 ... Steady No \n",
"19 6 ... Steady No \n",
"20 2 ... Down No \n",
"21 2 ... Steady No \n",
"22 2 ... No No \n",
"23 11 ... No No \n",
"24 3 ... Steady No \n",
"25 1 ... No No \n",
"26 6 ... No No \n",
"27 2 ... Steady No \n",
"28 10 ... Down No \n",
"29 5 ... No No \n",
"... ... ... ... ... \n",
"101735 6 ... Up No \n",
"101736 8 ... Steady No \n",
"101737 4 ... Steady No \n",
"101738 1 ... No No \n",
"101739 1 ... Steady No \n",
"101740 1 ... Steady No \n",
"101741 4 ... No No \n",
"101742 4 ... Steady No \n",
"101744 1 ... No No \n",
"101745 3 ... No No \n",
"101746 4 ... No No \n",
"101747 8 ... Steady No \n",
"101748 14 ... Down No \n",
"101749 3 ... Steady No \n",
"101750 3 ... Down No \n",
"101751 13 ... Up No \n",
"101752 3 ... Steady No \n",
"101753 13 ... Down No \n",
"101754 9 ... Steady No \n",
"101755 14 ... Up No \n",
"101756 2 ... Steady No \n",
"101757 5 ... Steady No \n",
"101758 5 ... Up No \n",
"101759 1 ... Up No \n",
"101760 6 ... Down No \n",
"101761 3 ... Down No \n",
"101762 5 ... Steady No \n",
"101763 1 ... Down No \n",
"101764 10 ... Up No \n",
"101765 6 ... No No \n",
"\n",
" glipizide-metformin glimepiride-pioglitazone \\\n",
"0 No No \n",
"1 No No \n",
"2 No No \n",
"3 No No \n",
"4 No No \n",
"5 No No \n",
"6 No No \n",
"7 No No \n",
"8 No No \n",
"9 No No \n",
"10 No No \n",
"11 No No \n",
"12 No No \n",
"13 No No \n",
"14 No No \n",
"15 No No \n",
"16 No No \n",
"17 No No \n",
"18 No No \n",
"19 No No \n",
"20 No No \n",
"21 No No \n",
"22 No No \n",
"23 No No \n",
"24 No No \n",
"25 No No \n",
"26 No No \n",
"27 No No \n",
"28 No No \n",
"29 No No \n",
"... ... ... \n",
"101735 No No \n",
"101736 No No \n",
"101737 No No \n",
"101738 No No \n",
"101739 No No \n",
"101740 No No \n",
"101741 No No \n",
"101742 No No \n",
"101744 No No \n",
"101745 No No \n",
"101746 No No \n",
"101747 No No \n",
"101748 No No \n",
"101749 No No \n",
"101750 No No \n",
"101751 No No \n",
"101752 No No \n",
"101753 No No \n",
"101754 No No \n",
"101755 No No \n",
"101756 No No \n",
"101757 No No \n",
"101758 No No \n",
"101759 No No \n",
"101760 No No \n",
"101761 No No \n",
"101762 No No \n",
"101763 No No \n",
"101764 No No \n",
"101765 No No \n",
"\n",
" metformin-rosiglitazone metformin-pioglitazone change diabetesMed \\\n",
"0 No No No No \n",
"1 No No Ch Yes \n",
"2 No No No Yes \n",
"3 No No Ch Yes \n",
"4 No No Ch Yes \n",
"5 No No No Yes \n",
"6 No No Ch Yes \n",
"7 No No No Yes \n",
"8 No No Ch Yes \n",
"9 No No Ch Yes \n",
"10 No No No Yes \n",
"11 No No Ch Yes \n",
"12 No No Ch Yes \n",
"13 No No No Yes \n",
"14 No No No Yes \n",
"15 No No Ch Yes \n",
"16 No No Ch Yes \n",
"17 No No No Yes \n",
"18 No No No Yes \n",
"19 No No Ch Yes \n",
"20 No No Ch Yes \n",
"21 No No Ch Yes \n",
"22 No No No No \n",
"23 No No No No \n",
"24 No No Ch Yes \n",
"25 No No No Yes \n",
"26 No No Ch Yes \n",
"27 No No No Yes \n",
"28 No No Ch Yes \n",
"29 No No Ch Yes \n",
"... ... ... ... ... \n",
"101735 No No Ch Yes \n",
"101736 No No No Yes \n",
"101737 No No Ch Yes \n",
"101738 No No No Yes \n",
"101739 No No No Yes \n",
"101740 No No Ch Yes \n",
"101741 No No No No \n",
"101742 No No Ch Yes \n",
"101744 No No No No \n",
"101745 No No Ch Yes \n",
"101746 No No No Yes \n",
"101747 No No No Yes \n",
"101748 No No Ch Yes \n",
"101749 No No Ch Yes \n",
"101750 No No Ch Yes \n",
"101751 No No Ch Yes \n",
"101752 No No Ch Yes \n",
"101753 No No Ch Yes \n",
"101754 No No Ch Yes \n",
"101755 No No Ch Yes \n",
"101756 No No No Yes \n",
"101757 No No No Yes \n",
"101758 No No Ch Yes \n",
"101759 No No Ch Yes \n",
"101760 No No Ch Yes \n",
"101761 No No Ch Yes \n",
"101762 No No No Yes \n",
"101763 No No Ch Yes \n",
"101764 No No Ch Yes \n",
"101765 No No No No \n",
"\n",
" readmitted OUTPUT_LABEL \n",
"0 NO 0 \n",
"1 >30 0 \n",
"2 NO 0 \n",
"3 NO 0 \n",
"4 NO 0 \n",
"5 >30 0 \n",
"6 NO 0 \n",
"7 >30 0 \n",
"8 NO 0 \n",
"9 NO 0 \n",
"10 >30 0 \n",
"11 <30 1 \n",
"12 <30 1 \n",
"13 NO 0 \n",
"14 >30 0 \n",
"15 NO 0 \n",
"16 <30 1 \n",
"17 NO 0 \n",
"18 >30 0 \n",
"19 NO 0 \n",
"20 NO 0 \n",
"21 NO 0 \n",
"22 NO 0 \n",
"23 >30 0 \n",
"24 NO 0 \n",
"25 NO 0 \n",
"26 NO 0 \n",
"27 >30 0 \n",
"28 >30 0 \n",
"29 >30 0 \n",
"... ... ... \n",
"101735 NO 0 \n",
"101736 >30 0 \n",
"101737 NO 0 \n",
"101738 NO 0 \n",
"101739 NO 0 \n",
"101740 NO 0 \n",
"101741 NO 0 \n",
"101742 NO 0 \n",
"101744 NO 0 \n",
"101745 NO 0 \n",
"101746 <30 1 \n",
"101747 >30 0 \n",
"101748 >30 0 \n",
"101749 NO 0 \n",
"101750 <30 1 \n",
"101751 NO 0 \n",
"101752 NO 0 \n",
"101753 NO 0 \n",
"101754 >30 0 \n",
"101755 >30 0 \n",
"101756 >30 0 \n",
"101757 NO 0 \n",
"101758 NO 0 \n",
"101759 NO 0 \n",
"101760 >30 0 \n",
"101761 >30 0 \n",
"101762 NO 0 \n",
"101763 NO 0 \n",
"101764 NO 0 \n",
"101765 NO 0 \n",
"\n",
"[99343 rows x 51 columns]>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.info"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>encounter_id</th>\n",
" <th>patient_nbr</th>\n",
" <th>race</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>weight</th>\n",
" <th>admission_type_id</th>\n",
" <th>discharge_disposition_id</th>\n",
" <th>admission_source_id</th>\n",
" <th>time_in_hospital</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2278392</td>\n",
" <td>8222157</td>\n",
" <td>Caucasian</td>\n",
" <td>Female</td>\n",
" <td>[0-10)</td>\n",
" <td>?</td>\n",
" <td>6</td>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>149190</td>\n",
" <td>55629189</td>\n",
" <td>Caucasian</td>\n",
" <td>Female</td>\n",
" <td>[10-20)</td>\n",
" <td>?</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>64410</td>\n",
" <td>86047875</td>\n",
" <td>AfricanAmerican</td>\n",
" <td>Female</td>\n",
" <td>[20-30)</td>\n",
" <td>?</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>500364</td>\n",
" <td>82442376</td>\n",
" <td>Caucasian</td>\n",
" <td>Male</td>\n",
" <td>[30-40)</td>\n",
" <td>?</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>16680</td>\n",
" <td>42519267</td>\n",
" <td>Caucasian</td>\n",
" <td>Male</td>\n",
" <td>[40-50)</td>\n",
" <td>?</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" encounter_id patient_nbr race gender age weight \\\n",
"0 2278392 8222157 Caucasian Female [0-10) ? \n",
"1 149190 55629189 Caucasian Female [10-20) ? \n",
"2 64410 86047875 AfricanAmerican Female [20-30) ? \n",
"3 500364 82442376 Caucasian Male [30-40) ? \n",
"4 16680 42519267 Caucasian Male [40-50) ? \n",
"\n",
" admission_type_id discharge_disposition_id admission_source_id \\\n",
"0 6 25 1 \n",
"1 1 1 7 \n",
"2 1 1 7 \n",
"3 1 1 7 \n",
"4 1 1 7 \n",
"\n",
" time_in_hospital \n",
"0 1 \n",
"1 3 \n",
"2 2 \n",
"3 2 \n",
"4 1 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[list(df.columns)[:10]].head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>payer_code</th>\n",
" <th>medical_specialty</th>\n",
" <th>num_lab_procedures</th>\n",
" <th>num_procedures</th>\n",
" <th>num_medications</th>\n",
" <th>number_outpatient</th>\n",
" <th>number_emergency</th>\n",
" <th>number_inpatient</th>\n",
" <th>diag_1</th>\n",
" <th>diag_2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>?</td>\n",
" <td>Pediatrics-Endocrinology</td>\n",
" <td>41</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>250.83</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>59</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>276</td>\n",
" <td>250.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>648</td>\n",
" <td>250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>44</td>\n",
" <td>1</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>250.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>?</td>\n",
" <td>?</td>\n",
" <td>51</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>197</td>\n",
" <td>157</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" payer_code medical_specialty num_lab_procedures num_procedures \\\n",
"0 ? Pediatrics-Endocrinology 41 0 \n",
"1 ? ? 59 0 \n",
"2 ? ? 11 5 \n",
"3 ? ? 44 1 \n",
"4 ? ? 51 0 \n",
"\n",
" num_medications number_outpatient number_emergency number_inpatient \\\n",
"0 1 0 0 0 \n",
"1 18 0 0 0 \n",
"2 13 2 0 1 \n",
"3 16 0 0 0 \n",
"4 8 0 0 0 \n",
"\n",
" diag_1 diag_2 \n",
"0 250.83 ? \n",
"1 276 250.01 \n",
"2 648 250 \n",
"3 8 250.43 \n",
"4 197 157 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[list(df.columns)[10:20]].head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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>diag_3</th>\n",
" <th>number_diagnoses</th>\n",
" <th>max_glu_serum</th>\n",
" <th>A1Cresult</th>\n",
" <th>metformin</th>\n",
" <th>repaglinide</th>\n",
" <th>nateglinide</th>\n",
" <th>chlorpropamide</th>\n",
" <th>glimepiride</th>\n",
" <th>acetohexamide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>?</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>255</td>\n",
" <td>9</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>V27</td>\n",
" <td>6</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>403</td>\n",
" <td>7</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>250</td>\n",
" <td>5</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" diag_3 number_diagnoses max_glu_serum A1Cresult metformin repaglinide \\\n",
"0 ? 1 None None No No \n",
"1 255 9 None None No No \n",
"2 V27 6 None None No No \n",
"3 403 7 None None No No \n",
"4 250 5 None None No No \n",
"\n",
" nateglinide chlorpropamide glimepiride acetohexamide \n",
"0 No No No No \n",
"1 No No No No \n",
"2 No No No No \n",
"3 No No No No \n",
"4 No No No No "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[list(df.columns)[20:30]].head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>glipizide</th>\n",
" <th>glyburide</th>\n",
" <th>tolbutamide</th>\n",
" <th>pioglitazone</th>\n",
" <th>rosiglitazone</th>\n",
" <th>acarbose</th>\n",
" <th>miglitol</th>\n",
" <th>troglitazone</th>\n",
" <th>tolazamide</th>\n",
" <th>examide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Steady</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Steady</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" glipizide glyburide tolbutamide pioglitazone rosiglitazone acarbose \\\n",
"0 No No No No No No \n",
"1 No No No No No No \n",
"2 Steady No No No No No \n",
"3 No No No No No No \n",
"4 Steady No No No No No \n",
"\n",
" miglitol troglitazone tolazamide examide \n",
"0 No No No No \n",
"1 No No No No \n",
"2 No No No No \n",
"3 No No No No \n",
"4 No No No No "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[list(df.columns)[30:40]].head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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>citoglipton</th>\n",
" <th>insulin</th>\n",
" <th>glyburide-metformin</th>\n",
" <th>glipizide-metformin</th>\n",
" <th>glimepiride-pioglitazone</th>\n",
" <th>metformin-rosiglitazone</th>\n",
" <th>metformin-pioglitazone</th>\n",
" <th>change</th>\n",
" <th>diabetesMed</th>\n",
" <th>readmitted</th>\n",
" <th>OUTPUT_LABEL</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>NO</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>No</td>\n",
" <td>Up</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Ch</td>\n",
" <td>Yes</td>\n",
" <td>>30</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>NO</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>No</td>\n",
" <td>Up</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Ch</td>\n",
" <td>Yes</td>\n",
" <td>NO</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>No</td>\n",
" <td>Steady</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Ch</td>\n",
" <td>Yes</td>\n",
" <td>NO</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" citoglipton insulin glyburide-metformin glipizide-metformin \\\n",
"0 No No No No \n",
"1 No Up No No \n",
"2 No No No No \n",
"3 No Up No No \n",
"4 No Steady No No \n",
"\n",
" glimepiride-pioglitazone metformin-rosiglitazone metformin-pioglitazone \\\n",
"0 No No No \n",
"1 No No No \n",
"2 No No No \n",
"3 No No No \n",
"4 No No No \n",
"\n",
" change diabetesMed readmitted OUTPUT_LABEL \n",
"0 No No NO 0 \n",
"1 Ch Yes >30 0 \n",
"2 No Yes NO 0 \n",
"3 Ch Yes NO 0 \n",
"4 Ch Yes NO 0 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[list(df.columns)[40:]].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From this, we see that there are a lot of categorical (non-numeric) variables. Note that the variables with _id are also categorical and you can see what the ids refer to with the IDs_mapping.csv. Let's take a look at the unique values for each column."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"encounter_id: 99343 unique values\n",
"patient_nbr: 69990 unique values\n",
"race\n",
"['Caucasian' 'AfricanAmerican' '?' 'Other' 'Asian' 'Hispanic']\n",
"gender\n",
"['Female' 'Male' 'Unknown/Invalid']\n",
"age\n",
"['[0-10)' '[10-20)' '[20-30)' '[30-40)' '[40-50)' '[50-60)' '[60-70)'\n",
" '[70-80)' '[80-90)' '[90-100)']\n",
"weight\n",
"['?' '[75-100)' '[50-75)' '[0-25)' '[100-125)' '[25-50)' '[125-150)'\n",
" '[175-200)' '[150-175)' '>200']\n",
"admission_type_id\n",
"[6 1 2 3 4 5 8 7]\n",
"discharge_disposition_id\n",
"[25 1 3 6 2 5 7 10 4 18 8 12 16 17 22 23 9 15 24 28 27]\n",
"admission_source_id\n",
"[ 1 7 2 4 5 20 6 3 17 8 9 14 10 22 11 25 13]\n",
"time_in_hospital\n",
"[ 1 3 2 4 5 13 12 9 7 10 6 11 8 14]\n",
"payer_code\n",
"['?' 'MC' 'MD' 'HM' 'UN' 'BC' 'SP' 'CP' 'SI' 'DM' 'CM' 'CH' 'PO' 'WC' 'OT'\n",
" 'OG' 'MP' 'FR']\n",
"medical_specialty: 73 unique values\n",
"num_lab_procedures: 118 unique values\n",
"num_procedures\n",
"[0 5 1 6 2 3 4]\n",
"num_medications: 75 unique values\n",
"number_outpatient\n",
"[ 0 2 1 5 7 9 3 8 4 12 11 6 20 15 10 13 14 16 21 35 17 29 36 18\n",
" 19 27 22 24 42 39 34 26 33 25 23 28 37 38 40]\n",
"number_emergency\n",
"[ 0 1 2 4 3 9 5 7 6 8 22 25 10 13 42 16 11 28 15 14 18 12 21 20\n",
" 19 46 76 37 64 63 54 24 29]\n",
"number_inpatient\n",
"[ 0 1 2 3 6 5 4 7 9 8 15 10 11 14 12 13 17 16 21 18 19]\n",
"diag_1: 716 unique values\n",
"diag_2: 748 unique values\n",
"diag_3: 787 unique values\n",
"number_diagnoses\n",
"[ 1 9 6 7 5 8 3 4 2 16 12 13 15 10 11 14]\n",
"max_glu_serum\n",
"['None' '>300' 'Norm' '>200']\n",
"A1Cresult\n",
"['None' '>7' '>8' 'Norm']\n",
"metformin\n",
"['No' 'Steady' 'Up' 'Down']\n",
"repaglinide\n",
"['No' 'Up' 'Steady' 'Down']\n",
"nateglinide\n",
"['No' 'Steady' 'Down' 'Up']\n",
"chlorpropamide\n",
"['No' 'Steady' 'Down' 'Up']\n",
"glimepiride\n",
"['No' 'Steady' 'Down' 'Up']\n",
"acetohexamide\n",
"['No' 'Steady']\n",
"glipizide\n",
"['No' 'Steady' 'Up' 'Down']\n",
"glyburide\n",
"['No' 'Steady' 'Up' 'Down']\n",
"tolbutamide\n",
"['No' 'Steady']\n",
"pioglitazone\n",
"['No' 'Steady' 'Up' 'Down']\n",
"rosiglitazone\n",
"['No' 'Steady' 'Up' 'Down']\n",
"acarbose\n",
"['No' 'Steady' 'Up' 'Down']\n",
"miglitol\n",
"['No' 'Steady' 'Down' 'Up']\n",
"troglitazone\n",
"['No' 'Steady']\n",
"tolazamide\n",
"['No' 'Steady' 'Up']\n",
"examide\n",
"['No']\n",
"citoglipton\n",
"['No']\n",
"insulin\n",
"['No' 'Up' 'Steady' 'Down']\n",
"glyburide-metformin\n",
"['No' 'Steady' 'Down' 'Up']\n",
"glipizide-metformin\n",
"['No' 'Steady']\n",
"glimepiride-pioglitazone\n",
"['No' 'Steady']\n",
"metformin-rosiglitazone\n",
"['No' 'Steady']\n",
"metformin-pioglitazone\n",
"['No' 'Steady']\n",
"change\n",
"['No' 'Ch']\n",
"diabetesMed\n",
"['No' 'Yes']\n",
"readmitted\n",
"['NO' '>30' '<30']\n",
"OUTPUT_LABEL\n",
"[0 1]\n"
]
}
],
"source": [
"# for each column\n",
"for c in list(df.columns):\n",
" \n",
" # get a list of unique values\n",
" n = df[c].unique()\n",
" \n",
" # if number of unique values is less than 30, print the values. Otherwise print the number of unique values\n",
" if len(n)<50:\n",
" print(c)\n",
" print(n)\n",
" else:\n",
" print(c + ': ' +str(len(n)) + ' unique values')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From analysis of the columns, we can see there are a mix of categorical (non-numeric) and numerical data. A few things to point out,\n",
"\n",
"- encounter_id and patient_nbr: these are just identifiers and not useful variables\n",
"- age and weight: are categorical in this data set\n",
"- admission_type_id,discharge_disposition_id,admission_source_id: are numerical here, but are IDs (see IDs_mapping). They should be considered categorical. \n",
"- examide and citoglipton only have 1 value, so we will not use these variables\n",
"- diag1, diag2, diag3 - are categorical and have a lot of values. We will not use these as part of this project, but you could group these ICD codes to reduce the dimension. We will use number_diagnoses to capture some of this information. \n",
"- medical_speciality - has many categorical variables, so we should consider this when making features. "
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"encounter_id 0\n",
"patient_nbr 0\n",
"race 0\n",
"gender 0\n",
"age 0\n",
"weight 0\n",
"admission_type_id 0\n",
"discharge_disposition_id 0\n",
"admission_source_id 0\n",
"time_in_hospital 0\n",
"payer_code 0\n",
"medical_specialty 0\n",
"num_lab_procedures 0\n",
"num_procedures 0\n",
"num_medications 0\n",
"number_outpatient 0\n",
"number_emergency 0\n",
"number_inpatient 0\n",
"diag_1 0\n",
"diag_2 0\n",
"diag_3 0\n",
"number_diagnoses 0\n",
"max_glu_serum 0\n",
"A1Cresult 0\n",
"metformin 0\n",
"repaglinide 0\n",
"nateglinide 0\n",
"chlorpropamide 0\n",
"glimepiride 0\n",
"acetohexamide 0\n",
"glipizide 0\n",
"glyburide 0\n",
"tolbutamide 0\n",
"pioglitazone 0\n",
"rosiglitazone 0\n",
"acarbose 0\n",
"miglitol 0\n",
"troglitazone 0\n",
"tolazamide 0\n",
"examide 0\n",
"citoglipton 0\n",
"insulin 0\n",
"glyburide-metformin 0\n",
"glipizide-metformin 0\n",
"glimepiride-pioglitazone 0\n",
"metformin-rosiglitazone 0\n",
"metformin-pioglitazone 0\n",
"change 0\n",
"diabetesMed 0\n",
"readmitted 0\n",
"OUTPUT_LABEL 0\n",
"dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df.isnull().sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Engineering\n",
"In this section, we will create features for our predictive model. For each section, we will add new variables to the dataframe and then keep track of which columns of the dataframe we want to use as part of the predictive model features. We will break down this section into numerical features, categorical features and extra features.\n",
"\n",
"In this data set, the missing numbers were filled with a question mark. Let's replace it with a nan representation."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# replace ? with nan\n",
"df = df.replace('?',np.nan)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numerical Features¶\n",
"The easiest type of features to use is numerical features. These features do not need any modification. The columns that are numerical that we will use are shown below"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"cols_num = ['time_in_hospital','num_lab_procedures', 'num_procedures', 'num_medications',\n",
" 'number_outpatient', 'number_emergency', 'number_inpatient','number_diagnoses']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check if there are any missing values in the numerical data."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"time_in_hospital 0\n",
"num_lab_procedures 0\n",
"num_procedures 0\n",
"num_medications 0\n",
"number_outpatient 0\n",
"number_emergency 0\n",
"number_inpatient 0\n",
"number_diagnoses 0\n",
"dtype: int64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df[cols_num].isnull().sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Categorical Features\n",
"The next type of features we want to create are categorical variables. Categorical variables are non-numeric data such as race and gender. To turn these non-numerical data into variables, the simplest thing is to use a technique called one-hot encoding, which will be explained below.\n",
"\n",
"The first set of categorical data we will deal with are these columns:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"cols_cat = ['race', 'gender', \n",
" 'max_glu_serum', 'A1Cresult',\n",
" 'metformin', 'repaglinide', 'nateglinide', 'chlorpropamide',\n",
" 'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide',\n",
" 'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone',\n",
" 'tolazamide', 'insulin',\n",
" 'glyburide-metformin', 'glipizide-metformin',\n",
" 'glimepiride-pioglitazone', 'metformin-rosiglitazone',\n",
" 'metformin-pioglitazone', 'change', 'diabetesMed','payer_code','medical_specialty']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check if there are any missing data"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"race 2234\n",
"gender 0\n",
"max_glu_serum 0\n",
"A1Cresult 0\n",
"metformin 0\n",
"repaglinide 0\n",
"nateglinide 0\n",
"chlorpropamide 0\n",
"glimepiride 0\n",
"acetohexamide 0\n",
"glipizide 0\n",
"glyburide 0\n",
"tolbutamide 0\n",
"pioglitazone 0\n",
"rosiglitazone 0\n",
"acarbose 0\n",
"miglitol 0\n",
"troglitazone 0\n",
"tolazamide 0\n",
"insulin 0\n",
"glyburide-metformin 0\n",
"glipizide-metformin 0\n",
"glimepiride-pioglitazone 0\n",
"metformin-rosiglitazone 0\n",
"metformin-pioglitazone 0\n",
"change 0\n",
"diabetesMed 0\n",
"payer_code 39398\n",
"medical_specialty 48616\n",
"dtype: int64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df[cols_cat].isnull().sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"race, payer_code, and medical_specialty have missing data. Since these are categorical data, the best thing to do is to just add another categorical type for unknown using the fillna function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Created separate category for features with Nan as 'UNK'"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"\n",
"df['race'] = df['race'].fillna('UNK')\n",
"df['payer_code'] = df['payer_code'].fillna('UNK')\n",
"df['medical_specialty'] = df['medical_specialty'].fillna('UNK')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's investigate medical specialty before we begin."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number medical specialty: 73\n"
]
},
{
"data": {
"text/plain": [
"medical_specialty\n",
"UNK 48616\n",
"InternalMedicine 14237\n",
"Emergency/Trauma 7419\n",
"Family/GeneralPractice 7252\n",
"Cardiology 5279\n",
"Surgery-General 3059\n",
"Nephrology 1539\n",
"Orthopedics 1392\n",
"Orthopedics-Reconstructive 1230\n",
"Radiologist 1121\n",
"Pulmonology 854\n",
"Psychiatry 853\n",
"Urology 682\n",
"ObstetricsandGynecology 669\n",
"Surgery-Cardiovascular/Thoracic 642\n",
"Gastroenterology 538\n",
"Surgery-Vascular 525\n",
"Surgery-Neuro 462\n",
"PhysicalMedicineandRehabilitation 391\n",
"Oncology 319\n",
"Pediatrics 253\n",
"Neurology 201\n",
"Hematology/Oncology 187\n",
"Pediatrics-Endocrinology 159\n",
"Otolaryngology 125\n",
"Endocrinology 119\n",
"Surgery-Thoracic 108\n",
"Psychology 101\n",
"Podiatry 100\n",
"Surgery-Cardiovascular 98\n",
" ... \n",
"Anesthesiology-Pediatric 19\n",
"Obstetrics 19\n",
"Rheumatology 17\n",
"Pathology 16\n",
"OutreachServices 12\n",
"Anesthesiology 12\n",
"Surgery-Colon&Rectal 11\n",
"Pediatrics-Neurology 10\n",
"PhysicianNotFound 10\n",
"Surgery-Maxillofacial 9\n",
"Endocrinology-Metabolism 8\n",
"Surgery-Pediatric 8\n",
"Cardiology-Pediatric 7\n",
"AllergyandImmunology 7\n",
"Psychiatry-Child/Adolescent 7\n",
"DCPTEAM 5\n",
"Pediatrics-Hematology-Oncology 4\n",
"Dentistry 4\n",
"Pediatrics-AllergyandImmunology 3\n",
"Pediatrics-EmergencyMedicine 3\n",
"Resident 2\n",
"Neurophysiology 1\n",
"Pediatrics-InfectiousDiseases 1\n",
"Perinatology 1\n",
"Proctology 1\n",
"Psychiatry-Addictive 1\n",
"Dermatology 1\n",
"Speech 1\n",
"SportsMedicine 1\n",
"Surgery-PlasticwithinHeadandNeck 1\n",
"Length: 73, dtype: int64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Number medical specialty:', df.medical_specialty.nunique())\n",
"df.groupby('medical_specialty').size().sort_values(ascending = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that most of them are unknown and that the count drops off pretty quickly. We don't want to add 73 new variables since some of them only have a few samples. As an alternative, we can create a new variable that only has 11 options (the top 10 specialities and then an other category). Obviously, there are other options for bucketing, but this is one of the easiest methods."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"top_10=['UNK','InternalMedicine','Emergency/Trauma',\\\n",
" 'Family/GeneralPractice ','Cardiology','Surgery-General' ,\\\n",
" 'Nephrology','Orthopedics',\\\n",
" 'Orthopedics-Reconstructive','Radiologist']\n",
"# make a new column with duplicated data\n",
"df['med_spec'] = df['medical_specialty'].copy()\n",
"\n",
"# replace all specialties not in top 10 with 'Other' category\n",
"df.loc[~df.med_spec.isin(top_10),'med_spec'] = 'Other'"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"med_spec\n",
"Cardiology 5279\n",
"Emergency/Trauma 7419\n",
"InternalMedicine 14237\n",
"Nephrology 1539\n",
"Orthopedics 1392\n",
"Orthopedics-Reconstructive 1230\n",
"Other 15451\n",
"Radiologist 1121\n",
"Surgery-General 3059\n",
"UNK 48616\n",
"dtype: int64"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('med_spec').size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To convert our categorical features to numbers, we will use a technique called one-hot encoding. In one-hot encoding, you create a new column for each unique value in that column. Then the value of the column is 1 if the sample has that unique value or 0 otherwise. For example, for the column race, we would create new columns ('race_Caucasian','race_AfricanAmerican', etc). If the patient's race is Caucasian, the patient gets a 1 under 'race_Caucasian' and 0 under the rest of the race columns. To create these one-hot encoding columns, we can use the get_dummies function.\n",
"\n",
"Now the problem is that if we create a column for each unique value, we have correlated columns. In other words, the value in one column can be figured out by looking at the rest of the columns. For example, if the sample is not AfricanAmerican, Asian, Causasian, Hispance or Other, it must be UNK. To deal with this, we can use the drop_first option, which will drop the first categorical value for each column.\n",
"\n",
"The get_dummies function does not work on numerical data. To trick get_dummies, we can convert the numerical data into strings and then it will work properly."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"cols_cat_num = ['admission_type_id', 'discharge_disposition_id', 'admission_source_id']\n",
"\n",
"df[cols_cat_num] = df[cols_cat_num].astype('str')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"df_cat = pd.get_dummies(df[cols_cat + cols_cat_num + ['med_spec']],drop_first = True)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"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>race_Asian</th>\n",
" <th>race_Caucasian</th>\n",
" <th>race_Hispanic</th>\n",
" <th>race_Other</th>\n",
" <th>race_UNK</th>\n",
" <th>gender_Male</th>\n",
" <th>gender_Unknown/Invalid</th>\n",
" <th>max_glu_serum_>300</th>\n",
" <th>max_glu_serum_None</th>\n",
" <th>max_glu_serum_Norm</th>\n",
" <th>...</th>\n",
" <th>admission_source_id_9</th>\n",
" <th>med_spec_Emergency/Trauma</th>\n",
" <th>med_spec_InternalMedicine</th>\n",
" <th>med_spec_Nephrology</th>\n",
" <th>med_spec_Orthopedics</th>\n",
" <th>med_spec_Orthopedics-Reconstructive</th>\n",
" <th>med_spec_Other</th>\n",
" <th>med_spec_Radiologist</th>\n",
" <th>med_spec_Surgery-General</th>\n",
" <th>med_spec_UNK</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 204 columns</p>\n",
"</div>"
],
"text/plain": [
" race_Asian race_Caucasian race_Hispanic race_Other race_UNK \\\n",
"0 0 1 0 0 0 \n",
"1 0 1 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 1 0 0 0 \n",
"4 0 1 0 0 0 \n",
"\n",
" gender_Male gender_Unknown/Invalid max_glu_serum_>300 \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 1 0 0 \n",
"4 1 0 0 \n",
"\n",
" max_glu_serum_None max_glu_serum_Norm ... admission_source_id_9 \\\n",
"0 1 0 ... 0 \n",
"1 1 0 ... 0 \n",
"2 1 0 ... 0 \n",
"3 1 0 ... 0 \n",
"4 1 0 ... 0 \n",
"\n",
" med_spec_Emergency/Trauma med_spec_InternalMedicine med_spec_Nephrology \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" med_spec_Orthopedics med_spec_Orthopedics-Reconstructive med_spec_Other \\\n",
"0 0 0 1 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" med_spec_Radiologist med_spec_Surgery-General med_spec_UNK \n",
"0 0 0 0 \n",
"1 0 0 1 \n",
"2 0 0 1 \n",
"3 0 0 1 \n",
"4 0 0 1 \n",
"\n",
"[5 rows x 204 columns]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cat.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To add the one-hot encoding columns to the dataframe we can use concat function. Make sure to use axis = 1 to indicate add the columns."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"df = pd.concat([df,df_cat], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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>encounter_id</th>\n",
" <th>patient_nbr</th>\n",
" <th>race</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>weight</th>\n",
" <th>admission_type_id</th>\n",
" <th>discharge_disposition_id</th>\n",
" <th>admission_source_id</th>\n",
" <th>time_in_hospital</th>\n",
" <th>...</th>\n",
" <th>admission_source_id_9</th>\n",
" <th>med_spec_Emergency/Trauma</th>\n",
" <th>med_spec_InternalMedicine</th>\n",
" <th>med_spec_Nephrology</th>\n",
" <th>med_spec_Orthopedics</th>\n",
" <th>med_spec_Orthopedics-Reconstructive</th>\n",
" <th>med_spec_Other</th>\n",
" <th>med_spec_Radiologist</th>\n",
" <th>med_spec_Surgery-General</th>\n",
" <th>med_spec_UNK</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2278392</td>\n",
" <td>8222157</td>\n",
" <td>Caucasian</td>\n",
" <td>Female</td>\n",
" <td>[0-10)</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>149190</td>\n",
" <td>55629189</td>\n",
" <td>Caucasian</td>\n",
" <td>Female</td>\n",
" <td>[10-20)</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>64410</td>\n",
" <td>86047875</td>\n",
" <td>AfricanAmerican</td>\n",
" <td>Female</td>\n",
" <td>[20-30)</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>500364</td>\n",
" <td>82442376</td>\n",
" <td>Caucasian</td>\n",
" <td>Male</td>\n",
" <td>[30-40)</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>16680</td>\n",
" <td>42519267</td>\n",
" <td>Caucasian</td>\n",
" <td>Male</td>\n",
" <td>[40-50)</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 256 columns</p>\n",
"</div>"
],
"text/plain": [
" encounter_id patient_nbr race gender age weight \\\n",
"0 2278392 8222157 Caucasian Female [0-10) NaN \n",
"1 149190 55629189 Caucasian Female [10-20) NaN \n",
"2 64410 86047875 AfricanAmerican Female [20-30) NaN \n",
"3 500364 82442376 Caucasian Male [30-40) NaN \n",
"4 16680 42519267 Caucasian Male [40-50) NaN \n",
"\n",
" admission_type_id discharge_disposition_id admission_source_id \\\n",
"0 6 25 1 \n",
"1 1 1 7 \n",
"2 1 1 7 \n",
"3 1 1 7 \n",
"4 1 1 7 \n",
"\n",
" time_in_hospital ... admission_source_id_9 med_spec_Emergency/Trauma \\\n",
"0 1 ... 0 0 \n",
"1 3 ... 0 0 \n",
"2 2 ... 0 0 \n",
"3 2 ... 0 0 \n",
"4 1 ... 0 0 \n",
"\n",
" med_spec_InternalMedicine med_spec_Nephrology med_spec_Orthopedics \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" med_spec_Orthopedics-Reconstructive med_spec_Other med_spec_Radiologist \\\n",
"0 0 1 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" med_spec_Surgery-General med_spec_UNK \n",
"0 0 0 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 1 \n",
"4 0 1 \n",
"\n",
"[5 rows x 256 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Save the column names of the categorical data."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"\n",
"cols_all_cat = list(df_cat.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extra features\n",
"The last two columns we want to make features are age and weight. Typically, you would think of these as numerical data, but they are categorical in this dataset as shown below."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[0-10)</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[10-20)</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[20-30)</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[30-40)</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[40-50)</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age weight\n",
"0 [0-10) NaN\n",
"1 [10-20) NaN\n",
"2 [20-30) NaN\n",
"3 [30-40) NaN\n",
"4 [40-50) NaN"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"df[['age', 'weight']].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One option could be to create categorical data as shown above. Since there is a natural order to these values, it might make more sense to convert these to numerical data. Another example when you would want to do this might be size of a t-shirt (small, medium, large). Let's start with age."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"age\n",
"[0-10) 160\n",
"[10-20) 690\n",
"[20-30) 1649\n",
"[30-40) 3764\n",
"[40-50) 9607\n",
"[50-60) 17060\n",
"[60-70) 22059\n",
"[70-80) 25331\n",
"[80-90) 16434\n",
"[90-100) 2589\n",
"dtype: int64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('age').size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's map these to 0-9 for the numerical data"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"age_id={'[0-10)':0,\n",
" '[10-20)':1,\n",
" '[20-30)':2,\n",
" '[30-40)':3,\n",
" '[40-50)':4,\n",
" '[50-60)':50,\n",
" '[60-70)':60, \n",
" '[70-80)':70, \n",
" '[80-90)':80, \n",
" '[90-100)':90\n",
" }\n",
"\n",
" \n",
" \n",
" \n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"df['age_group']=df.age.replace(age_id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's look at weight. Recall that this feature is not filled out very often."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3125"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.weight.notnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead of creating an ordinal feature that we did above, let's just create a variable to say if weight was filled out or not. The presence of a variable might be predictive regardless of the value."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"\n",
"df['has_weight'] = df.weight.notnull().astype('int')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"\n",
"cols_extra = ['age_group','has_weight']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Engineering Features Summary"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of features: 214\n",
"Numerical Features: 8\n",
"Categorical Features: 204\n",
"Extra features: 2\n"
]
}
],
"source": [
"print('Total number of features:', len(cols_num + cols_all_cat + cols_extra))\n",
"print('Numerical Features:',len(cols_num))\n",
"print('Categorical Features:',len(cols_all_cat))\n",
"print('Extra features:',len(cols_extra))"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"has_weight 0\n",
"payer_code_MP 0\n",
"payer_code_MC 0\n",
"payer_code_HM 0\n",
"payer_code_FR 0\n",
"payer_code_DM 0\n",
"payer_code_CP 0\n",
"payer_code_CM 0\n",
"payer_code_CH 0\n",
"diabetesMed_Yes 0\n",
"dtype: int64"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[cols_num + cols_all_cat + cols_extra].isnull().sum().sort_values(ascending = False).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"col2use = cols_num + cols_all_cat + cols_extra\n",
"df_data = df[col2use + ['OUTPUT_LABEL']]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Building Training/Validation/Test Samples\n",
"So far we have explored our data and created features from the categorical data. It is now time for us to split our data. The idea behind splitting the data is so that you can measure how well your model would do on unseen data. We split into three parts:\n",
"\n",
"- Training samples: these samples are used to train the model\n",
"- Validation samples: these samples are held out from the training data and are used to make decisions on how to improve the model\n",
"- Test samples: these samples are held out from all decisions and are used to measure the generalized performance of the model\n",
"\n",
"In this project, we will split into 70% train, 15% validation, 15% test.\n",
"\n",
"The first thing I like to do is to shuffle the samples using sample in case there was some order (e.g. all positive samples on top). Here n is the number. random_state is just specified so the entire class gets the same shuffling. You wouldn't need random_state in your own projects."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"# shuffle the samples\n",
"df_data = df_data.sample(n = len(df_data), random_state = 42)\n",
"df_data = df_data.reset_index(drop = True)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use sample again to extract 30% (using frac) of the data to be used for validation / test splits. It is important that validation and test come from similar distributions and this technique is one way to do it."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Split size: 0.300\n"
]
}
],
"source": [
"# Save 30% of the data as validation and test data \n",
"df_valid_test=df_data.sample(frac=0.30,random_state=42)\n",
"print('Split size: %.3f'%(len(df_valid_test)/len(df_data)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now split into test and validation using 50% fraction"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Split size: 0.500\n"
]
}
],
"source": [
"# Save 30% of the data as validation5and test data \n",
"df_test=df_valid_test.sample(frac=0.50,random_state=42)\n",
"print('Split size: %.3f'%(len(df_test)/len(df_valid_test)))\n",
"df_valid = df_valid_test.drop(df_test.index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that .drop just drops the rows from df_test to get the rows that were not part of the sample. We can use this same idea to get the training data."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"#training data set\n",
"df_train_all = df_data.drop(df_valid_test.index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, let's check what percent of our groups are hospitalized within 30 days. This is known as prevalence. Ideally, all three groups would have similar prevalance."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21358745"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_data.size"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test prevalence(n = 14902):0.117\n",
"Valid prevalence(n = 14901):0.113\n",
"Train all prevalence(n = 69540):0.113\n"
]
}
],
"source": [
"\n",
"print('Test prevalence(n = %d):%.3f'%(len(df_test),calc_prevalence(df_test.OUTPUT_LABEL.values)))\n",
"print('Valid prevalence(n = %d):%.3f'%(len(df_valid),calc_prevalence(df_valid.OUTPUT_LABEL.values)))\n",
"print('Train all prevalence(n = %d):%.3f'%(len(df_train_all), calc_prevalence(df_train_all.OUTPUT_LABEL.values)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"The prevalence is about the same for each group.\n",
"\n",
"Let's verify that we used all the data."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"all samples (n = 99343)\n"
]
}
],
"source": [
"print('all samples (n = %d)'%len(df_data))\n",
"assert len(df_data) == (len(df_test)+len(df_valid)+len(df_train_all)),'math didnt work'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, you might say, drop the training data into a predictive model and see the outcome. However, if we do this, it is possible that we will get back a model that is 89% accurate. Great! Good job! But wait, we never catch any of the readmissions (recall= 0%). How can this happen?\n",
"\n",
"What is happening is that we have an imbalanced dataset where there are much more negatives than positives, so the model might just assigns all samples as negative.\n",
"\n",
"Typically, it is better to balance the data in some way to give the positives more weight. There are 3 strategies that are typically utilized:\n",
"\n",
"- sub-sample the more dominant class: use a random subset of the negatives\n",
"- over-sample the imbalanced class: use the same positive samples multiple times\n",
"- create synthetic positive data\n",
"\n",
"Usually, you will want to use the latter two methods if you only have a handful of positive cases. Since we have a few thousand positive cases, let's use the sub-sample approach. Here, we will create a balanced training data set that has 50% positive and 50% negative. You can also play with this ratio to see if you can get an improvement."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train balanced prevalence(n = 15766):0.500\n"
]
}
],
"source": [
"\n",
"# split the training data into positive and negative\n",
"rows_pos = df_train_all.OUTPUT_LABEL == 1\n",
"df_train_pos = df_train_all.loc[rows_pos]\n",
"df_train_neg = df_train_all.loc[~rows_pos]\n",
"\n",
"# merge the balanced data\n",
"df_train = pd.concat([df_train_pos, df_train_neg.sample(n = len(df_train_pos), random_state = 42)],axis = 0)\n",
"\n",
"# shuffle the order of training samples \n",
"df_train = df_train.sample(n = len(df_train), random_state = 42).reset_index(drop = True)\n",
"\n",
"print('Train balanced prevalence(n = %d):%.3f'%(len(df_train), calc_prevalence(df_train.OUTPUT_LABEL.values)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we have done a lot of work, let's save our data."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"df_train_all.to_csv('df_train_all.csv',index=False)\n",
"df_train.to_csv('df_train.csv',index=False)\n",
"df_valid.to_csv('df_valid.csv',index=False)\n",
"df_test.to_csv('df_test.csv',index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Most machine learning packages like to use an input matrix X and output vector y, so let's create those"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training All shapes: (69540, 214)\n",
"Training shapes: (15766, 214) (15766,)\n",
"Validation shapes: (14901, 214) (14901,)\n"
]
}
],
"source": [
"X_train = df_train[col2use].values\n",
"X_train_all = df_train_all[col2use].values\n",
"X_valid = df_valid[col2use].values\n",
"\n",
"y_train = df_train['OUTPUT_LABEL'].values\n",
"y_valid = df_valid['OUTPUT_LABEL'].values\n",
"\n",
"print('Training All shapes:',X_train_all.shape)\n",
"print('Training shapes:',X_train.shape, y_train.shape)\n",
"print('Validation shapes:',X_valid.shape, y_valid.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some machine learning models have trouble when the variables are of different size (0-100, vs 0-1000000). To deal with that we can scale the data. Here we will use scikit learn's Standard Scaler which removes the mean and scales to unit variance. Here I will create a scaler using all the training data, but you could use the balanced one if you wanted."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OUTPUT_LABEL\n",
"0 88029\n",
"1 11314\n",
"dtype: int64"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('OUTPUT_LABEL').size()\n"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
},
{
"data": {
"text/plain": [
"StandardScaler(copy=True, with_mean=True, with_std=True)"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"scaler = StandardScaler()\n",
"scaler.fit(X_train_all)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will need this scaler for the test data, so let's save it using a package called pickle."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"scalerfile = 'scaler.sav'\n",
"pickle.dump(scaler, open(scalerfile, 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# load it back\n",
"scaler = pickle.load(open(scalerfile, 'rb'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can transform our data matrices"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n",
"/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
}
],
"source": [
"X_train_tf = scaler.transform(X_train)\n",
"X_valid_tf = scaler.transform(X_valid)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score\n",
"def calc_specificity(y_actual, y_pred, thresh):\n",
" # calculates specificity\n",
" return sum((y_pred < thresh) & (y_actual == 0)) /sum(y_actual ==0)\n",
"\n",
"def print_report(y_actual, y_pred, thresh):\n",
" \n",
" auc = roc_auc_score(y_actual, y_pred)\n",
" #F1_score=precision_score(y_actual, y_pred)\n",
" accuracy = accuracy_score(y_actual, (y_pred > thresh))\n",
" recall = recall_score(y_actual, (y_pred > thresh))\n",
" precision = precision_score(y_actual, (y_pred > thresh))\n",
" specificity = calc_specificity(y_actual, y_pred, thresh)\n",
" print('AUC:%.3f'%auc)\n",
" print('accuracy:%.3f'%accuracy)\n",
" print('recall:%.3f'%recall)\n",
" print('precision:%.3f'%precision)\n",
" print('specificity:%.3f'%specificity)\n",
" print('prevalence:%.3f'%calc_prevalence(y_actual))\n",
" print(' ')\n",
" return auc, accuracy, recall, precision, specificity"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"thresh = 0.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performing Exploratory Data Analysis (EDA)\n",
"\n",
"### Check for Correlation if any"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x720 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Let's see the relationship between different variables to understand the data and if there is a strong correlation between \n",
"#two variables then we can consider one of them.\n",
"import seaborn as sns\n",
"from pandas.plotting import scatter_matrix\n",
"sm = scatter_matrix(df[['num_procedures', 'num_medications', 'number_emergency']], figsize = (10, 10))\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"From the above, we can see that there is no problem of multi-collinearity. We can also see that as the number_emergency increases the num_medication decreases."
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Let's try to see how the age and number of medicines vary,\n",
"sortage = df.sort_values(by = 'age')\n",
"x = sns.stripplot(x = \"age\", y = \"num_medications\", data = sortage, color = 'red')\n",
"sns.despine() #remove top and right axes\n",
"x.figure.set_size_inches(10, 6)\n",
"x.set_xlabel('Age')\n",
"x.set_ylabel('Number of Medications')\n",
"x.axes.set_title('Number of Medications vs. Age')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x468 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Gender and Readmissions,\n",
"plot1 = sns.countplot(x = 'gender', hue = 'OUTPUT_LABEL' ,data = df) \n",
"sns.despine()\n",
"plot1.figure.set_size_inches(7, 6.5)\n",
"plot1.legend(title = 'Readmitted patients', labels = ('No', 'Yes'))\n",
"plot1.axes.set_title('Readmissions Balance by Gender')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x468 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"b = df.age.unique()\n",
"b.sort()\n",
"b_sort = np.array(b).tolist()\n",
"\n",
"\n",
"ageplt = sns.countplot(x = 'age', hue = 'OUTPUT_LABEL', data = df, order = b_sort) \n",
"\n",
"sns.despine()\n",
"ageplt.figure.set_size_inches(7, 6.5)\n",
"ageplt.legend(title = 'Readmitted within 30 days', labels = ('No', 'Yes'))\n",
"ageplt.axes.set_title('Readmissions Balance by Age')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c297369e8>"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x720 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Exploring the categorical variables,\n",
"\n",
"import seaborn as sns\n",
"\n",
"fig, ax = plt.subplots(figsize=(15,10), ncols=2, nrows=2)\n",
"\n",
"sns.countplot(x=\"readmitted\", data=df, ax=ax[0][0])\n",
"sns.countplot(x=\"race\", data=df, ax=ax[0][1])\n",
"sns.countplot(x=\"gender\", data=df, ax=ax[1][0])\n",
"sns.countplot(x=\"age\", data=df, ax=ax[1][1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Selection: baseline models¶\n",
"In this section, we will compare the performance of different machine learning models using default hyperparameters.\n",
"\n",
"## K nearest neighbors (KNN)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=1, n_neighbors=100, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# k-nearest neighbors\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"knn=KNeighborsClassifier(n_neighbors = 100)\n",
"knn.fit(X_train_tf, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"Y_knn=knn.predict(X_valid_tf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_train_preds = knn.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_knn = knn.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('KNN')\n",
"print('Training:')\n",
"knn_train_auc, knn_train_accuracy, knn_train_recall, \\\n",
" knn_train_precision, knn_train_specificity = print_report(y_train,y_train_preds, thresh)\n",
"print('Validation:')\n",
"knn_valid_auc, knn_valid_accuracy, knn_valid_recall, \\\n",
" knn_valid_precision, knn_valid_specificity = print_report(y_valid,y_valid_preds_knn, thresh)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix,roc_curve, auc,roc_auc_score\n",
"roc_auc = roc_auc_score(y_valid, y_valid_preds_knn)\n",
"fp_rate, tp_rate, thresholds = roc_curve(y_valid, y_valid_preds_knn)\n",
"plt.figure()\n",
"plt.plot(fp_rate, tp_rate, label='KNN (area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1],'r--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC - KNN')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.grid(True)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[9177 4039]\n",
" [ 907 778]]\n"
]
}
],
"source": [
"#Printing the confusion matrix,\n",
"\n",
"print(confusion_matrix(y_valid, Y_knn))"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
"y_valid1=(y_valid>thresh)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAH+CAYAAADNpjfmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xe4HFX9x/H3N7kJSSgpQAKEDiE0MXSQKiU0aQISEAhFggVR+YlioygooCiCIkSkiAoIUkIRCKEpgvReQw8lAdJJIOTe8/tj5obNzS27YzZ3k32/nmee3D17ZubM5u7uuZ9zZiZSSkiSJFWiS2c3QJIkLXzsQEiSpIrZgZAkSRWzAyFJkipmB0KSJFXMDoQkSaqYHQhJklQxOxCSJKlidiAkSVLFGjq7AZIkLbRmTKn+5Zx79Y6q76MAEwhJklQxEwhJkoqq4/tJmUBIkqSKmUBIklSYCYQkSVLZTCAkSSrKORCSJGlhFBHfioinI+KZiPh2XtYvIkZHxEv5v33z8oiIcyNibEQ8GREblWxneF7/pYgY3tF+7UBIklRUStVf2hER6wNHA5sBnwW+EBGDgBOBMSmlQcCY/DHAbsCgfBkB/CHfTj/gZGDzfFsnN3c62mIHQpKkhdc6wAMppRkppdnAPcC+wN7AZXmdy4B98p/3Bv6cMg8AfSJieWAXYHRKaWJKaRIwGti1vR3bgZAkqbBU9SUiRkTEwyXLiJIGPA1sGxFLR0QvYHdgJWBASukdgPzf/nn9gcCbJeuPy8vaKm+TkyglSaphKaWRwMg2nnsuIs4kSwymA08As9vZXGuXxU7tlLfJBEKSpKI6eQ5E1oT0p5TSRimlbYGJwEvA+HxogvzfCXn1cWQJRbMVgbfbKW+THQhJkhZiEdE//3dl4IvAFcAooPlMiuHADfnPo4DD8rMxtgCm5EMctwFDI6JvPnlyaF7WJocwJEkqqjauA/GPiFga+AT4RkppUkScAfw9Io4C3gAOyOveQjZPYiwwAzgCIKU0MSJ+BjyU1/tpSmliezuNVBsHL0nSwmfKhOp/ifbuX5O38zaBkCSpsPr9I9w5EJIkqWImEJIkFVW/AYQJhCRJqpwJhCRJRdXxiQgmEJIkqWImEJIkFVa/CYQdCEmSinIIQ5IkqXwmEJIkFWUCoc6S39Dk1YhIEbFmZ7en1kTEMhHxu4h4JSI+ioi3I+K2iNins9tWVERsFRH/jYiZ+f/9cRWsOyIins5fi/ERcVUrdVaJiCsiYmJEzIiIJyJi1xZ1do6I+yJiSr6d6yJicCvbOjoiXsr390hE7Nji+Uvz393WloNK6t3dRp0eLba3dUTcX/J/fXpEzPOHzgI+xu3baPsZLeqVe4wdtisiLoiI5yNiekRMioh7I2Kngu06MCKujYh38ucPb/kaSEWYQHS+LYFV85+HAad1XlNqS0R0A+4CegGnAy+T3WJ2KLAjcH3nta6YvJN4G3AT8ANgM+DXETEjpXRRB+ueBhxL9jvyEDAA2K5FnZWA+4EnyG6S8yEwBOhZUmdj4Gay1+9UYEngJGB0RKyfUpqa1xsGXACcAvw7395NEbFpSunpfHM/y+uU+hpwMHBHi/K7gB+2KPu4pF2rAaPz12dfYE3gF8DiwLc78RibfRl4peTxW8yro2Msq135sfwOeAHoDhwF/DMitkkpPVBhu/Yn+4y5CfhKK23W/6R+EwhvptXJIuI8sg+tp4ElU0rrdXKT5oiIHimljzpx/zsDtwObpZQeavFcpCr/8kZEz5TSzPm8zQuBzwPrppRm52XnA3sCK7d1TBGxHvAksGtKaXQ7278SGAhsl1JqaqPOGcDhwIolbdiA7At595TSP/OyF4D7UkpH5o+75HWeSCkd0k4bngHeSCntVlJ2N/B+Smn/dta7ENgZWKukXccBZ5O9Nu90xjFGxPZkHYPPtNKpKN1nOcdYVrtaWa8r8CpwfUrpuArb1SWl1BQRSwDTgCNSSpe2VV8V+mBc9b9El16xJm+m5RBGJ8o/FA4guz/7xcC6+YdJy3rNce37eVz7ZEQcXPJ8z4g4KyJej4iPI4vFf1HyfIqIY1ts85SIeL/k8eF5vc3yKHYmcEL+3BkR8VQep46LiL9GxHKttPPovF5zvH5NRPSOiD0ioin/C7O0/mp5+V5tvER98n/fbflEyy/aiNggIm6MiMl5Ox/MOyCl+7o+IqZGxLS87pottpEi4viIOCci3gOeKnlu74h4OD+2d/PXu1sb7W7PbsC1zV8euSvJkpX121lvOPByB52H3sAXgfPb+mLNdQNmtGjD5ObN5NtaHVgL+HtzhXybV+fH0FYbNgDWBa5oZ/9tGQLc3aJdt5MlpUPz7Xf6Mf6POmxXa1JKjXm97pXusIPXSf+rlKq/1Cg7EJ1rB7IY+krgGrJ7uR9UWiEi+pPFtZsC3yX7S/VPwEr58wHcQBYb/57sPu8nA8sUbNMVZFHn7vm/AP2BnwN7kEXJqwN35h2g5nb+GLgQuAfYJ2/PFGAJ4FbgbbIvwVKHA++R3Z++NY8DTcDFkY2NtzrkFhFrA/cBywNfJYu/r+PT12gxYAywDnB0vt/VgHsiol+LzZ2Qb+dQoPkvvS8B1wIPAnuRRc8jyOL15jasGh2ML0fE4nmbnm/x1HP5v2u3tS6wOfBUc8cv7yjeERHrlNTZiOwLKkU2xv5J3uH7Qf570uwvwAoR8f2I6BvZkMCv83aNadGW1traLyKWbaOdw4CPaH14aWjeAZ4R2TyWlp3lHsCsFmXN8X/zcXbmMd4ZEY0R8VpE/Lj097+CYyynXcCc+VENEbF0RHwHGET2h0ZL5bRLmv9SSi6dtJB9GEwCuuePbyaLKaOkzi/IxniXb2Mbu5ANwu3Vzn4ScGyLslPI4tbmx4fn9b7VQZu7ksXHCdg2L+sDzAB+3c56p5UeG9lfW68Bv+pgf8eTfakkYCZZZ+SAFnWuAMYBPdvYxleB2cDqJWUr5tv9QYvX6bEW6wbwOnBJi/Ij8/YsnT9eJd/HYe0cS/Prtk+L8oa8fEQ7675AFj8/S9ZB2pMs9n4d6JHXOSjfzhTgDLKhkp8CjcDXW2xvp/x3L+XLc2TDBM3Pfzkv79PKeolsmKG1dr4M/KOV8lPJhuq2AQ7J9zcFWLWkzj+AR1qsd2C+v5GddYzAhmTvw93z587J9/fbSo+xnHaV1BtWUmc6Ld7j5barpP4S+bYOb+8951LZ0jTh9VTtpbOPsa2l0xtQrwuwWP4hcnFJ2SH5G3zLkrL/tvaBXPL8mcAHHeyrkg7Emq2svxvwn/zDMJUsXyl5PpGNxbbVhtXJ0oTP5493yNdZr4zXaiDwdbKoufmD9xclz48Hzm5n/YuBB1spvwu4ucXrdFqLOoPz8t3Ivuibl1Xz8u0q+D9v7kDs3aK8uQNxdDvrvpS/fuu0eE1nA0flj5u/EK9s5fjfLHm8HjCBLMnakSxV+Q/ZPJylWmyrd4tt7ZyXD2qljZvnz+1fxmuxHFkkf05JWfMX90/IErQtyDqds4E/1MIxtnjffQIsU+Exdtiukrp9gU2AXYHLyTrp23fwurbZLuxAVGWp5w6EQxidZzeyv9xviYg+EdEHuJsssi0dxlgaeKed7XT0fKXGlz6IiE3J5miMI4v1tyT7YIcscm5uA+21I6X0CtnxHZEXHUH2pf5MRw1KKb2VUjo/pfQlsuTgVuCEiGjeb0evwfItjys3Hmg5hNGyXvNQ0C1kH8zNy6t5+Uodtb9E81h3nxblfVs835qJwPiUUvNwR/Nr+hrZnIPmOpB1jErdCawYEUvlj38GvJRSOiqlNCalNIpseGo1Pp2lP6mNtjY/bq2tw8hSkpvbOY7mtr9LNuy0UUnZHcCPgR+RDW3dS/ZFO5FP/186+xibXUPW8ZtnzlJ7x1hmu5rXn5RSejildGtK6VCyocyfttOmstql+S0tgKU22YHoPM2dhKvJPsgmAW+SJRNfKhnH/IDsC7AtHT0PWaek5eSrll+czVr+tu5L9mF+YEppVMpOIWs5qfGD/N+O2nERsF9EDCSbCHdJB/XnbVxKHwLnkw2lNE+C7Og1eIdsHkdLA/j0C2nOLlo8bn5+BNk8lJZLq7Pm22n7m8w716GtsfhSz7VRHmTJREd1KKm3Ntn8ktK2TSIbDlmjRVtaa+vElNJ7c+0gO3vhALKzBCo5c2Wu1zuldDpZp20Dsv+fC4BlgeZTFzvtGMtpfxl1ymlXWx4jS53KUbvfOlpk2IHoBJGdTvUFsrH7z7dYjif74Px8Xn0MsEtEDGhjc2PIJnx9oZ1djuPTSWjNH/Y7lNncnsAnKaXSD6Qvt6hzP9l8gOEdbOtasnkHV5L97l3ZXuWI6NfGxMlB+b8T8n/HkHW6erRSF7JhoI1LzwLJOzGfIzv3vz0vkJ1Xv2r+12DL5YMO1m/pn8C+LSa6HUjWsWjzVDyyCa3LRURz2kBErEE29+IJgJTSa8AzZNF4qR3JzuCYnj9+nWz8fI48zVmVLNFoTjdeJOsUNNdp7iS01mnalmyIpqyzL/Lf562AR1o+l1KanlJ6Kv9i/Ube3jtq4BhL7Uc2tPJkhcfYYbva2FaQpX+vtlWn3HZpPkupbs/C6PQxlHpc+HTsdfNWnusGvA/8KX+8LFkH4EWyL+gdyD5Uv5c/H2SR/lSyszR2yLd/Yck2f0k2fvp1svHUa4A3aH0OxBIt2rN7Xn4O2Yf0T8i+VBMl8yrILorUBJyb72NvYCQwsMX2fpev+7cyXqf9ycb+f0g2Lt28/w+BG0vqDc6P/0GyL+OdyM6mODJ/fjGyC+08D3yJ7EP2KbKOQb+S7cx1TCXlB5J1fM7j0wlrI8iGNXrldVahg0mUeb01ySbE/Y2sk/g9siGRr7SoNxs4qeRxV7Ivoufz12Xf/BieBbqV1Ns3/3/4Zf6anU42se7LJXX2zo/1UrJJuF8k62RNBlYoqXdQvu6P87ZeStZRXL+V47qA7Pe2oZXnNiAb1jg8387w/DgmMvekxjXJLqq0K1kH+4L8dd+5xfYW6DECfyAbOtgz39Zv83XOLnCMHbaLbBLm9cBhwPZkv6+j8n3uXkm78nrrkv3ONM+x+l3+eLvO/ixcFJam8a+mai+dfYxtLZ3egHpcyP6afLGd588nG9JYLH+8CnBVXjaD7C/OYSX1ewK/IutofEz2V8rpJc8vAVyWf5i9m39YnkIZHYj8ue+R/YX8IdlfgoNofWLmMWRfaB/n+/k7804Ma54ot1MZr9NK+XE9nn/ATiP70vwB+Rd3Sd0NyL7Qp+XLf4EdS55fPf9Qnkb2BX4TLSbJtXZMJc/tBvwrfw2m5m06jfwLk08nVR5exnFtTdbZ+Yjsr87jWqmTgFNalA0g+wt/Sn4c19H67P3mMwBmAWOBr7ZS50tkV7OcSpbk3AIMaaXe0fk2PgYeLX1NS+o0kA1zXdDG8Q7Mt/9O3qYPyM64WLtFvZXJ5j1MyV/nu4Ft2tjmAjtGstN5n8xf84/JEpBvA10qPcZy2pX/Ll3Dp+/ncfnv65aVtiuvdwqtD6zfXfQzzOXTpendV1K1l84+xrYWr0SpBSoiziL7i3615AVuJC3k0vhXq/4lGgNWq8krUXovDC0Qkd0saF2yC0ydaudB0qKhfv8ItwOhBeVCsusEjCKbJyFJWojZgdACkVLavrPbIEnzXR1PA/A0TkmSVDETCEmSCqvfBKKWOxD1+78iSZofavLshUVFLXcgYMaUzm6BVJt69QZgyueHdHJDpNrU+67HO640P9TxHIja7kBIklTL6rgD4SRKSZJUMRMISZIKM4GQJEkqmwmEJElF1W8AYQIhSZIqZwIhSVJRnoUhSZJUPhMISZIKM4GQJEkqmwmEJElFOQdCkiSpfCYQkiQVZQIhSZJUPhMISZIKM4GQJEkqmwmEJElFOQdCkiSpfCYQkiQVlZo6uwWdxgRCkiRVzARCkqSi6ngOhB0ISZKKanIIQ5IkqWwmEJIkFVXHQxgmEJIkqWImEJIkFeVpnJIkSeUzgZAkqSjnQEiSJJXPBEKSpKKcAyFJklQ+EwhJkgpKC2AORFR9D8WYQEiSpIqZQEiSVJT3wpAkSSqfCYQkSUV5FoYkSVL5TCAkSSrKK1FKkiSVzwRCkqSinAMhSZJUPhMISZKKquM5EHYgJEkqyiEMSZKk8plASJJUVFP9DmGYQEiSpIqZQEiSVJRzICRJkspnB0KSpKJSqv7SjogYHBGPlyxTI+LbEXFKRLxVUr57yTo/iIixEfFCROxSUr5rXjY2Ik7s6NAdwpAkaSGVUnoBGAIQEV2Bt4DrgCOA36SUflVaPyLWBYYB6wErAHdExFr5078HdgbGAQ9FxKiU0rNt7dsOhCRJRdXWHIgdgZdTSq9HRFt19gauTCl9DLwaEWOBzfLnxqaUXgGIiCvzum12IBzCkCRp0TAMuKLk8bER8WREXBwRffOygcCbJXXG5WVtlbfJDoQkSUUtgDkQETEiIh4uWUa0bEZEdAf2Aq7Oi/4ArEE2vPEOcHZz1daOop3yNjmEIUlSDUspjQRGdlBtN+DRlNL4fJ3xzU9ExB+Bm/KH44CVStZbEXg7/7mt8laZQEiSVFRqqv5SnoMoGb6IiOVLntsXeDr/eRQwLCIWi4jVgEHAg8BDwKCIWC1PM4blddtkAiFJ0kIsInqRnT1xTEnxWRExhGwY4rXm51JKz0TE38kmR84GvpFSasy3cyxwG9AVuDil9Ex7+7UDIUlSUTVwL4yU0gxg6RZlh7ZT/3Tg9FbKbwFuKXe/DmFIkqSKmUBIklRUbV0HYoEygZAkSRUzgZAkqSgTCEmSpPKZQEiSVFDq4G6ZizI7EJIkFeUQhiRJUvlMICRJKqqOhzBMICRJUsVMICRJKso5EJIkSeUzgZAkqagauJlWZzGBkCRJFTOBkCSpKOdASJIklc8EQpKkorwOhCRJUvlMICRJKso5EJIkSeUzgZAkqSjnQEiSJJXPBEKSpKKanAMhSZJUNhMISZKKcg6EJElS+UwgJEkqyutASJIklc8EQpKkoup4DoQdCEmSivI0TkmSpPKZQEiSVFQdD2GYQEiSpIqZQEiSVJSncUqSJJXPBEKSpKKanAMhSZJUNhOIRdBlf7uSq6+9npQSB3xxHw7/8kH8c/Qd/O6CP/Lyq69x9eWX8Jn11gVg1C238qfLLp+z7gsvjeW6Ky5npRUH8uUjR8wpf3fCBPbafTd+dMLx8+zvwj9dyjU3jKJLly78+Hv/xzaf2xKAe++7n9N/eTZNTU0csM/ejDhyOABvvvUWx5/4Y6ZMmcq66wzmrNNOpXu3btV8SaS5denCEhf8jab3JzDjh8cRy61Ar5POJJbsTeNLzzHz5z+C2bPpvuf+dN/nQGhqIs2cwcyzf0bT669AQwM9j/8JXQevC6mJmef9ksYnHp5nN7HkUvQ86Sy6LLcCTe++zYxTT4Dp0wDo8c3v0bD51vDRR8w48ySaXnoegG677MlihxwNwMd/+SOf3HbjgntdVDnnQGhR8eLYl7n62uu5+vJLueGqv3L3vf/mtdffYK011uC8s89i0402nKv+Xrvvyg1X/ZUbrvorZ512KgNXWJ51Bq/FEosvPqf8hqv+ysDll2foDtvPs7+xL7/Czbfdzs3XXMlFv/8tp/7iLBobG2lsbOSnZ5zFRb/7LTf/4ypuuvU2xr78CgC/+u3vOPzLB3H7qH+w1JJLcs11NyyAV0b6VPf9DqbxjVfnPO5xzLeZdfVfmH7oXqRpU+m++74AzBrzT6YfdQDTjz6Qj6+8lB5f/79s/S/sB8D0ow7gw+9+lZ5fPx4i5tnPYgcfSeOj/2X6oXvR+Oh/6XHwkQA0bL41XQauzPRD9mLm2T+j53d+BGQdjh6HHcOHXz+E6V/7Mj0OOwaWWLKqr4VUVNU6EBGxdkR8PyLOjYjf5j+vU639KfPyq6/y2c+sT8+ePWhoaGDTjTdi9F13s8bqq7H6qqu0u+7Nt97OF3YdOk/5a6+/wQcTJ7JJi84HwJi772WPXYbSvXt3Vho4kFVWWpEnn36GJ59+hlVWWpGVVhxI927d2GOXoYy5+15SSjzw0MPsstMOAOy75x6Mufue+XPwUhlimf5022IbZt187Zyyhg035ZN77gDgk9tupGHrz2dPzPjw0/V69Jxzzn+XVVZn9qP/BSBNnkSaPo2ug9ebZ18Nn9ueWXmCMOu2G2nYKttuw1bb88ntNwHQ+NxTxOJLEv2WoWHTz/HJIw+Qpk2F6dP45JEH6LbZVvP5FdB8lVL1lxpVlQ5ERHwfuBII4EHgofznKyLixGrsU5m11liDhx99jEmTJzNz5kfc++/7ePfd8WWte8vto9lj113mKb/p1tvZfejORCt/YY1/7z2WW27AnMcD+vdn/IT3GD/hPZYbUFI+oD/j33uPSZOnsNSSS9LQkI2eLTdgAOMnvFfpYUqF9Tz2BGZeeM6cyW+xVB/S9GnQ1AhA03vj6bJM/zn1u+9zIEv85UZ6HPNtPjrvLAAaX36Rblt9Hrp0JZZbga5rrUv0HzDPvrr0W5o08X0A0sT36dK3X1a+TH+aJrw7p156P9tnLNOfVFr+3niipC1SLanWHIijgPVSSp+UFkbEr4FngDNaWykiRgAjAC688EJGHHJglZq36Fpj9dX4yuGHceTXvkmvnj0ZvNYgujZ07XC9J556mp49erDWmmvM89wtt43mrNNOaXW91ErvOCJoamVcMLIVWq0vLQgNW2xD0+RJNL34HF0/u0lW2NrvX8nv6azrr2LW9VfRbcfdWOzQo5l5xk/45Jbr6bryaixx4d9oGv82s59+Ahoby29IK/tMpA7bohpUx3MgqtWBaAJWAF5vUb58/lyrUkojgZHND5kxpTqtW8QdsO/eHLDv3gD8+rzzGTCg479gbr7tdvZoZfji+RdepLFxNuuv2/ro03L9+8+VcIyfMIH+yy4DwLvjS8rHT6D/ssvSt28fpk6bxuzZs2loaODd8ePn1Jeqrev6Q+j2ue3otvnW0L070Wtxehx7ArHEktClKzQ10mXZATR9MG8q9smdt9Lz2z9kJkBTIx+d/6s5zy1+3mU0jXtjnnWaJn5A9FuGNPF9ot8yNE2amJW/N54u/ZejucsRywwgvf8e6b3xdBmyyZz1Y9kBND0+7+RMqRZUaw7Et4ExEfHPiBiZL7cCY4BvVWmfyn0wMfuQevudd7n9zrtanddQqqmpiVtH38keu8xb76Zbb291WKPZDttvw8233c6sWbN48623eO2NN9lg/fX4zHrr8tobb/LmW28x65NPuPm229lh+22ICDbfZGNuu+NOAK678WZ22H67/+FopfJ9fNF5TPvSLkw7aHdm/PREZj/2EDNP/yGNjz1Mt+12ArKzIGbfdzcAXQauPGfdhi22ofGtvJOwWA/o0SMr33gLaJydnZ3Rwuz/3EP3XfYEoPsuezL7P3fPKe829AsAdF3nM6QPp5Mmvs/sh/5Dt022zCZOLrEk3TbZktkP/acKr4Tmm6ZU/aVGVSWBSCndGhFrAZsBA8nS63HAQymlCnI+FfHN736fyZOn0tDQlZNPPIHeSy3F6Dvv4mdnns3ESZM45rjjWWfwIP50/nkAPPToYyw3oD8rrThwnm39c/QdjDzvnLnKxtx9L08/+xzf+voxDFpjDXYbuhO773cgXbt25aQTv0fXrtmQyUnfP4GvfP04Gpua2G/vPRm0RjY8csK3vsl3TvwR55x/AesMXosD9tmryq+I1L6ZI8+h10/OZLGjvkHTSy/w0S3XAdB932E0bLw5zJ5NmjaVmWecBED06cfiZ50PqYn0/gRm/OLHc7bV87snMWvUNTS++CwfX3ExvU4+i26770ua8A4zTjkBgNkP/IuGzbdmib/cCB9/xMwzTwYgTZvKR5ePZIkL/grAR38emU2olGpQtDaGXSMcwpDa0qs3AFM+P6STGyLVpt53PQ751Ktqarz611X/Eu16wPE1OVHM60BIkqSKeSVKSZKKqt0Uv+pMICRJUsVMICRJKqqGz5KoNjsQkiQVVccXknIIQ5IkVcwEQpKkopxEKUmSVD4TCEmSinIOhCRJUvlMICRJKqqOT+M0gZAkSRUzgZAkqSjnQEiSJJXPBEKSpKK8DoQkSVL5TCAkSSqqyTkQkiRJZTOBkCSpKOdASJIklc8EQpKkorwOhCRJUvlMICRJKso5EJIkSeUzgZAkqag6vg6EHQhJkopyCEOSJKl8JhCSJBXlaZySJEnlM4GQJKmoJudASJIklc0EQpKkopwDIUmSVD47EJIkFZVS9ZcORESfiLgmIp6PiOciYsuI6BcRoyPipfzfvnndiIhzI2JsRDwZERuVbGd4Xv+liBje0X7tQEiStHD7LXBrSmlt4LPAc8CJwJiU0iBgTP4YYDdgUL6MAP4AEBH9gJOBzYHNgJObOx1tsQMhSVJRnZxARMRSwLbAn7LmpFkppcnA3sBlebXLgH3yn/cG/pwyDwB9ImJ5YBdgdEppYkppEjAa2LW9fduBkCSphkXEiIh4uGQZUfL06sB7wCUR8VhEXBQRiwMDUkrvAOT/9s/rDwTeLFl/XF7WVnmbPAtDkqSiFsDNtFJKI4GRbTzdAGwEfDOl9N+I+C2fDle0JlrbRTvlbTKBkCRp4TUOGJdS+m/++BqyDsX4fGiC/N8JJfVXKll/ReDtdsrbZAdCkqSiOnkORErpXeDNiBicF+0IPAuMAprPpBgO3JD/PAo4LD8bYwtgSj7EcRswNCL65pMnh+ZlbXIIQ5Kkhds3gb9GRHfgFeAIsoDg7xFxFPAGcEBe9xZgd2AsMCOvS0ppYkT8DHgor/fTlNLE9nZqB0KSpKLKuE5D9ZuQHgc2aeWpHVupm4BvtLGdi4GLy92vQxiSJKliJhCSJBVVAwlEZzGBkCRJFTOBkCSpqAVwHYhaZQdCkqSiHMKQJEkqnwmEJElFmUBIkiSVzwRCkqSi6ngSpQmEJEmqmAmEJElFOQdCkiSpfCYQkiQVZQIhSZJUPhMISZKKMoGQJEkqnwmEJElFeR0ISZKk8plASJJUlHMgJEmSymcCIUlSUSYQkiRJ5TOBkCSpKBMISZI/x89vAAAaH0lEQVSk8plASJJUUPI6EJIkSeVrM4GIiH7trZhSmjj/myNJ0kKkjudAtDeE8QiQgGjluQSsXpUWSZK0sLADMa+U0moLsiGSJGnh0eEciMgcEhE/yR+vHBGbVb9pkiTVuJSqv9SociZRng9sCRycP54G/L5qLZIkSTWvnNM4N08pbRQRjwGklCZFRPcqt0uSpNrnaZzt+iQiupJNnCQilgXq9xWTJEllJRDnAtcBAyLidGB/4MdVbZUkSQuDGp6jUG0ddiBSSn+NiEeAHfOifVJKz1W3WZIkqZaVeynrXkDzMEbP6jVHkqSFSB0nEOWcxnkScBnQD1gGuCQiHMKQJKmOlZNAHARsmFL6CCAizgAeBU6rZsMkSap5JhDteg3oUfJ4MeDlqrRGkiQtFNq7mdZ5ZHMePgaeiYjR+eOdgX8vmOZJklTD6vg6EO0NYTyc//sI2Wmcze6uWmskSdJCob2baV22IBsiSdJCp47nQHQ4iTIiBgG/ANalZC5ESsnbeUuSVKfKOQvjEuBk4DfA54EjgKhmoyRJWijUcQJRzlkYPVNKY4BIKb2eUjoF2KG6zZIkSbWsnATio4joArwUEccCbwH9q9ssSZIWAiYQ7fo22aWsjwM2Bg4FhlezUZIkqbaVczOth/Ifp5PNf5AkSeB1IFoTETeSXTiqVSmlvarSIkmSFhZ1PITRXgLxqwXWCkmStFBp70JS9yzIhkiStNCp4wSinEmUkiRJcynnNE5JktSaOk4garsD0at3Z7dAqmm973q8s5sgqU7V9FkYx3WxAyG15tymKQB8NZbq5JZItemCNHXB7KjJBKI1noUhSZJa5VkYkiQVlJwD0TZv5y1Jklrydt6SJBVVx3MgvJ23JEmqmLfzliSpqDqeA+HtvCVJUsW8nbckSQWlOp4DUc5ZGHfRygWlUkrOg5AkqU6VMwfiuyU/9wD2A2ZXpzmSJC1E6ngORDlDGI+0KLovIrzIlCRJdaycIYx+JQ+7kE2kXK5qLZIkaWHhHIh2PUI2ByLIhi5eBY6qZqMkSVJtK6cDsU5K6aPSgohYrErtkSRpoVHP98Io5zoQ/2ml7P753RBJkhY6Tan6S41qM4GIiOWAgUDPiNiQT+9/sRTZhaUkSVKdam8IYxfgcGBF4Gw+7UBMBX5Y3WZJklT76nkIo80ORErpMuCyiNgvpfSPBdgmSZJU48qZA7FxRPRpfhARfSPitCq2SZKkhUNK1V9qVDkdiN1SSpObH6SUJgG7V69JkiSp1pVzGmfXiFgspfQxQET0BDyNU5KkGj5LotrK6UD8BRgTEZeQXVDqSODPVW2VJEmqaeXcC+OsiHgS2InsTIyfpZRuq3rLJEmqcfV8FkY5cyBIKd2aUvpuSun/gOkR8fsqt0uSJJUpIrpGxGMRcVP++NKIeDUiHs+XIXl5RMS5ETE2Ip6MiI1KtjE8Il7Kl+Ed7bOcIQzyHR8EHEh2L4xrixygJEmLlNqZA/Et4Dmyiz02OyGldE2LersBg/Jlc+APwOb5jTNPBjYhm67wSESMyk+caFWbCURErBURJ0XEc8DvgHFApJQ+n1I6r/JjkyRJ81tErAjsAVxURvW9gT+nzANAn4hYnuzikaNTShPzTsNoYNf2NtTeEMbzwI7AnimlrfNOQ2MZjZMkqT4sgOtARMSIiHi4ZBnRohXnAN8DmlqUn54PU/ym5CaYA4E3S+qMy8vaKm9Tex2I/YB3gbsi4o8RsSOfXs5akiQtACmlkSmlTUqWkc3PRcQXgAkppUdarPYDYG1gU6Af8P3mVVrbRTvlbWqzA5FSui6ldGDegLuB7wADIuIPETG0vY1KklQPUlOq+tKBrYC9IuI14Epgh4j4S0rpnXyY4mPgEmCzvP44YKWS9VcE3m6nvE0dnoWRUvowpfTXlNIX8g0+DpzY0XqSJKm6Uko/SCmtmFJaFRgG3JlSOiSf10BEBLAP8HS+yijgsPxsjC2AKSmld4DbgKH57Sr6AkPzsjaVdRZGSUMnAhfmiyRJ9a12rwPx14hYlmxo4nHgq3n5LWS3oxgLzACOgOz7PSJ+BjyU1/tp/p3fpoo6EJIkqTallO4mm3JASmmHNuok4BttPHcxcHG5+7MDIUlSQanleQ91pKwrUUqSJJUygZAkqajanQNRdXYgJEkqqnYuZb3AOYQhSZIqZgIhSVJB3s5bkiSpAiYQkiQV5RwISZKk8plASJJUlHMgJEmSymcCIUlSQWXcbnuRZQIhSZIqZgIhSVJRzoGQJEkqnwmEJEkFOQdCkiSpAiYQkiQV5RwISZKk8plASJJUlHMgJEmSymcCIUlSQck5EJIkSeUzgZAkqSgTCEmSpPKZQEiSVFQdn4VhB0KSpIKcRClJklQBEwhJkgpKTZ3dgs5jAiFJkipmAiFJUkHOgZAkSaqACYQkSQXVcQBhAiFJkipnAiFJUkHOgZAkSaqACYQkSQXVcQBhAiFJkipnAiFJUkHOgZAkSaqACYQkSQXVcQBhAiFJkipnAiFJUkFNdRxBmEBIkqSKmUBIklRQHQcQJhCSJKlyJhCSJBWUmuo3grADIUlSQQ5hSJIkVcAEQpKkgkwgJEmSKmACIUlSQd5MS5IkqQImEJIkFVTHAYQJhCRJqpwJhCRJBXkzLUmSpAqYQEiSVFAdBxAmEJIkqXImEJIkFeR1ICRJkipgAiFJUkF1HECYQEiSpMqZQCzitjvuq2z5leFEBPdfdBl3//YP9Orbl8OvvIR+q67MxNfe4JIDD2fm5Mns8N3j2OTgAwDo0tDAcusM5of912DGpElzbbPfqqtw+BUX06tfX8Y9+gSXHzaCxk8+oaF7dw657EJW2ngIH34wkUuHHcHE198AYOcTj2eLIw+lqbGRf3zr+zx/+5gF/lpILQ1Ya02+ctWlcx4vs/qq3HjSz1l9y00ZMHgQAL369GbG5CmcvuHWdGlo4NCLfsfKG32WLg0NPPDnK7jtjF/Ps92lV12Fr1x5CYv368sbjz7OJYd++h45/M8XsvLGG/LhBxO56MDD+SB/j+xy4vFsddRhNDU28vfjvsezvkcWCs6B0CJp+fXWYcuvDOfszXfgzCFbsd4eu7Lsmquz04nf4cU77+G0wRvx4p33sPOJ3wHgzl+dy1kbbcNZG23DTT88lbH33DdP5wFg7zNO5e5zzue0wRsxY/JktjzqMAC2OOowZkyezM/W2pC7zzmfvc44FYDl1hnMRgd+kV+svzl/2G0/vvT7s4ku/uqp841/cSynb7g1p2+4NT/feFtmzZjJ49fdyEXDjphT/ug/RvHYtTcCsPEB+9Kw2GL8bIMt+fnG27LtMUew9Corz7PdL555KmN+83tOWmtDZkyazFb5e2Srow5jxqTJnDRoCGN+83v2PTN7jyy/zmA2HbYfP11vM87b9YscdP6vfY+o5vkbuggbsM5gXv/vw3wycyZNjY2MvfffbLDvnnxmr9158LK/AfDgZX/jM3vvMc+6Gw3bn0euvKbV7Q7aYVsev+b6edYv3e7j11zPWjtul5XvvQePXnUts2fNYuJrr/Pe2FdYZbON5/vxSv+LtXfcnvdffpWJb7w5V/nGX9qXh6/I3gspJRZbvBddunale8+ezJ71CTOnTptnW4N32I5H8/fI/ZddwWf3+QIAG+y9B/dfdgUAj15zPWvvuP2c8oeu/AezZ83ig9deZ8LYV1h1s02qdaiaj1JT9ZdatcA7EBFxxILeZ7165+lnWWObz9GrX1+69ezJursNpc9KA1lywLJMfXc8AFPfHc+S/Zeda71uPXuyzq478cQ/Rs2zzcWX7sfMyVNoamwEYPK4t+k9cHkAeg9cnslvvgVAU2MjH02ZyuJL96P3wOWZ9Oa4OduY/Nbb9Bm4QlWOWSpqk2H78dAVc3ea19zmc0wbP4EJY18Gsi/9jz+cwZnvvMTP33iG0b86d56UbvGl+zFjrvfIW/TJ3yN9St4LTY2NzMzfI30HrsCk/L3TvE7ffB2pVnXGHIhTgUtaeyIiRgAjAC688MIF2aZF0vjnX+SOs87hG7ffwMfTp/PWk0/TNHt2h+utv+duvHrfA60OX0TEvCvkY4CtPZdSarNcqhVdu3Xjs3vtzvU/OGWu8k0P2n+uTsVqm21Mamzk+yusxeJ9+/B//7qN5++4m/dffW1OnUrfIyTA98hCq57/n6qSQETEk20sTwED2lovpTQypbRJSmmTESNGVKNpdeeBiy/nl5tsy7nb786MiZN476VXmDb+PZZaLvtvWGq5AUyb8N5c62x04BfbHL6Y/v4H9OzTmy5duwLQZ8UVmPL2u0CWRvRZaSAAXbp2pUfvpZgxcRKTx71N35VWnLONPgNXYMrb78z3Y5WKWn+3nXnj0Sfmei906dqVDb+4Fw9fde2csk0P/hLP3HoHTbNnM+2993n5vgdYZZMN59rW9Pc/oNdc75GBTM7fI5NK3gtdunalZ++l+HDiRCaNe4u++Xun5TqqbSlVf6lV1RrCGAAcBuzZyvJBlfapViyx7DIA9F1pRT677548csU1PH3jP9ls+MEAbDb8YJ4adcuc+j2WWoo1t9uap264pdXtAbx0178Ysv8+86z/9I23zNnukP334aU77wXgqVG3sNGBX6She3f6rboKyw5ag9cffGT+H6xU0CYHHcBDV1w9V9naO32ed59/kclvvT2nbOIbbzJ4h20B6N6rF6tvsSnvPv/iPNt74a572Sh/j2w5/CCevOFmAJ4cdQtbDj8IgI3234cX7rxnTvmmw/ajoXt3ll51FfoPWp3XHnx4/h+oNB9FNeKXiPgTcElK6d+tPPe3lNLBZWwmHdel93xvW7351j3/ZPGl+9H4ySdc938/4sU776FXv74ccdVl9F15RSa9MY5LvjR8znDFZsMPZp1dduKyg4+cazvH3HQ1Vxz9Taa+8y5Lr7bqp6dxPvYklx96NLNnzaJhscU49M8jWXHDDZgxcRKXHnQkH+TR7tAffpctjjiExtmzufY7J/LcrXcs4Fdi0XJu0xQAvhpLdXJLFn7devbkF28+x49X34CPpk6dUz78kj/wygMP8a8LL55Tttjii3PYJeez/LprExH855K/MPpX5wJw7M3XcPlXjmXKO++yzGqr8pUrL6FXv768+dgTXHLIp++RIy4fyUobfpYZEydx0bAj5gx/7PbD7/K5Iw+lcfZsrv72iTxz6+gF+TIsci5IUwFaGTOav55dY/WqZwTrvvxK1Y+jiKp0IOYTOxBSG+xASO2zA1F9XkhKkqSCavdv8OrzOhCSJKliJhCSJBVUw9MAqs4EQpIkVcwEQpKkguo4gDCBkCRJlTOBkCSpIOdASJKkhU5E9IiIByPiiYh4JiJOzctXi4j/RsRLEXFVRHTPyxfLH4/Nn1+1ZFs/yMtfiIhdOtq3HQhJkgqqgXthfAzskFL6LDAE2DUitgDOBH6TUhoETAKOyusfBUxKKa0J/CavR0SsCwwD1gN2Bc6PiK7t7dgOhCRJC6mUmZ4/7JYvCdgBaL4r4mXAPvnPe+ePyZ/fMbLbxO4NXJlS+jil9CowFtisvX3bgZAkqaAFkUBExIiIeLhkmet21RHRNSIeByYAo4GXgckppdl5lXFA8+1eBwJvZm1Ps4EpwNKl5a2s0yonUUqSVMNSSiOBke083wgMiYg+wHXAOq1Vy/9t7b4aqZ3yNtmBkCSpoNRUO2dhpJQmR8TdwBZAn4hoyFOGFYHm+9KPA1YCxkVEA9AbmFhS3qx0nVY5hCFJ0kIqIpbNkwcioiewE/AccBewf15tOHBD/vOo/DH583em7FzUUcCw/CyN1YBBwIPt7dsEQpKkgmoggFgeuCw/Y6IL8PeU0k0R8SxwZUScBjwG/Cmv/yfg8ogYS5Y8DANIKT0TEX8HngVmA9/Ih0baZAdCkqSFVErpSWDDVspfoZWzKFJKHwEHtLGt04HTy923HQhJkgqq5ytR2oGQJKmg+u0+OIlSkiQVYAIhSVJBJhCSJEkVMIGQJKmgep5EaQIhSZIqZgIhSVJB9Zs/mEBIkqQCTCAkSSqoqbMb0IlMICRJUsVMICRJKqiOT8IwgZAkSZUzgZAkqaBUx+dhmEBIkqSKmUBIklRQ/eYPJhCSJKkAEwhJkgoygZAkSaqACYQkSQU11XEEYQIhSZIqZgIhSVJBXgdCkiSpAiYQkiQVVL/5gx0ISZIK82ZakiRJFTCBkCSpoDoOIEwgJElS5UwgJEkqqKmOMwgTCEmSVDETCEmSCqrf/MEEQpIkFWACIUlSQV4HQpIkqQImEJIkFVTHAYQJhCRJqpwJhCRJBXk7b0mSpAqYQEiSVFBT/QYQJhCSJKlyJhCSJBVUxwGECYQkSaqcCYQkSQWZQEiSJFXABEKSpILq+ToQdiAkSSrIm2lJkiRVwARCkqSCmjq7AZ3IBEKSJFXMBEKSpILqeAqECYQkSaqcCYQkSQWlOj4NwwRCkiRVzARCkqSC6jd/MIGQJEkFmEBIklSQCYQkSVIFTCAkSSrIBEKSJKkCJhCSJBXU5HUgJEmSymcCIUlSQfWbP5hASJKkAkwgJEkqqKmzG9CJTCAkSVLFTCAkSSqoqY4nQdiBkCSpoKY6nkbpEIYkSaqYCYQkSQXV8xCGCYQkSaqYCYQkSQXVcQBhAiFJkipnAiFJUkHOgZAkSaqACYQkSQV5HQhJkqQKmEBIklSQcyAkSZIqYAdCkqSCmhbA0pGIuDgiJkTE0yVlp0TEWxHxeL7sXvLcDyJibES8EBG7lJTvmpeNjYgTO9xvSjWbv9RswyRJC4Wo9g5+v8QyVf+u+sb099s9jojYFpgO/DmltH5edgowPaX0qxZ11wWuADYDVgDuANbKn34R2BkYBzwEHJRSerat/dbyHIiq/8erMhExIqU0srPbIdUq3yP1pxbmQKSU7o2IVcusvjdwZUrpY+DViBhL1pkAGJtSegUgIq7M67bZgXAIQ5UY0dkNkGqc7xHNdxExIiIeLlnK/T07NiKezIc4+uZlA4E3S+qMy8vaKm+THQhJkgpqIlV9SSmNTCltUrKUk3L9AVgDGAK8A5ydl7eW7qd2yttUy0MYkiSpgJTS+OafI+KPwE35w3HASiVVVwTezn9uq7xVJhCqhGO7Uvt8j9SZplT9pYiIWL7k4b5A8xkao4BhEbFYRKwGDAIeJJs0OSgiVouI7sCwvG6bTCBUNieHSe3zPaLOEBFXANsDy0TEOOBkYPuIGEI2DPEacAxASumZiPg72eTI2cA3UkqN+XaOBW4DugIXp5SeaXe/NXwapyRJNe3sXktX/Uv0/2Z8UJNnJTqEoQ5VenERqZ60dhEf1Y9aHcJYEOxAqF0R0RX4PbAbsC5wUH4hEkmZS4FdO7sR0oLmHAh1ZDMqvLiIVE8qvIiPFjHezltqW8UXF5EkLfpMINSRii8uIkn1opybXS2qTCDUkfYuOiJJqlMmEOrInIuLAG+RXVzk4M5tkiTVhlo+S6LaTCDUrpTSbKD54iLPAX/v6OIiUj3JL+JzPzA4IsZFxFGd3SZpQTCBUIdSSrcAt3R2O6RalFI6qLPboM7jHAhJkqQKmEBIklRQUx3fDsIEQpIkVcwEQpKkgpwDIUmSVAE7EFKZIqIxIh6PiKcj4uqI6PU/bGv7iLgp/3mv9u5yGhF9IuLrBfZxSkR8t9zyFnUujYj9K9jXqt6NUvXIu3FKKsfMlNKQlNL6wCzgq6VPRqbi91RKaVRK6Yx2qvQBKu5ASFI12YGQivkXsGb+l/dzEXE+8CiwUkQMjYj7I+LRPKlYAiAido2I5yPi38AXmzcUEYdHxO/ynwdExHUR8US+fA44A1gjTz9+mdc7ISIeiognI+LUkm39KCJeiIg7gMEdHUREHJ1v54mI+EeLVGWniPhXRLwYEV/I63eNiF+W7PuY//WFlBZmTQtgqVV2IKQKRUQDsBvwVF40GPhzSmlD4EPgx8BOKaWNgIeB4yOiB/BHYE9gG2C5NjZ/LnBPSumzwEbAM8CJwMt5+nFCRAwFBpHdan0IsHFEbBsRG5NdanxDsg7KpmUczrUppU3z/T0HlF5FcVVgO2AP4IL8GI4CpqSUNs23f3R+mXNJdcazMKTy9YyIx/Of/wX8CVgBeD2l9EBevgWwLnBfRAB0J7vM8drAqymllwAi4i/AiFb2sQNwGEBKqRGYEhF9W9QZmi+P5Y+XIOtQLAlcl1Kake9jVBnHtH5EnEY2TLIE2SXLm/09pdQEvBQRr+THMBTYoGR+RO983y+WsS9pkZPq+DoQdiCk8s1MKQ0pLcg7CR+WFgGjW17eOCKGMP9ugx7AL1JKF7bYx7cL7ONSYJ+U0hMRcTiwfclzLbeV8n1/M6VU2tEgIlatcL+SFnIOYUjz1wPAVhGxJkBE9IqItYDngdUiYo28Xlv3TxgDfC1ft2tELAVMI0sXmt0GHFkyt2JgRPQH7gX2jYieEbEk2XBJR5YE3omIbsCXWzx3QER0ydu8OvBCvu+v5fWJiLUiYvEy9iMtkup5DoQJhDQfpZTey/+SvyIiFsuLf5xSejEiRgA3R8T7wL+B9VvZxLeAkfkdHRuBr6WU7o+I+/LTJP+Zz4NYB7g/T0CmA4eklB6NiKuAx4HXyYZZOvIT4L95/aeYu6PyAnAPMAD4akrpo4i4iGxuxKOR7fw9YJ/yXh1Ji5Ko5/EbSZL+F99r6FP1L9GzZk+Oau+jCBMISZIKquUhhmpzDoQkSaqYCYQkSQV5O29JkqQKmEBIklSQcyAkSZIqYAIhSVJBtXy77WozgZAkSRUzgZAkqSDnQEiSJFXABEKSpIK8DoQkSVIFTCAkSSqonudAeDdOSZJUMYcwJElSxexASJKkitmBkCRJFbMDIUmSKmYHQpIkVcwOhCRJqpgdCEmSVDE7EJIkqWJ2ICRJUsXsQEiSpIr9P+GpOJJRC/dqAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 648x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9,9))\n",
"sns.heatmap(confusion_matrix(y_valid, Y_knn), annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Reds_r');\n",
"plt.ylabel('Actual label');\n",
"plt.xlabel('Predicted label');\n",
"all_sample_title = 'Accuracy Score: {0}'.format(knn.score(X_valid_tf , y_valid))\n",
"plt.title(all_sample_title, size = 15);"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.91 0.69 0.79 13216\n",
" 1 0.16 0.46 0.24 1685\n",
"\n",
"avg / total 0.83 0.67 0.73 14901\n",
"\n"
]
}
],
"source": [
"# Checking the summary of classification\n",
"from sklearn.metrics import classification_report\n",
"print(classification_report(y_valid, Y_knn, target_names = ['NO', 'YES']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic Regression\n"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 226,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# logistic regression\n",
"from sklearn.linear_model import LogisticRegression\n",
"lr=LogisticRegression(random_state = 42)\n",
"lr.fit(X_train_tf, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Y_lr=lr.predict(X_valid_tf)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression\n",
"Training:\n",
"AUC:0.681\n",
"accuracy:0.629\n",
"recall:0.566\n",
"precision:0.648\n",
"specificity:0.692\n",
"prevalence:0.500\n",
" \n",
"Validation:\n",
"AUC:0.661\n",
"accuracy:0.659\n",
"recall:0.560\n",
"precision:0.179\n",
"specificity:0.672\n",
"prevalence:0.113\n",
" \n"
]
}
],
"source": [
"y_train_preds = lr.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_lr= lr.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Logistic Regression')\n",
"print('Training:')\n",
"lr_train_auc, lr_train_accuracy, lr_train_recall, \\\n",
" lr_train_precision, lr_train_specificity = print_report(y_train,y_train_preds, thresh)\n",
"print('Validation:')\n",
"lr_valid_auc, lr_valid_accuracy, lr_valid_recall, \\\n",
" lr_valid_precision, lr_valid_specificity = print_report(y_valid,y_valid_preds_lr, thresh)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xe8zvX7wPHX5RgnexeOPcreZBQlMy3tVKLxpdAvKRSlkKLh65uSFqWi0lBkr4Rssh37GNnb4Yzr98f7djv73Dj3uc+4no/Hebg/4/7c1/1xzn3d7y2qijHGGJOYLIEOwBhjTNpmicIYY0ySLFEYY4xJkiUKY4wxSbJEYYwxJkmWKIwxxiTJEoXJlESklIicFpGgK3juaBEZ4I+40jIReUVEPgt0HCb1WaIwiRKRnSJyzvOBekBExopI7jjnNBaROSJySkROiMhvIlIlzjl5RWSEiOz2XCvUs13YxzjGisjglHxvqrpbVXOralQyr/2EiCyM89yuqjrocl/Tl/uZlqnqW6r6VKDjMKnPEoVJzh2qmhuoBdQG+l08ICKNgBnAr0BxoCywBvhLRMp5zskOzAaqAm2AvEBj4AjQIPXeRpqR6P1MSVdSUjImMZYojE9U9QAwHfcBd9Ew4CtV/a+qnlLVo6raH1gCDPSc8zhQCrhHVTeoarSqHlTVQao69Wrj8pRolnlKM8tEpHGMY2VFZIGntDNLREaJyHjPsTIioiKS1bP9hIhs95y7Q0Q6ikhlYDTQyFMKOO45N1YJR0TuEpHVInJSRLaJSJvk4k7ofopIDhF511Py+tdTxXVNjOMvi8h+EdknIk954q8QI6aPRWSqiJwBbknqeiJSWER+F5HjInJURP4UkSyeY31EZK/nXmwWkRae/QMv3j/P9p0ist5zjXme+3Xx2E4R6S0iaz3/NxNFJPiy/nNNmmGJwvhEREKAtkCoZzsnrmTwQwKnfw+09Dy+DZimqqf9EFNBYAowEigEvA9MEZFCnlO+BZZ6jg0EHkvkOrk812irqnlw72u1qm4EugKLPdVU+RN4bgPgK+AlID9wM7DTh9hj3U+Pd4BKuORRASgBvOY5vw3QC3c/KwDNErjsI8AQIA+wMKnrAS8CYUAR4FrgFUBF5HqgO1Dfcy9aJ/R+RKQS8B3wf55rTAV+85QgL3oAV4osC9QAnkjuvpi0yRKFSc4vInIK2AMcBF737C+I+/3Zn8Bz9gMX2x8KJXJOSrgd2KqqX6tqpKp+B2wC7hCRUkB94DVVvaCqC4HJSVwrGqgmIteo6n5VXe9jDE8CX6jqTE9paa+qbkri/ATvp4gI8DTwgqdkdgp4C3jI87wHgC9Vdb2qngXeSODav6rqX6oaDZxP5noRQDGgtKpGqOqf6iZ+iwJyAFVEJJuq7lTVbQm81oPAFM/7jgDeBa7BJdmLRqrqPlU9CvxG7NKoSUcsUZjk3O35ZtkcuIFLCeAY7sO1WALPKQYc9jw+ksg5CRLXs+a052d0MqcXB3bF2bcL9825OHDU86F60Z6ELqKqZ3AffF2B/SIyRURu8DHkkkBCH6SJSex+FgFyAis8VTnHgWme/XjeT8z4E3ovMfcld73huNLMDE+VW18AVQ3FlRIGAgdFZIKIFE/gtWLde09y2oO79xcdiPH4LJBuGu5NbJYojE9UdT4wFvfN8eKH62Lg/gROfwDXgA0wC2jtqd7x5XXe8lTz5FbVrsmcvg8oHWdfKWAvrhRT0FNFdlHJJF53uqq2xCW1TcCnFw8lE8MeoHwy5yT0erHuJy6xngOqqmp+z08+T8M3uPcTEuMSCb2XmLEmeT1Pm9KLqloOuAPodbEtQlW/VdWmuHuruCqsuGLde0+JqCTu3psMxhKFuRwjgJYicrEKoS/QSUR6ikgeESngaeRtxKWqka9xH6aTROQGEckiIoU8JYd2l/HaQSISHOMnO65evJKIPCIiWUXkQaAK8Luq7gKWAwNFJLu4Hlp3JHRhEbnW0zCbC1dlcxpXBQPwLxASp+49ps+BziLSwvPeSlxGacR7Pz3fyD8FPhCRop64SohIa8+533tep7In+b2W8CWd5K4nIu1FpILnA/6k5/1Gicj1InKriOQAwnHJJqEuxN8Dt3vedzZcm8d5YJGP792kI5YojM9U9RCu4XaAZ3shrrGzA+4b7y5cl8+mqrrVc855XAPsJmAm7kNpKa7K5e/LePm+uA+tiz9zVPUI0B73IXUEeBlor6oXq7064pLWEWAwMBH3YRZXFs819gFHcQ3Fz3qOzQHWAwdE5HDcJ6rqUqAz8AFwAphP/FJOguLeT6APrjpoiYicxJXGrvec+weuwX2u55zFnuck9H4uSvR6QEXP9mnPtT5S1Xm49om3cSWSA0BRXEN33Ng3A48C//Oceweu6+8FX967SV/EFi4ymYWITAQ2qerryZ6cxnm6oq4DcqhqZKDjMRmblShMhiUi9UWkvKdKqA1wF/BLoOO6UiJyj6carQCu3eA3SxImNViiMBnZdcA8XPXKSKCbqq4KaERX5z/AIVwvqyigW2DDMZmFVT0ZY4xJkpUojDHGJClroAO4XIULF9YyZcoEOgxjjElXVqxYcVhViyR/ZnzpLlGUKVOG5cuXBzoMY4xJV0Qk7iwGPrOqJ2OMMUmyRGGMMSZJliiMMcYkyRKFMcaYJFmiMMYYkyRLFMYYY5Lkt0QhIl+IyEERWZfIcRGRkSIS6llXt46/YjHGGHPl/FmiGItbLzcxbXFTHVcEngE+9mMsxhiT6VyIjCY8IorwiISWFPGd3wbcqeoCESmTxCl3AV951uldIiL5RaSYqvprfWVjjMnQwiOimLhsDx/M2sLxsxGgSusti2m9dXHyT05CIEdmlyD2Gr9hnn3xEoWIPIMrdVCqVKlUCc4YY9K6aesO8POqMKav/zfesZAT/zJ0zhhu2vI3h8v5uuhiwgKZKCSBfQlOZauqY4AxAPXq1bPpbo0xmdKmAyeZu+kQijJs2uZ4x2+vXowbrstD3dL5afxgG9i7Gd57j8I9e0K2bFf8uoFMFGHEXiA+BLcUpTHGGCA6Wlm/7yQb9p+gz6R/Ejznm6ca0qRCYbexaBFUrwh58sBnn0HhwlCyZILPuxyBTBSTge4iMgFoCJyw9gljTGYXevA0b03dyIZ9JzlwMjze8T5tbuDeOiXIe002grMFuZ1HjkDfvi45vP46DBwItWunWEx+SxQi8h3QHCgsImHA60A2AFUdDUwF2uEWfz+LW6DeGGMynRNnIxj/9y6GT49dnVQ8XzDX5Qumx60VKV0oJ2UL50IkRq29Knz1FfTuDceOwUsvuZ8U5s9eTw8nc1yB5/z1+sYYk9YcOBHOr6v3kic4G7uOnOGTBdvjnVOmUE66NS9PyyrXUTBX9qQv2KcPDB8OjRvD6NFQvbpf4k5361EYY0x68+fWQ3y9eBczNsTvnQRwU8XCNChTkIcalKJInhxJX+zcOThzxrU/PPkkVKzo/s3iv2FxliiMMcYPdh05Q/9f1vHn1sOx9lcvkY9PH6+HCOTPmY0cWYN8v+i0afDcc1CrFkyaBNdf7378zBKFMcZchTPnIwmPiOLgqfNMWbufcYt3cio8MtY52YKECc80om7pAlf2Ivv2wf/9H/zwg0sM3btffeCXwRKFMcZchrVhx3lh4mp2Hz1LRFTiw7rKFMrJA/VL8p+byxOUJaFhYz6aPRvuuQcuXIBBg1xjdY5kqqdSmCUKY4zxwf9mb2XO5oOs2n3cu69MoZzkypGVO2oW55psQYjA3bVLkDf4yge3eUVEuEFyNWtCu3YweDBUqHD1170CliiMMSaOY2cusO/EOb5bupvxS3bHO/5E4zIMvLOqf1785EkYMAD+/hv++ss1Wk+Y4J/X8pElCmOMAUbNDWXisj38ezKc85HRsY7VDMlHleJ5eah+KWqWzO+fAFThxx/h+efhwAF49lk4fx5y5vTP610GSxTGmExL1U2R0f5/C7378ufMRp1SBWhRuSh5g7PRssq1FEhuPMPVOnQIOnWCP/5wI6p//RXq1/fva14GSxTGmExl1e5jTFi6h7+2HSbs2LlYx37s2oh6ZQqmflB588LhwzBihOv+mjVtfTSnrWiMMcYP5m0+yNqwE3y1eBeHT5+Pdaxi0dz0bFGRO2oWT92gFiyAIUPceIjcuWHJEr8OmrsaliiMMRnSjsNnePqr5YQePB3v2BONy9D/9spkDQrAB/Phw66L69ixUKYM7NwJ1aql2SQBliiMMRnI+cgoZqz/lx7frYq1v0ieHAy6qyrNKhXlmuyXMRI6JanCl1+6JHHyJPTrB/37p4nG6uRYojDGpHsXIqNp8NYst/xnDF2bladv26tb3S1FjR8PVaq4Cfyq+ql7rR9YojDGpEuHT5/nj3UHmLp2P4u3H/Huf7xRaR5uUIrKxfIGMDqPs2fhrbega1cICXHtEfnypelqpoRYojDGpGmqys4jZ3l/5haK5Qtm/uZDhB07y5kLUbHOu71GMT58uHbs9RoCaepU14Np504oUQK6dYMCVzjXU4BZojDGpFmrdh/jno8WJXjs2rw5aFutGJ0al6F4/uDLm4XVn8LC3AR+kyZB5cowfz7cfHOgo7oqliiMMWnSN3/v4tWf13m3372/JvfULnF1E+ylhiFDYMoUV+X04ouQ3c+D9VKBJQpjTJry25p9sXotDbu3Bg/ULxnAiHywdClcc41bYW7wYNezqVy5QEeVYixRGGPShJGzt/L+zC3e7UrX5uaNO6vRqHyhAEaVjBMn4JVX4OOPoX17mDwZChVyPxmIJQpjTEBN/Wc/z36zMta+cV0a0KxSkQBF5ANVmDgRXngBDh6EHj3cWhEZlCUKY0yq233kLK/+8k+8ZUL/fPkWShZM+wPQGD8eHn8c6tWD33+HunUDHZFfWaIwxqSK0IOn+HzhDnYePusd9yDivpyPfrQubapdF+AIk3H+PGzf7noyPfAAREa6ZBGURnpb+ZElCmOM34RHRLH139N0Hb+CvccvzdR6Xd5g7qxVnFfaVQ5gdJdh7lw3DuLsWdi61S1F2rlzoKNKNZYojDEpavWe44ycvZU5mw7G2p89axYG3VWVu2uXSDtjHpJz8CD07g1ff+16MY0Zk+rrVacFliiMMVdt3d4TbDt0mucnrI61v2zhXNxfL4RyhXOn/aqluEJDoUEDOH0aXn3V/VxzTaCjCghLFMaYK3LmfCS3vDuPg6fOxzs2tnN9GpcvTPas6WtOI8DN7Jo3L5QvD08+CV26uHaJTMwShTHGZ5sOnGTSijAmLtvDyfBI7/48wVkZeEdVapbMR/kiudPOfEuX48wZePNN+PRTWLvWTeI3fHigo0oTLFEYY5J08FQ4b03ZyC+r98U79p9m5ejT+gaypPVpNZLz22/QvTvs3u1KEelgjYjUZInCGJOov0IP0/Gzv73b+a7JxtAO1Wld9bq0P+eSLyIjXVfXn39260P8+Sc0bRroqNIcSxTGmFhUlT6T1vL98jDvvpsrFeHTx+umn95KyVF1gziyZoVixeDtt90o6wwwgZ8/WKIwxhAdrXQeu4z5Ww7F2l+uSC7euqc6N5bLQHMXLVni1on49FOoUwdGjQp0RGmeJQpjMrGFWw/zzrRN/LP3hHdfcLYstK9RnM5NylC1eL4ARpfCjh1zE/h98gkUL+62jU/8mihEpA3wXyAI+ExV345zvBQwDsjvOaevqk71Z0zGZHbnI6P4ddU+Jq/Zx8LQS3Mt1SqZn0ndGmeMtoe4Jk6Enj3h8GG3qNAbb0CePIGOKt3wW6IQkSBgFNASCAOWichkVd0Q47T+wPeq+rGIVAGmAmX8FZMxmVV0tDJi9lY+mhtKZLTGOvbJY3VpWfna9N9zKSmbNkGZMjBtGtSuHeho0h1/ligaAKGquh1ARCYAdwExE4UCF1dAzwfE739njLliG/ef5L0Zm5m1MfZ0GrVL5WfEg7UIKZAzY5YgwsPhnXdcG8Qdd7gqp/79M8UEfv7gz0RRAtgTYzsMaBjnnIHADBHpAeQCbkvoQiLyDPAMQKlSpVI8UGMymkXbDvPsNys5fjbCuy9PcFYmPtOIKsXzJvHMDGDWLHj2WTd534svukSRLVugo0rX/JkoEvqaonG2HwbGqup7ItII+FpEqqlqdKwnqY4BxgDUq1cv7jWMyfT+Cj3M0h1H+Xj+Ni5Exvrz4fU7qvBE4zLpc7T05fj3X+jVC779FipUgBkzoGXLQEeVIfgzUYQBMRe6DSF+1dKTQBsAVV0sIsFAYeAgxpgkXYiMplL/PxI8VrNkfl5tV5kGZQumclQBNHMm/PgjvPYa9OsHwcGBjijD8GeiWAZUFJGywF7gIeCROOfsBloAY0WkMhAMHMIYkyBVZcHWw6zcdYz/zt7q3V88XzDvPVCL8kVyUTRvJvqAXLPGVTHddx907AhNmkDZsoGOKsPxW6JQ1UgR6Q5Mx3V9/UJV14vIm8ByVZ0MvAh8KiIv4KqlnlBVq1oyJgEnzkZQ880ZsfZVLJqbGS/cnPGrleI6fRpefx3++1/Xm+nuu90oa0sSfuHXcRSeMRFT4+x7LcbjDUATf8ZgTHoWHhHF4CkbGL9kd6z9Xz/ZgIpF83BdvkxUerjol1+gRw8IC4NnnoGhQ12SMH5jd9eYNOjshUge+GQx6/aejLX/1XaVebJp2Yw95iEp//wD99wD1au7QXSNGwc6okzBEoUxaUR0tBJ27BzjFu/k84U7vPvrlS7AV082IGf2TPrnGhHhZnW99VaXIKZMcb2ZrMtrqsmkv3nGpA0fz9vGDyv2sP3QmXjHqpXIy2/dm2a+9oeYFi2Crl1h/XrYvNl1e23XLtBRZTqWKIwJkN4/rOHHFZem8m5YtiDF8gVzU8UiNL++CIVy5whgdAF29Cj07etmeC1ZEn76ySUJExCWKIxJZdHRym0fzPeWIn7v0ZRqJTLQLK1XKzwcatWCffvcyOqBAyF37kBHlalZojAmlRw7c4HBUzYyaeWlUsScF5tRroh9CAKuF1NIiBsoN2iQSxY1awY6KoMlCmP8KiIqmiFTNjJ20c5Y+wvnzsGivreSPWuWwASWlpw757q4vvOOG1l9xx3QqVOgozIx+JQoRCQ7UEpVQ/0cjzEZxodztvLujC2x9nVpUpbut1agYC5bchNw8zE9+yxs2waPPgoNGgQ6IpOAZBOFiNwOvA9kB8qKSC3gdVW9x9/BGZMeHTwVTqsPFnhnbs2eNQvL+99G3mDrzhlLjx7w4YdQsaKb8bVFi0BHZBLhS4niTdz04HMBVHW1iFj3A2MS8NPKMHp9v8a7PfOFm6l4ra2k5hUV5f4NCoIbb4TChaFPH5vAL43zJVFEqOrxOH25bT4mY2LYcfgMt7w7z7vdqVFpXrm9Mjmy2kI5XitXujERjz3mShMdOwY6IuMjXxLFRhF5AMjimQn2eWCJf8MyJn3Yd/wcz327klW7j3v3ffp4PVpWuTaAUaUxp065qb9HjoQiRaBYsUBHZC6TL4miO/AaEA38hJsNtp8/gzImrTsVHkHnL5exfNcx775bri/Cl52tMTaWGTOgSxc3JqJrV3jrLcifP9BRmcvkS6Jorap9gD4Xd4hIB1zSMCbTWLf3BFP+2c/H87bF2v9k07IMaF8lQFGlcdmzQ9GiMGkSNIy7ErJJLyS55R9EZKWq1omzb4Wq1vVrZImoV6+eLl++PBAvbTKpdXtP0P5/C+Ptb1XlWj5+tC5BmXUm14RERMD778PJkzBkiNsXHQ1ZbLxIoHk+t+tdyXMTLVGISGvcMqUlROT9GIfy4qqhjMnQjp65QJ1BM2PtG3JPNe6rG2KN1AlZuPDSBH73338pQViSSPeSqno6CKwDwoH1MfafAvr6MyhjAu3QqfPUHzLLu/3JY3VpXfW6AEaUhh054rq4fv45lCoFv/0G7dsHOiqTghJNFKq6ClglIt+oangqxmRMwOw5epbOY5cRevC0d1/okLZkDbJvxYk6cgQmTICXX3a9m3LlCnREJoX50phdQkSGAFUA76gYVa3kt6iMSWVb/j1Ft/Er2BZjXYj764Yw7L4amXs9iMRs3Ajff+/Wra5UCXbvhoIFAx2V8RNfEsVYYDDwLtAW6Iy1UZgM5O0/NjF6/qWeTA83KMWgu6paKSIhZ8+6Rurhw93U308+6WZ8tSSRofmSKHKq6nQReVdVtwH9ReRPfwdmjL+dj4yi46d/e8dCdKhdgmH31bAEkZhp09wEfjt2uNldhw93A+hMhudLojgvruy9TUS6AnuBov4Nyxj/UVVe/WUd3/6927tvbOf6NL/efq0Tdfq0m3qjUCGYOxeaNw90RCYV+ZIoXgByAz2BIUA+oIs/gzLGX1btPsY9Hy3ybl+bNwczXmhGvmtsZtd4oqLgu+/g4YddNdOsWXDDDZAjEy/RmkklmyhU9W/Pw1PAYwAiEuLPoIxJaQkNmlvR/7bMvS51UlasgP/8x/17zTVw77222lwmlmSiEJH6QAlgoaoeFpGquKk8bgUsWZg0TVXpNn4l09YfiLV/2L01eKB+yQBFlcadOAEDBsCoUW7qjQkToEOHQEdlAiypkdlDgXuBNbgG7J9xM8e+A3RNnfCMuTJxSxA5smZhxIO1aFvdZi5N0r33wpw58NxzMHgw5MsX6IhMGpBUieIuoKaqnhORgsA+z/bm1AnNmMsXduwsTd+ZG2vf5sFtbMqNpGzf7nov5cnjur5myQL16wc6KpOGJJUowlX1HICqHhWRTZYkTFoTHa38uDKMBVsO8fva/bGOffJYXVpVudYGzCXmwgV4910YNAh69oR33rEZXk2CkkoU5UTk4lTiApSJsY2qWsWlCbgbBkzjQtSl8Z/F8wXT7ZYKPHZj6QBGlQ4sWOAm8Nu4Ee67zyUKYxKRVKK4N872h/4MxJjL9cAni71J4rfuTalWIq+VHnzxwQfQqxeUKQNTpkC7doGOyKRxSU0KODs1AzHGV0t3HOWBTxZ7t9e83srGQSQnOhrOnHHtELffDocOQf/+kDNnoCMz6YDNVWDSlan/7I+VJBb1vdWSRHLWr4dmzeCJJ9x2pUpuSVJLEsZHfk0UItJGRDaLSKiIJLiGhYg8ICIbRGS9iHzrz3hM+tbju1U8+81KAB5pWIodQ9tRPP81AY4qDTt7Fvr1g1q1XFtE+/aQzIqWxiTElyk8ABCRHKp6/jLODwJGAS2BMGCZiExW1Q0xzqkI9AOaqOoxEbHJdkw8+0+co9HQOd7t9x+oSYc6Nt4zSatWuYFyO3dC584wbBgULhzoqEw6lWyiEJEGwOe4OZ5KiUhN4ClV7ZHMUxsAoaq63XOdCbixGRtinPM0MEpVjwGo6sHLfwsmo/p5VRgvTFwTa9+kbo2pW7pAgCJKB1RBxK00V6oUjBsHN98c6KhMOudLiWIk0B74BUBV14jILT48rwSwJ8Z2GBC3k3YlABH5CwgCBqrqNB+ubTKw3j+sYfLqfd4eTVkE+ra9gU6Ny9jAucRERsKHH8LkyTBzppvldf78QEdlMghfEkUWVd0Vp9thlA/PS6ifYtwK0qxARaA5bu6oP0Wkmqoej3UhkWeAZwBKlSrlw0ub9Gjauv10Hb/Su12/TAG6NitPi8rXBjCqdGDpUjcmYtUqaNsWTp6EAlbqMinHl0Sxx1P9pJ52hx7AFh+eFwbEnHktBDcNSNxzlqhqBLBDRDbjEseymCep6hhgDEC9evWsNS6D+XPrIXr/sIZ/T15qAvuxayPqlbFV05J0+jT06QMffwzFisEPP7i5mmwsiUlhviSKbrjqp1LAv8Asz77kLAMqikhZ3GJHDwGPxDnnF+BhYKyIFMZVRW33LXST3u09fo4mb8+Jte+rLg24uZKtmuaTbNlg3jzo0cNNw5E3b6AjMhmUL4kiUlUfutwLq2qkiHQHpuPaH75Q1fUi8iawXFUne461EpENuOqsl1T1yOW+lkl/pv6z39vVFWBclwbcXLGwjaxOTmgovPmmmwY8Tx63XkRwcKCjMhmcaDL9qkVkG7AZmAj8pKqnUiOwxNSrV0+XL18eyBDMVVgbdpyHxizh7AXXzNXihqJ8/oTNVJqs8+ddF9chQyB7djf1xk03BToqk46IyApVrXclz/VlhbvyItIYV3X0hoisBiao6oQreUGTeVUfOJ1T4ZHe7bfuqc4jDa1zQrLmzoVu3WDzZnjwQXj/fShePNBRmUzEpwF3qroIWCQiA4ERwDeAJQrjk52Hz9D83Xne7bGd63NzxSJkyWLVTMlSdaWIiAiYNg1atw50RCYT8mXAXW7cQLmHgMrAr0BjP8dlMoBT4RE8+tnfrAk74d23dmAr8gbb3ExJio6Gzz+HNm2gZEn4+mvIn9+tXW1MAPgy19M64EZgmKpWUNUXVfVvP8dl0rnQg6epPnCGN0l0aVKWnW/fbkkiOWvXQtOm8Mwz8Nlnbl+xYpYkTED5UvVUTlWjkz/NGOeFiav5edVeAPIGZ2XN662sN1NyTp+GN95wa0UUKABjx8Ljjwc6KmOAJBKFiLynqi8Ck0QkXtcoW+HOxPXZn9sZPGWjd7tVlWsZ8/gVdbLIfAYOhPfeg6eegrffdlNwGJNGJFWimOj511a2M0mK21hdoWhuJj5zI4Vy5whcUOnBnj1uMaEbboC+feHuu121kzFpTFIr3C31PKysqrGShWcgna2AZ5i76SCdx16aceXbpxvSuLxNZ52kyEgYORJeew3q1nWT9xUubEnCpFm+tFF0IX6p4skE9plM5HxkFNVen05E1KVayR1D21lbRHKWLHET+K1Z45Yk/dD+jEzal1QbxYO4LrFlReSnGIfyAMcTfpbJDHYdOUOz4fO826MfrUObasUCF1B6MWUK3HGHGyz300+uqskSq0kHkipRLAWO4GZ9HRVj/ylglT+DMmnXwZPhsZKElSKSoQr79kGJEnDbbW6epuefd/M0GZNOJNVGsQPYgZst1hgA7vhwIQB31yrOiIdqBziaNG7LFnj2Wffvhg2QOzf07x/oqIy5bIkOuBOR+Z5/j4nI0Rg/x0TkaOqFaNKCVbuPcdv7871rRliSSEJ4uOvuWr06LF8O/frZgDmTriVV9XQX2M18AAAgAElEQVRxuVPrwpKJqSqtPljA1oOnvfvm9W4euIDSugMH3BrVW7fCww+7Cfyuuy7QURlzVZKqero4GrsksE9VL4hIU6AGMB44mQrxmQBat/cE7f+30Lv9bPPyvNjqeoJsMr/4IiLcQkLXXusSxahR0LJloKMyJkX40j32F6C+iJQHvgKmAN8C7f0ZmAmcb/7exas/r/Nu58oexIoBLQnOFhTAqNKo6GgYMwbeegsWLYKQkEtzNBmTQfiSKKJVNUJEOgAjVHWkiFivpwxo84FTtB6xINa+wXdX49EbSwcoojRuzRr4z3/g77/h1ltdqcKYDMinpVBF5H7gMeBuzz6bAjSDiY7WWEliVq9mVCiaO4ARpWGq8NJLMGIEFCzopgHv2NHGRJgMy5dpxrvgGraHqep2ESkLfOffsExq+nFFGOVemQpAzuxB7Hz7dksSSRGBY8fgySfdqnOPPmpJwmRoya6ZDSAiWYEKns1QVY1M6nx/sjWzU05UtFLx1alEe34FyhTKyewXm1tjdUJ27XID5V57DerUcW0TWXz5nmVM2nA1a2Yn+5suIjcBocDnwBfAFhFpciUvZtKGiKhoPl+4g/KvXEoSP3RtxLyXbrEkEVdEBAwbBlWqwMyZrgQBliRMpuJLG8UHQDtV3QAgIpWBrwFbaCAd+iv0MB0/u7RAYd7grKx6rZUliIQsWuQaq9etg7vucjO+lioV6KiMSXW+JIrsF5MEgKpuFJHsfozJpLBT4RF89ucO/jt7q3dfqYI5mdStMUXy2JoRiZo1C06cgF9+cYnCmEwq2TYKERkLnMeVIgA6AjlVtZN/Q0uYtVFcHlWlydtz2HciHIB812Tj/26rSOcmZQMcWRqk6nowFSkCbdvC+fOu6im3Neyb9O9q2ih8KVF0BXoCLwMCLAD+dyUvZlLXotDDPBKjmmnToDY2aC4xmzZBt24wbx7cf79LFDlyuB9jMrkkE4WIVAfKAz+r6rDUCcmkhAG/rOPrJbu82zNfuNmSRELOnXOjqt95B3Llgk8+cetWG2O8kpo99hXc9B0dgZki0iXVojJXZfj0Td4k0a15eXa+fTsVr7X1DxL0228weDA8+KArVTzzjPVoMiaOpEoUHYEaqnpGRIoAU3HdY00atvnAKUbN3QbAwj63EFIgZ4AjSoMOHIDVq6FNG1fNVKYMNGgQ6KiMSbOSShTnVfUMgKoeEhH7mpWGRUUrHT9bwpLtbqmQp28qa0kirqgoV7XUrx9kzw67d7t1IixJGJOkpBJFuRhrZQtQPuba2arawa+RGZ+dOBdBzTdmeLcfvbEUr95eJYARpUErV0LXrrBsmVuS9KOPbDEhY3yUVKK4N872h/4MxFy52m9eShIr+t9GodzWUyeWHTtcqaFwYfj2W3joIZubyZjLkNTCRbNTMxBzZdqMWOCdhiN0SFuyBlkNIeDGRPzzD9SoAWXLwpdfwh13QP78gY7MmHTHPlXSoehoZeDk9ZTpO4VNB04BruHakoTHjh3Qvj3Urg1r17p9jz1mScKYK+TXTxYRaSMim0UkVET6JnHefSKiImLzRyUjMiqacq9MZeyinQDcVLEwG95sbQ3XABcuwNtvQ9WqMH8+vPuum8zPGHNVfBmZDYCI5FDV85dxfhAwCmgJhAHLRGRyzHmjPOflwY38/jv+VUxMo+aGMnz6Zu/24n63UiyfNcgCrkdT48awYgV06OAWFSpZMtBRGZMh+DLNeAMR+QfY6tmuKSK+TOHRALd2xXZVvQBMABKaWW0QMAwI9z3szKdM3yneJFEgZza2v9XOkgTAyZPu36Ag6NLFDaCbNMmShDEpyJeqp5FAe+AIgKquwa14l5wSwJ4Y22GefV4iUhsoqaq/J3UhEXlGRJaLyPJDhw758NIZy7sxShF/vnwLq15rRZbMPi24KowdC+XKwa+/un3PPuvaJowxKcqXRJFFVXfF2Rflw/MS+iTzTlXrGcD3AfBichdS1TGqWk9V6xUpUsSHl8443vxtAx/ODQVgbu/mlCxobRFs2ADNm0PnznDDDVC+fKAjMiZD8yVR7BGRBoCKSJCI/B+wxYfnhQExy/8hwL4Y23mAasA8EdkJ3AhMtgbtS577ZiVf/LUDgJdaX0/ZwrkCHFEaMGwY1KzpFhP67DNYsACqVQt0VMZkaL40ZnfDVT+VAv4FZnn2JWcZUFFEygJ7gYeARy4eVNUTQOGL2yIyD+itqrbYBNDvp3+Y8s9+wAbRAa6qSQSuuw46doThw926EcYYv0s2UajqQdyH/GVR1UgR6Q5MB4KAL1R1vYi8CSxX1cmXHW0mcO5CFPd+vIgN+10j7eTuTTJ3kti3D55/Hm66CXr2hMcfdz/GmFSTbKIQkU+J0bZwkao+k9xzVXUqbtbZmPteS+Tc5sldLzPoPHapN0lM6taYGiGZdJBYVJSbj+nVV90qc40bBzoiYzItX6qeZsV4HAzcQ+zeTCYFnAqPoPrAS3M27RjaDsms8xGtXu0WD1qxAlq1cgnDGqyNCRhfqp4mxtwWka+BmX6LKBPafeQsNw+f693+5bkmmTdJAJw44aqcJk5060Vk5nthTBrg88jsGMoCpVM6kMzq5R/X8P3yMO92pixJqMIPP8DWra6qqVkz2L4dgoMDHZkxBt9GZh8TkaOen+O40sQr/g8t42s0dLY3STx2Y+nMmSS2bYN27dxSpL/+6tojwJKEMWlIkiUKcZ9aNXHdWwGiVTVew7a5PJFR0VR49Q/v9t+vtODavJnsg/H8eTdp3+DBkC0b/Pe/bmR11isp5Bpj/CnJEoUnKfysqlGeH0sSV+n42QuxksSivrdmviQBsGcPDBrkptzYuNF1fbUkYUya5MvI7KUiUsfvkWQCEVHR1HrzUj+A0CFtKZ4/E03sd+gQfOhZKLFCBTcVxw8/QIkSST/PGBNQiSYKEbn49a4pLllsFpGVIrJKRFamTngZR3S0UjFGSWLH0HaZZ6Gh6Gj4/HM3L1OvXrDZM8lhuXKBjcsY45OkyvpLgTrA3akUS4bW4v353sehQ9pmnkbrdeugWzdYuNCNrh49Gq6/PtBRGWMuQ1KJQgBUdVsqxZIhRUcr5V65NDh906A2mackceGCGzB34QJ88QU88YSNiTAmHUoqURQRkV6JHVTV9/0QT4YyYtYWRsza6t1e90ZrgrMFBTCiVDJnjhsLkT07fP+9q3IqXDj55xlj0qSkvtoGAblx04En9GOS8PYfm7xJombJ/Kx5rRW5c2TwXj1hYXDvvdCiBXz1ldvXtKklCWPSuaQ+ufar6pupFkkGsuvIGUbPdzV2X3auzy3XFw1wRH4WGel6Mw0Y4CbzGzrUTQVujMkQkm2jMJfnp5Vh9Pp+DQDP3Fwu4ycJgMcegwkToG1bGDUKypYNdETGmBSUVKJokWpRZBAx2yRqhuTjlXaVAxyRHx0/7gbI5c4Nzz3nqpzuvdcaq43JgBJto1DVo6kZSHrX8bMl3iTRrXl5fu3eNMAR+YmqKz1UruyqmsC1Q9x3nyUJYzKoDN66mjq6jV/BX6FHALfYUN3SBQIckZ+Ehrr5mGbOhHr14NFHAx2RMSYVZJIO/f6zZPsR/lh3AIDh99XIuEni22+hWjX4+2/XcL1kCdStG+iojDGpwEoUV+mhMUsA+OKJetx6w7UBjsYPIiLc7K716rnqpWHDoHjxQEdljElFVqK4QqpKBc+I65zZgzJekjh40PVmevBBt12pEowfb0nCmEzIEsUVOHTqPGX7TSUy2s26vnJAywBHlIKio2HMGDcf08SJULWqGxthjMm0rOrpMqkq9YfM8m5vGtQm40zLsX27a6BevBiaN4ePP3bTbxhjMjVLFJepbL9LE/ztfPv2AEbiB/nyufER48a5aifr7mqMwaqeLssn8y9NpLtlcNsARpKCJk+GDh1c9VKhQm5a8McftyRhjPGyROGj39bsY+gfmwBY8NItZM+azm/d7t1w991w112wZQvs3+/2Z0nn78sYk+LsU8EHI2dvpcd3qwD4qksDShXKGeCIrkJkJLz7rhtZPWMGvPMOrFoFISGBjswYk0ZZG0UyFm87wvsztwDwYstK3FypSIAjukpRUfDZZ3DrrfC//0GZMoGOyBiTxlmJIgmfzN/Gw5+6AXXtaxSjR4uKAY7oCh07Bn36wKlTkCMH/PWXa5uwJGGM8YGVKBKx7dBpb5vEh4/Upn2NdDjQTNVNvdGrFxw5Ak2awJ13ukZrY4zxkZUoEnD0zAVavDcfgE6NSqfPJLFlC7Rs6cZFlCkDy5e7JGGMMZfJEkUcXy3eSZ1BMwFoXL4Qb9xVLbABXan/+z+XHD76CBYtglq1Ah2RMSadsqqnGL5ftofXfl0PuNXp+rVNZ6OSZ850I6lLlnSjqnPkgOuuC3RUxph0zq8lChFpIyKbRSRURPomcLyXiGwQkbUiMltESvsznqR8vnAHL09aC8Abd1bllXaVkfQy6OzAAXjkEWjVynV3BShd2pKEMSZF+C1RiEgQMApoC1QBHhaRKnFOWwXUU9UawI/AMH/Fk5xBv28A4NunGtKpcZlAhXF5oqNh9GhXipg0CV5/3Y2RMMaYFOTPEkUDIFRVt6vqBWACcFfME1R1rqqe9WwuAQIy6uvnVWEA5A3OSuMKhQMRwpUZOhS6dXMLCK1dCwMHQnBwoKMyxmQw/myjKAHsibEdBjRM4vwngT8SOiAizwDPAJQqVSql4gMgOlp5YeIaAH7vcVOKXtsvTp2Cw4ehbFno2tX9+/DDNjeTMcZv/FmiSOiTSxM8UeRRoB4wPKHjqjpGVeupar0iRVJuZLSqUu6VS7PBpumpOVTh55+hShW3mJCqGw/xyCOWJIwxfuXPRBEGlIyxHQLsi3uSiNwGvArcqarn/RhPLNHRGmvK8O1vtUutl758u3a5MRAdOkDBgjBypCUHY0yq8WfV0zKgooiUBfYCDwGPxDxBRGoDnwBtVPWgH2OJ58tFO72PNw1qQ5YsafSDd/FiuO029/jdd+H55yGr9Wo2xqQev5UoVDUS6A5MBzYC36vqehF5U0QuDhEeDuQGfhCR1SIy2V/xxPXFwh2AW1ciTa5Qd/Kk+7dOHejSBTZuhBdftCRhjEl1fv3UUdWpwNQ4+16L8fg2f75+Yvr9tJa9x8+RNYukvXUljhyBvn3dFODr10Pu3G6WV2OMCZA09inpf30nreW7pa4z1qiOdQIcTQyq8NVXbkzEl1+6BmtrhzDGpAGZqh6j76S1TFjmksTk7k2oEZI/wBF5nDjhVpubNw8aNXKD6GrUCHRUxhgDZKJEMXfzQW+S6NPmhrSRJFRdqSFvXihcGMaMgSeftOVIjTFpSqZJFJ2/XAbArF7NqFA0d4CjAaZPd20Rv/3mliH94YdAR2SMMQnKFF9d/wo97H0c8CSxfz889BC0aQNnz8LBVO0VbIwxly3DJ4rwiCg6fvY3AFN7BniKjlGjXGP1L7/AG2+4+ZnqpKEGdWOMSUCGr3pqM2IBANfmzUGV4nkDG8yKFdCwoUsYFdPp+tvGmEwnQ5cowo6dZecRNzntor4tUj+AkyfdSnMrVrjtjz5ybROWJIwx6UiGTRThEVE0fWcuAG93qE5Qak7RoQo//giVK7t5mea79bcJDraxEcaYdCfDJoqnxi0HoHKxvDzUIGWnJk/Sjh3Qvj3cfz8ULermaurVK/Ve3xhjUliGTBThEVEs9PR0mtKjaeq++DffwIIF8MEHsGyZa5Mwxph0LEM2Zt8wYBoAr7arnDqzwv75J5w/72Z5fekleOIJNzbCGGMygAxXoliz57j3cecmZfz7YocPu5ldb74Z3nzT7cuRw5KEMSZDyVAlivCIKO4a9RcA47o0IGuQn/KgKowd60oPJ05Anz4wYIB/Xst4RUREEBYWRnh4eKBDMSbNCg4OJiQkhGzZsqXYNTNUovhobigANUPy0axSyi2ZGs/Uqa4k0aSJm8CvWjX/vZbxCgsLI0+ePJQpUwax3mPGxKOqHDlyhLCwMMqWLZti181QVU8j57hE8X3XRil/8bNn4S9XWqFdO/j1V9dobUki1YSHh1OoUCFLEsYkQkQoVKhQipe6M0yiGPrHRgCql8hHjqwpvGLdH3+4hNC2LRw/7sZC3HmnzfIaAJYkjEmaP/5GMswn3SfztwPw/gM1U+6ie/e68RDt2rlG6t9+g/xpYHpyY4xJRRkiUUxbtx+AckVyUfHaPClz0YMHoUoV+P13GDwY1qyBZs1S5tom3cqd++pnH963bx/33XdfosePHz/ORx995PP5cT3xxBOULVuWWrVqUbNmTWbPnn1V8aa00aNH89VXX6XItfbv30/79u1T5Fr+Mm7cOCpWrEjFihUZN25couf973//4/rrr6dq1aq8/PLL3v1r166lUaNGVK1alerVq3urlW677TaOHTvm9/gB1/iRnn7q1q2rMUVHR2vpPr9r6T6/6+4jZ/SqhYVdevzf/6qGhl79NU2K2LBhQ6BD0Fy5cvn9NXbs2KFVq1a94ud36tRJf/jhB1VVnTNnjlaoUCFF4oqIiEiR66Sk3r176y+//OLz+ZGRkX6MJr4jR45o2bJl9ciRI3r06FEtW7asHj16NN55c+bM0RYtWmh4eLiqqv7777+q6u559erVdfXq1aqqevjwYe97GDt2rA4ePDjB103obwVYrlf4uZvuez1dXP86f85slCyY88ovdOIE9O8Pn3wCS5a46b979kyhKE1Ke+O39WzYdzJFr1mleF5ev6PqZT9v165ddOnShUOHDlGkSBG+/PJLSpUqxbZt2+jYsSNRUVG0bduW999/n9OnT7Nz507at2/PunXrWL9+PZ07d+bChQtER0czadIkBgwYwLZt26hVqxYtW7bkueee854fFRVFnz59mD59OiLC008/TY8ePRKNrVGjRuzdu9e7vWLFCnr16sXp06cpXLgwY8eOpVixYixbtownn3ySXLly0bRpU/744w/WrVvH2LFjmTJlCuHh4Zw5c4Y5c+YwfPhwvv/+e86fP88999zDG2+8wZkzZ3jggQcICwsjKiqKAQMG8OCDD9K3b18mT55M1qxZadWqFe+++y4DBw4kd+7c9O7dm9WrV9O1a1fOnj1L+fLl+eKLLyhQoADNmzenYcOGzJ07l+PHj/P5559z003xlwmYNGkSgwcPBmDnzp089thjnDlzBoAPP/yQxo0bM2/ePN544w2KFSvG6tWr2bBhA+PHj2fkyJFcuHCBhg0b8tFHHxEUFES3bt1YtmwZ586d47777uONN9647N+HmKZPn07Lli0pWLAgAC1btmTatGk8/PDDsc77+OOP6du3Lzly5ACgaNGiAMyYMYMaNWpQs6arUi9UqJD3OXfeeSc33XQTr7766lXF6It0X/X00TzX02l2ryusFlKF7793E/iNGgVdu0L58ikYocnounfvzuOPP87atWvp2LEjPT1fMJ5//nmef/55li1bRvHixRN87ujRo3n++edZvXo1y5cvJyQkhLfffpvy5cuzevVqhg8fHuv8MWPGsGPHDlatWuV9vaRMmzaNu+++G3DjUHr06MGPP/7IihUr6NKli/dDpnPnzowePZrFixcTFBS7M8jixYsZN24cc+bMYcaMGWzdupWlS5eyevVqVqxYwYIFC5g2bRrFixdnzZo1rFu3jjZt2nD06FF+/vln1q9fz9q1a+nfv3+8+B5//HHeeecd1q5dS/Xq1WN9MEdGRrJ06VJGjBiR4Af2jh07KFCgQKwP15kzZ7Jy5UomTpzo/X8AWLp0KUOGDGHDhg1s3LiRiRMn8tdff7F69WqCgoL45ptvABgyZAjLly9n7dq1zJ8/n7Vr18Z73eHDh1OrVq14Pz0T+GK5d+9eSpYs6d0OCQmJlbgv2rJlC3/++ScNGzakWbNmLFu2zLtfRGjdujV16tRh2LBh3ucUKFCA8+fPc+TIkXjXS2npukSxKPQwYcfOkTWLUCh3jsu/gCp06OAWEqpTByZPhnr1Uj5Qk+Ku5Ju/vyxevJiffvoJgMcee8xbv7x48WJ++eUXAB555BF69+4d77mNGjViyJAhhIWF0aFDByomMwX9rFmz6Nq1K1mzuj/di99U43rppZd4+eWXOXjwIEuWLAFg8+bNrFu3jpYtWwIQFRVFsWLFOH78OKdOnaJx48beWH///XfvtWJ+I54xYwYzZsygdu3aAJw+fZqtW7dy00030bt3b/r06UP79u256aabiIyMJDg4mKeeeorbb789XlvCiRMnOH78OM08bX+dOnXi/vvv9x7v0KEDAHXr1mXnzp3x3uP+/fspUuTSeKmIiAi6d+/u/fDfsmWL91iDBg284wpmz57NihUrqF+/PgDnzp3zfoP//vvvGTNmDJGRkezfv58NGzZQo0aNePf2pZdeSvC+x+VqfGJLqFdSZGQkx44dY8mSJSxbtowHHniA7du3ExkZycKFC1m2bBk5c+akRYsW1K1blxYt3LIJRYsWZd++fbFKGv6QrhPFk54ZYod2qH55T4yIgGzZXDfXpk3h1lvh2WchKIW71ZpM6XK6Jz7yyCM0bNiQKVOm0Lp1az777DPKlSuX6Pmq6tP1hw8fTocOHRg5ciSdOnVixYoVqCpVq1Zl8eLFsc5NrkE0V65csV6/X79+/Oc//4l33ooVK5g6dSr9+vWjVatWvPbaayxdupTZs2czYcIEPvzwQ+bMmZNs7BddLCkEBQURGRkZ7/g111wTa7zABx98wLXXXsuaNWuIjo4mODg40ffQqVMnhg4dGut6O3bs4N1332XZsmUUKFCAJ554IsHxCMOHD/eWQGK6+eabGTlyZKx9ISEhzJs3z7sdFhZG8+bN4z03JCSEDh06ICI0aNCALFmycPjwYUJCQmjWrBmFCxcGoF27dqxcudKbKMLDw7nmmmviXS+lpduqpyXbj3AuIoo8ObJyf72SyT/honnzoEYNN2AO4MUXoUcPSxLmijVu3JgJEyYA8M0339C0qZux+MYbb2TSpEkA3uNxbd++nXLlytGzZ0/uvPNO1q5dS548eTh16lSC57dq1YrRo0d7PziPHj2aaFxZsmTh+eefJzo6munTp3P99ddz6NAhb6KIiIhg/fr1FChQgDx58nhLHonFCtC6dWu++OILTp8+DbiqlYMHD7Jv3z5y5szJo48+Su/evVm5ciWnT5/mxIkTtGvXjhEjRrB69epY18qXLx8FChTgzz//BODrr7/2li58UalSpVgljRMnTlCsWDGyZMnC119/TVRUVILPa9GiBT/++CMHPevVHz16lF27dnHy5Ely5cpFvnz5+Pfff/njjz8SfP5LL73E6tWr4/3ETRIX79eMGTM4duwYx44dY8aMGbRu3TreeXfffbc3iW7ZsoULFy5QuHBhWrduzdq1azl79iyRkZHMnz+fKlWqAC7hHThwgDJlyvh8z65Uui1RPDTG/VIPv79GMmd6HDoEvXvDV19B2bKQJ4W60ZpM5ezZs4TEmPSxV69ejBw5ki5dujB8+HBvYzbAiBEjePTRR3nvvfe4/fbbyZcvX7zrTZw4kfHjx5MtWzauu+46XnvtNQoWLEiTJk2oVq0abdu25bnnnvOe/9RTT7FlyxZq1KhBtmzZePrpp+nevXui8YoI/fv3Z9iwYbRu3Zoff/yRnj17cuLECSIjI/m///s/qlatyueff87TTz9Nrly5aN68eYKxgktUGzdupFEjN/tB7ty5GT9+PKGhobz00ktkyZKFbNmy8fHHH3Pq1CnuuusuwsPDUVU++OCDeNcbN26ctzG7XLly3nvni1y5clG+fHlCQ0OpUKECzz77LPfeey8//PADt9xyS6xSRExVqlRh8ODBtGrViujoaLJly8aoUaO48cYbqV27NlWrVqVcuXI0adLE51gSU7BgQQYMGOCt5rr4/wvu/7Jr167Uq1ePLl260KVLF6pVq0b27NkZN24cIkKBAgXo1asX9evXR0Ro164dt99+O+BKcDfeeKO3GtKvrrS7VKB+6tatqxcio7R0n9+1witTEuwaFs+336oWKKCaLZvqK6+onkmBbrQm1aWF7rGX48yZMxodHa2qqt99953eeeedAY4ocadOnfI+Hjp0qPbs2TOA0fjup59+0ldffTXQYQREz549ddasWQkes+6xwN/bXXG7x60+rj0dGemm4Bg92g2iMyYVrFixgu7du6Oq5M+fny+++CLQISVqypQpDB06lMjISEqXLs3YsWMDHZJP7rnnnlTp9ZMWVatWzdtW4W+iCbTKp2X16tXTlq98wXdL9/B7j6ZUK5FAEfnMGRg0CEqVco3UF9+jzROUrm3cuJHKlSsHOgxj0ryE/lZEZIWqXlG3znTZmH1xkF2VYnnjH/z9d6haFd55By52jxOxJJFBpLcvNsakNn/8jaS7RHEuwvVkqF0qf+xlTsPC3JiIO+6AXLncFOAjRgQoSuMPwcHBHDlyxJKFMYlQdetRxOwanBLSXRvF/uPh5AX63x6nCmL7dpg+HYYOhV69IHv2gMRn/CckJISwsDAOHToU6FCMSbMurnCXktJdojhzIZK8QN3SBWHpUli8GJ5/3q1bvXs3+HmEogmcbNmypeiqXcYY3/i16klE2ojIZhEJFZG+CRzPISITPcf/FpEyvly3dYkcrpH6xhvh/fdd4zVYkjDGGD/wW68nEQkCtgAtgTBgGfCwqm6Icc6zQA1V7SoiDwH3qOqDSV332vzX6Y6gKHIeP+pGVL/5JuRNoFHbGGOMV1rt9dQACFXV7ap6AZgA3BXnnLuAiyt5/Ai0kGQmsgk5cZDgcmVg2TLXWG1Jwhhj/MqfbRQlgD0xtsOAhomdo6qRInICKAQcjnmSiDwDPOPZPB+0fPk66tb1S9DpTGHi3KtMzO7FJXYvLrF7ccn1V/pEfyaKhEoGceu5fDkHVR0DjAEQkeVXWpNTl2cAAAeTSURBVHzKaOxeXGL34hK7F5fYvbhERJZf6XP9WfUUBsSc1jUE2JfYOSKSFcgHJD4dpjHGmFTnz0SxDKgoImVFJDvwEDA5zjmTgU6ex/cBc9RGUxljTJrit6onT5tDd2A6EAR8oarrReRN3CyGk4HPga9FJBRXknjIh0uP8VfM6ZDdi0vsXlxi9+ISuxeXXPG9SHeTAhpjjEld6W6uJ2OMManLEoUxxpgkpdlE4a/pP9IjH+5FLxHZICJrRWS2iJQORJypIbl7EeO8+0RERSTDdo305V6IyAOe3431IvJtaseYWnz4GyklInNFZJXn76RdIOL0NxH5QkQOisi6RI6LiIz03Ke1IlLHpwtf6dJ4/vzBNX5vA8oB2YE1QJU45zwLjPY8fgiYGOi4A3gvbgFyeh53y8z3wnNeHmABsASoF+i4A/h7URFYBRTwbBcNdNwBvBdjgG6ex1WAnYGO20/34magDrAukePtgD9wY9huBP725bpptUThl+k/0qlk74WqzlXVs57NJbgxKxmRL78XAIOAYUB4agaXyny5F08Do1T1GICqHkzlGFOLL/dCgYvz/eQj/piuDEFVF5D0WLS7gK/UWQLkF5FiyV03rSaKhKb/KJHYOaoaCVyc/iOj8eVexPQk7htDRpTsvRCR2kBJVf09NQMLAF9+LyoBlUTkLxFZIiJtUi261OXLvRgIPCoiYcBUoEfqhJbmXO7nCZB216NIsek/MgCf36eIPArUA5r5NaLASfJeiEgW4APgidQKKIB8+b3Iiqt+ao4rZf4pItVU9bifY0ttvtyLh4GxqvqeiDTCjd+qpqrR/g8vTbmiz820WqKw6T8u8eVeICK3Aa8Cd6rq+VSKLbUldy/yANWA/2/vfkOkqsI4jn9/hJUmCSJFErSFYSXpUhaSL8Is6Q9JibiFaRtJKEVo2YswqKAXkvUiM1tLQgMTU7SkP5iEWsiaSvinxDJUJJCSEAnbQLZfL87ZnLbZmbubf2Z3ng8M7Jw7956zB+Y+c597ec5mSYdJOdj1ffSGdtHvyMe2T9k+BPxAChx9TZG5eBz4EMB2K3AxqWBgvSl0PumsVgNFlP84repc5HTLElKQ6Kt5aKgyF7ZP2B5iu8F2A+l+zUTbPS6GVsOKfEc+Ij3ogKQhpFTUwXM6ynOjyFwcAcYDSLqeFCjqcU3d9cD0/PTTGOCE7aPVdqrJ1JPPXvmPXqfgXCwABgKr8/38I7YnnrdBnyUF56IuFJyLDcAESfuAduA527+dv1GfHQXn4lngXUlzSKmW5r74w1LSSlKqcUi+H/Mi0A/Adgvp/sy9wE/AH8BjhY7bB+cqhBDCGVSrqacQQgg1IgJFCCGEiiJQhBBCqCgCRQghhIoiUIQQQqgoAkWoOZLaJe0qeTVU+GxDV5Uyu9nn5lx9dHcueTG8B8eYKWl6/rtZ0tCSbUsl3XCGx7lDUmOBfWZLGvB/+w71KwJFqEVtthtLXofPUb9TbY8iFZtc0N2dbbfYfj+/bQaGlmybYXvfGRnl6XEuptg4ZwMRKEKPRaAIvUK+cvha0rf5dVuZz4yQtD1fheyRdG1uf6SkfYmkC6p09xUwLO87Pq9hsDfX+r8ot8/X6TVAXsttL0maK2kyqebWitxn/3wlMFrSLEmvloy5WdKbPRxnKyUF3SS9LWmn0toTL+e2p0kBa5OkTbltgqTWPI+rJQ2s0k+ocxEoQi3qX5J2WpfbfgXusn0T0AQsLLPfTOAN242kE/XPuVxDEzA2t7cDU6v0fz+wV9LFwDKgyfaNpEoGsyQNBh4ERtgeCbxSurPtNcBO0i//RtttJZvXAJNK3jcBq3o4zrtJZTo6zLM9GhgJ3C5ppO2FpFo+42yPy6U8XgDuzHO5E3imSj+hztVkCY9Q99ryybJUP2BRzsm3k+oWddYKzJN0JbDW9gFJ44GbgR25vEl/UtApZ4WkNuAwqQz1cOCQ7R/z9uXAk8Ai0loXSyV9ChQuaW77mKSDuc7OgdzH1nzc7ozzElK5itIVyqZIeoL0vb6CtEDPnk77jsntW3M/F5LmLYQuRaAIvcUc4BdgFOlK+D+LEtn+QNI3wH3ABkkzSGWVl9t+vkAfU0sLCEoqu75Jri10K6nI3EPAU8Ad3fhfVgFTgP3AOttWOmsXHidpFbf5wFvAJElXA3OBW2wfl7SMVPiuMwEbbT/cjfGGOhepp9BbDAKO5vUDppF+Tf+LpGuAgzndsp6UgvkSmCzpsvyZwSq+pvh+oEHSsPx+GrAl5/QH2f6MdKO43JNHv5PKnpezFniAtEbCqtzWrXHaPkVKIY3JaatLgZPACUmXA/d0MZZtwNiO/0nSAEnlrs5C+EcEitBbLAYelbSNlHY6WeYzTcB3knYB15GWfNxHOqF+IWkPsJGUlqnK9p+k6pqrJe0F/gJaSCfdT/LxtpCudjpbBrR03MzudNzjwD7gKtvbc1u3x5nvfbwOzLW9m7Q+9vfAe6R0Vod3gM8lbbJ9jPRE1srczzbSXIXQpageG0IIoaK4ogghhFBRBIoQQggVRaAIIYRQUQSKEEIIFUWgCCGEUFEEihBCCBVFoAghhFDR32dmQf+3Fq/tAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix,roc_curve, auc,roc_auc_score\n",
"roc_auc = roc_auc_score(y_valid, y_valid_preds_lr)\n",
"fp_rate, tp_rate, thresholds = roc_curve(y_valid, y_valid_preds_lr)\n",
"plt.figure()\n",
"plt.plot(fp_rate, tp_rate, label='Logistic Regression (area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1],'r--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC -Logistic Regression ')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[8883 4333]\n",
" [ 741 944]]\n"
]
}
],
"source": [
"print(confusion_matrix(y_valid, Y_lr))"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9,9))\n",
"sns.heatmap(confusion_matrix(y_valid, Y_lr), annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Greens_r');\n",
"plt.ylabel('Actual label');\n",
"plt.xlabel('Predicted label');\n",
"all_sample_title = 'Accuracy Score: {0}'.format(lr.score(X_valid_tf , y_valid))\n",
"plt.title(all_sample_title, size = 15);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" When you have a lot of data logistic regression may take a long time to compute. There is an alternative approach called stochastic gradient descent that works similarly to logistic regression but doesn't use all the data at each iteration."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stochastic Gradient Descent Classifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Y_sgdc=sgdc.predict(X_valid_tf)"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stochastic Gradient Descend\n",
"Training:\n",
"AUC:0.679\n",
"accuracy:0.627\n",
"recall:0.561\n",
"precision:0.646\n",
"specificity:0.693\n",
"prevalence:0.500\n",
" \n",
"Validation:\n",
"AUC:0.662\n",
"accuracy:0.661\n",
"recall:0.558\n",
"precision:0.179\n",
"specificity:0.674\n",
"prevalence:0.113\n",
" \n"
]
}
],
"source": [
"y_train_preds = sgdc.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_sgdc = sgdc.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Stochastic Gradient Descend')\n",
"print('Training:')\n",
"sgdc_train_auc, sgdc_train_accuracy, sgdc_train_recall, sgdc_train_precision, sgdc_train_specificity =print_report(y_train,y_train_preds, thresh)\n",
"print('Validation:')\n",
"sgdc_valid_auc, sgdc_valid_accuracy, sgdc_valid_recall, sgdc_valid_precision, sgdc_valid_specificity = print_report(y_valid,y_valid_preds_sgdc, thresh)"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[8908 4308]\n",
" [ 745 940]]\n"
]
}
],
"source": [
"print(confusion_matrix(y_valid, Y_sgdc))"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x648 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9,9))\n",
"sns.heatmap(confusion_matrix(y_valid, Y_sgdc), annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Greens_r');\n",
"plt.ylabel('Actual label');\n",
"plt.xlabel('Predicted label');\n",
"all_sample_title = 'Accuracy Score: {0}'.format(sgdc.score(X_valid_tf , y_valid))\n",
"plt.title(all_sample_title, size = 15);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Naive Bayes"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GaussianNB(priors=None)"
]
},
"execution_count": 228,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.naive_bayes import GaussianNB\n",
"\n",
"nb = GaussianNB()\n",
"nb.fit(X_train_tf, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Y_nb=nb.predict(X_valid_tf)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Naive Bayes\n",
"Training:\n",
"AUC:0.513\n",
"accuracy:0.511\n",
"recall:0.987\n",
"precision:0.506\n",
"specificity:0.035\n",
"prevalence:0.500\n",
" \n",
"Validation:\n",
"AUC:0.509\n",
"accuracy:0.139\n",
"recall:0.982\n",
"precision:0.114\n",
"specificity:0.032\n",
"prevalence:0.113\n",
" \n"
]
}
],
"source": [
"y_train_preds = nb.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_nb = nb.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Naive Bayes')\n",
"print('Training:')\n",
"nb_train_auc, nb_train_accuracy, nb_train_recall, nb_train_precision, nb_train_specificity =print_report(y_train,y_train_preds, thresh)\n",
"print('Validation:')\n",
"nb_valid_auc, nb_valid_accuracy, nb_valid_recall, nb_valid_precision, nb_valid_specificity = print_report(y_valid,y_valid_preds_nb, thresh)\n"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix,roc_curve, auc,roc_auc_score\n",
"roc_auc = roc_auc_score(y_valid, y_valid_preds_nb)\n",
"fp_rate, tp_rate, thresholds = roc_curve(y_valid, y_valid_preds_nb)\n",
"plt.figure()\n",
"plt.plot(fp_rate, tp_rate, label='Naive Bayes (area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1],'r--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC - Naive Bayes')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 424 12792]\n",
" [ 31 1654]]\n"
]
}
],
"source": [
"print(confusion_matrix(y_valid, Y_nb))"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9,9))\n",
"sns.heatmap(confusion_matrix(y_valid, Y_nb), annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Greens_r');\n",
"plt.ylabel('Actual label');\n",
"plt.xlabel('Predicted label');\n",
"all_sample_title = 'Accuracy Score: {0}'.format(nb.score(X_valid_tf , y_valid))\n",
"plt.title(all_sample_title, size = 15);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decision Tree"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=10,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
" splitter='best')"
]
},
"execution_count": 238,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"tree = DecisionTreeClassifier(max_depth = 10, random_state = 42)\n",
"tree.fit(X_train_tf, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {},
"outputs": [],
"source": [
"Y_tree=tree.predict(X_valid_tf)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision Tree\n",
"Training:\n",
"AUC:0.734\n",
"accuracy:0.669\n",
"recall:0.603\n",
"precision:0.695\n",
"specificity:0.733\n",
"prevalence:0.500\n",
" \n",
"Validation:\n",
"AUC:0.628\n",
"accuracy:0.657\n",
"recall:0.552\n",
"precision:0.176\n",
"specificity:0.667\n",
"prevalence:0.113\n",
" \n"
]
}
],
"source": [
"y_train_preds = tree.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_tree = tree.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Decision Tree')\n",
"print('Training:')\n",
"tree_train_auc, tree_train_accuracy, tree_train_recall, tree_train_precision, tree_train_specificity =print_report(y_train,y_train_preds, thresh)\n",
"print('Validation:')\n",
"tree_valid_auc, tree_valid_accuracy, tree_valid_recall, tree_valid_precision, tree_valid_specificity = print_report(y_valid,y_valid_preds_tree, thresh)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix,roc_curve, auc,roc_auc_score\n",
"roc_auc = roc_auc_score(y_valid, y_valid_preds_tree)\n",
"fp_rate, tp_rate, thresholds = roc_curve(y_valid, y_valid_preds_tree)\n",
"plt.figure()\n",
"plt.plot(fp_rate, tp_rate, label='Decision Tree (area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1],'r--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC - Decision Trees')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[8855 4361]\n",
" [ 755 930]]\n"
]
}
],
"source": [
"print(confusion_matrix(y_valid, Y_tree))"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAH+CAYAAADNpjfmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XecXFX9//HXJyEV0iEJJJGW0EFQpKv0UKQJaECkWKIo1p+KKF9BilJFEUWCghQFEUEjIIgUAQVCL6FIqAmkQHpIz57fH/duGJad3bkXNrthXs887mMz556599zZ2Zkz73PunUgpIUmSVESn9m6AJEla+diBkCRJhdmBkCRJhdmBkCRJhdmBkCRJhdmBkCRJhdmBkCRJhdmBkCRJhdmBkCRJha3S3g2QJGlltXApbX455+6rEG29jzJMICRJUmEmEJIklVTPXydlAiFJkgozgZAkqaTU9lMgoGNOgTCBkCRJxZlASJJUlnMgJEmSamcCIUlSSXUcQJhASJKk4kwgJEkqyetASJIkFWACIUlSSV4HQpIkqQATCEmSynIOhCRJUu1MICRJKqmOAwgTCEmSVJwJhCRJJXkdCEmSpAJMICRJKmnFXAeiYzKBkCRJhZlASJJUknMgJEmSCrADIUmSCnMIQ5KkkhzCULuJzIsRkSJieHu3p6OJiNUj4oKIeCEiFkbEaxFxS0Qc2N5tKysidoyI+yNiQf67/3qB+46OiCfzx2JqRPypyfo78+dS06V70W3lddaOiKsiYkZEzI+IxyJiryLbioh1qrQpRcSzFfU+HhF3RMS0iFiU/87PjYjeTfb1m4h4JiLmRcTMiLgrInZvUuf3LezzsCLtqvEYe0fEjyNiXETMjogpEXF9RGzQZBvV9nl1M/sbEBEX5dtakB/zkc3U+2REPJDXmR4RN0fEqvm6zhFxfETcna+bHhH/jIiPNN2OVJQJRPvbHlgn//8o4LT2a0rHEhFdgDuAnsDpwPPAUGBPYDfgr+3XunLyTuItwA3ACcA2wM8iYn5K6bet3Pc04Diy58gDwCDg481UvQP4QZOyRUW3FRHDgHuBx4BjgDeBLYEeBbc1mex5XqkH8E/gHxVl/YFHgF8DrwObAj8GNgQ+0eS+FwDPAl2BzwP/iIiPppTuy+ucCvymyT6PBQ4H/lWwXbUc4weALwK/A35I9pw9Abg/IrZIKU1ssp/vAP+puP1Gk/31Bu4C5gFfy9dvkh9vZb0v5I/FWcB3gX7Arrz12t4D+D5wKfBTsisvHwfcExE7pJQeQu9KPZ/GGame85cOICJ+Sfbi/CTQK6W0aTs3abmI6J5SWtiO+9+D7MV8m5TSA03WRWrjJ29E9EgpLXiPt3kRsAuwSUppaV72a2A/4APVjikiNgUeB/ZKKd3awvbvBN5IKR3SQp1at3U1MAT4eEqp4d1sq5n7HQpcA2yXUrq/hXpfBMYAA1JKM6rU6Qy8CPw1pVQ1zYmI8cArKaW9i7SrlmPMP/E3VD5fIqI/8Apwdkrpx3nZOnlb90sp3dBCO84ADgE2r/YcjIjV8219O6V0cZU6nYHeKaWZFWVdgf8Bd6SUjqnWBtVm+ptL2/xNdMCqq0Rb76MMhzDaUf7HfSgwFrgE2CQitmimXmOM/EYeIz8eEYdXrO8REWdFxMt59PtiRPy0Yn2KiOOabPPkiHij4vbReb1tIovBF5B9oiEizoiIJ/LIeFJE/CEiBjfTzi/m9Roj3msjok9E7BsRDRGxbpP66+bl+1d5iPrmP6c0XdH0jTYitoiIv0fErLyd4/IOSOW+/hoRcyJibl53eJNtpIj4dkT8PCJeB56oWHdARDyYH9uU/PHuUqXdLdkbuK6x85C7mixZ2ayF+x0FPF/kTfrdbCsi+gCfBH5drfPwLtt1GPBiS52H3PT8Z9dqFVJKy4BZLdXJ/642Aa4q0a5WjzGl9GbTN/q8w/MyMLCVfTbnGOB3rXRgP5X/vKyFdi2r7DzkZYuB8SXbpSZSavulo7ID0b52JYtCrwauBZaQvYAtFxEDyWLkj5DFnvuRxaTD8vUB/I0snv0VsA9wErB6yTZdRRav75P/hOyF5ifAvsA3gfWA2/MOUGM7TwQuAv4NHJi3ZzawGnAz8BrZC3Glo8mi6puqtOVRoAG4JCJ2iohmh9wiYiOyOHhN4MvAQcD1vPUYdQNuAzYmi5mPBtYF/p1/Sqz03Xw7nwW+nt//U8B1wDhgf7JYfTRZJNzYhsax7aOrHEvjp9RhwDNNVj2d/9yo2n2BbYEnGjt+eUfxXxGxcTN198w7mvMjmy/StFNay7Y+BHQBUkT8JyKW5J3HE/LnXJl2NT4Ovck6Us2+mUc2bt8tIrYETiTrcE1pUiciYpXI5gl8CxhB1gmvZhSwkBaGvVpoV+FjzLe3BjAceKqZ1ZdGxLKImBwRP4uIHhX3W5fsb25WRNwUEYsj4vW8XmUnaVuyYZzP57+bJZHNrdmhlXZ1Az5cpV1S7VJKLu20kL3gzQS65rdvJIsko6LOT8nGntesso2RZOOa+7ewnwQc16TsZLKou/H20Xm9b7TS5s5ksXYCPpaX9QXmAz9r4X6nVR4bEMBLwDmt7O/bwOJ8fwvIOiOHNqlzFTAJ6FFlG18GlgLrVZQNzbd7QpPH6ZEm9w2yT5GXNin/XN6eAfnttfN9HNnCsTQ+bgc2KV8lLx/dwn2fBeaSvegfRNaRfCxvW/eKej8m+/T6UeAIss7JbGCdItsi68im/L5nkA27nAIsA75StF1NjuXIfNubV1n/TL4+5b/vns3UGVVRZx4tPP/z+s8Df2mlTrPtKnOM+f0uJ0tQBlSUrUk2Z2F/YGeyv8MFwN8q6myft2MucDHZB41v5fXOqqh3S17nVeAzwF7A7cAcYFAL7TqFbE7Mhi09Hi61LdPmLkltvbT3MVZ9LrV3A+p1AbqRdR4uqSg7In/h2L6i7P6WXviAM4HpreyrSAdieDP33xv4b/5mkiqWL1Ssr/qGkNdZjyxN2CW/vWt+n01reKyGAF8hG5uemd/vpxXrpwLntnD/S4BxzZTfAdzY5HE6rUmdDfPyvcne6BuXdfLyjxf4nTd2IA5oUt7YgfhiC/d9Ln/8Nm7ymC4FPt/C/QaTxfs/L7Kt/A0pAVc381hOfDftIpug+GQLbd4U2AH4Etmb441UdKrzOv2ArcneNK8g68DuXGV72+bHckgrv59m21XyGI/N73NQDc+LY/P2bZnf3jG/fV+Tej8iS1F65rdvzevtVVGnd/43cmqVfe1L1gn8Zq3PW5eWl3ruQDiE0X72JvvkflNE9I2IvsCdZJ8MKocxBpDNFq+mtfVFTa28EdnpXmPJPuF/luzT0Xb56sZTAwfkP6u2I6X0AtnxNU7aOobsTX18aw1KKb2aUvp1SulTZMnBzcB3I6Jxv609Bms2Pa7cVLKZ/03LKjUOBd1ENsTUuLyYlw9rrf0VZuU/+zYp79dkfXNmAFNTSo3DHY2P6UtkY/vNSln0/x+yIYki22qcsHhHk03eDgyNt06tLNSu/He2Oy3MRUgpjU8p/TeldBHZ38I+ZAlIZZ2ZKaUHU0o3p5Q+SzbMd0qVTY4i+6R+Y7V9ttKuose4P/BL4PiU0vXV9lnh2vxn4++opce+G7B+k3p3VrRrDvBQlXZ9BPgTcFFK6ec1tEu1SCtg6aDsQLSfxk7Cn8k+McwEJpK9QHyqYn7BdLI3wGpaWw9Zp6TpBLOmb5yNmj5dDyKbp/DplNLYlJ0m13RSY+NEt9ba8Vvg4IgYQjZB79JW6r+zcSm9SXaaX2ey8eXG/be078k0P2FsEG+9CC/fRZPbjetHk81Dabr8gxrlbZ/IO+c6NN5uOjei0tNVyoPsk26ruy+4rZbqUGO95tp1CFni8o7rHlTxcP5zvVbqPdJcnYjoRDZR+a+p5QmJLbWr5mPM5x9cDfwmpXR2K21ulJr8fJ5seK25/cHbH/tUUd5SuzYg60DdRnZaqPSu2YFoBxGxGtl57VeRfbKqXL5N9sbW+InrNmBkRAyqsrnbgP4R8Ykq6yFLD5ZP+MpfVHetsbk9gCxDe8tnmtS5l2x89qhWtnUd2Qvj1WTPvRbfRCKif5WJkyPyn9Pyn7eRdbrecbGk3P3AhyvPAsk7MTsA97TS5mfJYvR18k+8TZfprdy/qX8AB1VOQAU+TdaxeLKF+90ADI6I5Z8sI2J9srkXj1W7U/682ZHsU2nN20opvUQ2U3+3JpvcjeyMhHkl23UYWfL0fAvHWmnH/OeL1Srkkzq3r1LnY2RDR7WcfVGtXTUdY2Sne95AlpDVfHEwss4L5L+jlJ0lcSvv/BvdjWyoZkJFu4KKdCY/e+bDTdq1Jtl8ieeBw1J21oreI2kF/Ouw2nsMpR4X3hpf3raZdV3ILhrzu/z2GmQdgP+RvUHvCnwV+F6+PshesOaQnaWxa779iyq2eTbZC89XyMaMryU7P725ORCrNWnPPnn5z8lewP6P7E31bfMqyC6a0wCcn+/jALLz94c02d4F+X3/WMPjdAjZ+PMPgD0q9v8m8PeKehvmxz+O7M14d7KzKT6Xr+8GvED2Cf9TwMFkp2i+CvSv2M475ork5Z8m6/j8Mn88didLJG7irfHotWllEmVebzjZpL8/kr3wf49sSOQLTeotBX5Ucbsz2RvMM/njclB+DE8BXfI6W5B9yjw63/ZRef0ZZNeYqHlbeb2D8t/p2fnjfzrZ+Plnim4rr7sWLYy/k81l+D+ySYq75b/D18nm33TK63yU7EyKI8kmIR5MNsS2DNinmW3+huzvaZUWfiettauWx34gWSfwlbxd21Usm1Rs62TgXLIEbneyYZcFNJnnRHaBscVkKd2eZH/bC4EfNqn3V7KE7Siy+Q3/zh+zfvn6HmRnM83K11e2a6v2fi18PyxT5yxObb209zFWW9q9AfW4kH1y+F8L639NNqTRLb+9NtnY5UyyjsBjwKiK+j2Ac8g6GovIPomdXrF+NbJzxWeQDT+cSPVJlKs1057v5S+Ob5JdxW8EzU/M/FL+oroo3881ZBexqayze37f3Wt4nIblx9X4Ajg3f+E+gSYz88nePG/K68wlSx12q1i/Xv5iO5fsDfwGYESTbTTbgcjX7Q3cnT8Gc/I2nUb+xsRbkyqPruG4diLr7CwkG0f/ejN1EnByk7JBZJ+kZ+fHcT1v7xgMyR+DyWRvPtOBvwAbNbP9FrdVUa/xTI7FZJ98v/wutvVNsjfqtao8Ll8je6Oenf+OniDrUKxWUWcdsg5w43N9Uv673L6Z7a1C9mb6m1Z+Hy22q8bHfmeqj2DfWVFvFPBgvp3Gx/QU8r/1JvscSTaEs4js7+//yDtSTf62L8x/1wvI/j43b/J4VWvXS2Vev1zevkyZvTi19dLex1ht8UqUWqEi4iyyT/TrppYvUCRJHd7UOUva/E10UO8uHfJKlH4XhlaIiNiQbGb4scCP7TxIej+o54/gdiC0olxEdj7+WLJ5EpKklZgdCK0QKaWd27sNkvSeq+MIwtM4JUlSYSYQkiSVlOo4gujIHYj6/a1Ikt4LHfLshfeLjtyBYOHS9m6B1DF1z/9yD7v80fZtiNRBXXXklitkP/V8JYQO3YGQJKkjq+P+g5MoJUlScSYQkiSVVM9DGCYQkiSpMBMISZJKq98IwgRCkiQVZgIhSVJJzoGQJEkqwARCkqSS6jiAMIGQJEnFmUBIklSScyAkSZIKMIGQJKmkev46bxMISZJUmAmEJEll1W8AYQIhSZKKM4GQJKmkOg4gTCAkSVJxJhCSJJXkdSAkSZIKMIGQJKmker4OhB0ISZLKqt/+g0MYkiSpOBMISZJKquMAwgRCkiQVZwIhSVJJnsYpSZJUgAmEJEkl1fNpnCYQkiSpMBMISZLKqt8AwgRCkiQVZwIhSVJJdRxAmEBIkqTi7EBIklRSSm2/tCQiNoyIRyuWORHxzYg4OSJerSjfp+I+J0TEhIh4NiJGVpTvlZdNiIjvt3bsDmFIkrSSSik9C2wJEBGdgVeB64FjgPNSSudU1o+ITYBRwKbAWsC/ImKDfPWvgD2AScADETE2pfRUtX3bgZAkqaQOdh2I3YDnU0ovR0S1OgcAV6eUFgEvRsQEYJt83YSU0gsAEXF1XrdqB8IhDEmSOrCIGB0RD1Yso6tUHQVcVXH7uIh4PCIuiYh+edkQYGJFnUl5WbXyquxASJJUVmr7JaU0JqW0dcUypmkzIqIrsD/w57zoQmB9suGNycC5jVWrHEW18qocwpAkaeW3N/BwSmkqQONPgIi4GLghvzkJGFZxv6HAa/n/q5U3ywRCkqSSVkAAUavDqBi+iIg1K9YdBDyZ/38sMCoiukXEusAIYBzwADAiItbN04xRed2qTCAkSVqJRURPsrMnvlRRfFZEbEnWB3mpcV1KaXxEXEM2OXIp8NWU0rJ8O8cBtwCdgUtSSuNb2q8dCEmSSmrtOg0rpg1pPjCgSdlnW6h/OnB6M+U3ATfVul87EJIkldTBTuNcoZwDIUmSCjOBkCSprPoNIEwgJElScSYQkiSVVMcBhAmEJEkqzgRCkqSSOsJpnO3FBEKSJBVmAiFJUkleB0KSJKkAEwhJksqq3wDCBEKSJBVnAiFJUkl1HECYQEiSpOJMICRJKsnrQEiSJBVgAiFJUkleB0KSJKkAEwhJksqq3wDCBEKSJBVnAiFJUkl1HEDYgZAkqSxP45QkSSrABEKSpJI8jVOSJKkAEwhJksqq3wDCBEKSJBVnAiFJUkl1HECYQEiSpOJMICRJKsnrQEiSJBVgAiFJUkleB0KSJKkAEwhJksqq3wDCBEKSJBVnAiFJUkl1HECYQEiSpOJMICRJKqmhji8EYQIhSZIKM4GQJKmk+s0fTCAkSVIJJhCSJJVUx1MgTCAkSVJxJhCSJJVUz9+FYQdCkqSSGuq3/+AQhiRJKs4EQpKkkup5CMMEQpIkFWYCIUlSSZ7GKUmSVIAJhCRJJTkHQpIkqQATiPehKy77Pdf95c9EBCNGbMApp/+URx95mJ+dcxapoYEePXty6uln8IG11+Zv11/HeeeexcCBgwAYdfgRfPKQQwHYavONGTFiAwAGr7km5//qN+/Y1+LFi/nhCd/j6fHj6dO3L2edex5DhgwF4HcXX8T1f7mWTp07cfwJJ7LjTh8F4D9338WZZ5xOw7IGDjr4UD7/xdEr4mGRlouAn+y7ATPmL+Hs219k9PbDWG9ATyJg8pxFXPifV1i0tAGA7dbuy8EfHAwkXp65kAvufhmA7++2HsPXWJVnp83j7NtfbHY/q3QKvrLTB1i3f0/mLVrKL+56mTfeXAzAAZsNZOfhA2hIicseeJXHX5sLwAfX6sWRHxlCpwjumDCdsU9Oa/sHRKXV83Ug7EC8z0ydOpU//uFyrh97E927d+e73/4GN990I7+9+CJ+8ctfs9766/Onq/7AxRddyKk/OQOAPffahx+c+KN3bKtbt+5cc93fWtzf9X/5M7179+aGm2/lHzfdyM9/dg5nn/tznp8wgZtvupHrxt7ItGlT+dIXjmHsjbcA8JPTT+Giiy9l0KBBHP7pQ9h5l11Zf/jw9/7BkKrYe6M1eHX2Inp0yULYKx58lQVLsg7DEVuvxciNVmfsk9MY3KsrB2w+kJNvfo43Fy+jd/e3XjL/Pn4a3VbpxG4bDKi6n11G9OfNRcv41l+fZvt1+nL4h9fk/LteZkifbmy/Tj++O/YZ+vXswg/3WJ9v/fVpAI7Zdig/ufV5ps9fwun7bMBDE2fz6uxFbfhoSOW02RBGRGwUEcdHxPkR8Yv8/xu31f70lmXLlrFo4UKWLl3KgoULWWPgQCJg3pvzAJg3bx5rDBz4nuzrjttvZ/8DDgJgjz1HMu6+e0kpcecdt7HXPvvStWtXhg4dxrBha/PkE4/z5BOPM2zY2gwdNowuXbuy1z77cucdt70nbZFq0b9nF7Ya2ps7npu+vKyx8wDQtXOn5TPrdx0xgH8+8wZvLl4GwJyFS5fXGz9l3tvu15wPD+vDXc/PAOD+l2ex2eBeAGw9rA/3vjSTpQ2J1+ctZsrcRQwf0JPhA3oyZe4ips1bzLKGxL0vzWTrYX3ek+NW20gr4F9H1SYJREQcDxwGXA2My4uHAldFxNUppTPaYr+CQYMGcdTRn2Pk7rvQvXs3tt9hR3bYcSdOPuV0jvvyaLp178Zqq67GFVdds/w+t936Tx5+6AHWXntdvnv8CQxec00AFi9exGGf+iSdO6/C574wml132/0d+5s2bSqDB2f1V1llFVbr1YtZs2YydepUtvjgB99q1+BBTJs6FYDBaw5eXj5w0CCeePzxNnkspOYc+ZEh/PGh1+jepfPbyr+0wzC2GtKbSbMXcuWDrwIwuHd3AE7eazidIvjLY1N4LB9qqEX/Hl2YPn8JkEXd85cso1e3zvTr2YUJr89fXm/Gm0vo17MLANPfXLK8fPr8JQxfvWe5A5XaWFslEJ8HPpJSOiOldGW+nAFsk69rVkSMjogHI+LBMWPGtFHT3t/mzJ7NHbffxk3/vI1b77ibBQsWcMPf/8YVl/+eC34zhltvv4sDDvok55z1UwA+vssu/OPW27n2+r+z7fbbc+IPjl++rZv/dQdXXXMdZ5x1Lmef8RMmvvLKO/aXmjkJOiKaPTk6IprtTUfEuzlkqWZbDenNnIVLeXHGgnesu+i/Ezn22vG8NnsR26/TD4DOnWBw726cessEfnn3y3xx+2H0bNLxaElzT+0EVHvG+6ew8kmp7ZeOqq06EA3AWs2Ur5mva1ZKaUxKaeuU0tajRzuxroz77vsvQ4YOpX///nTp0oXddt+TRx95mP89+wxbbJElAiP32ofHHnkEgL59+9G1a1cADj7kUzz91Pjl22qcWDl02DC2/sg2PPP0U+/Y36BBg5kyZTIAS5cuZd7cufTp05dBgwczdcqU5fWmTpnKGgMHZvUnv1U+bepUBr5HwylSazYcuCofGtqb8z+5CV//2NpsOrgXX93pA8vXpwT3vjSTbdbOhg1mvLmEByfOYVmC1+ctZvKcRQzu3bXm/U2fv4QBebLQKaBnl87MW7SMGfOXMGDVLsvr9V+1CzPnL2HGm28vH9AzK5c6orbqQHwTuC0i/hERY/LlZuA24BtttE8Bg9dci8cfe4wFCxaQUuL+++5lvfWHM2/uXF56KZspfu+9/2Hd9dYH4PXX35rhfecdty8vnzN7NosXZ7PFZ86cwaOPPMx6679zouPOu+zK2L9dD8Ct/7yFbbbdjojg47vsys033cjixYuZNGkir7zyEpttvgWbbrY5r7zyEpMmTWTJ4sXcfNONfHyXXdv0MZEaXf3IZI77y1N8/bqnOP+ulxk/ZS6/uucVBvV6q1PwoaF9eC2ftPjgxNlsOng1AHp168yavbsxbd7imvf30MQ5fGz9/gBsu3Zfxk+Zu7x8+3X6sUqnYI3VujK4VzcmTJ/P89PnM7hXN9ZYrSudOwXbr9OPhybOea8OX22gnhOINpkDkVK6OSI2IBuyGEKW2E0CHkgpLWuLfSqzxRYfZI89RzLq0IPo3HkVNtp4Yw459NMMGjSY//fNr9Mpgt59+vDjU38CwB+vvII777idVTp3pnefPpx6eja08cILz3Pqj0+iUwQNKXHMF764/EyJX/3yF2y66WbsvOtuHHTwIfzw+9/lE3vtQe8+fTjrnPMAGD58BHvutTcH7b8PnTt35gcn/ojOnbPo94Qf/ohjR3+BhoZlHHjQwQwfPqIdHikpE8CxO36AHl06E8DLMxdwyf2TAHjstblsvlYvzt5/IxpS4g8Pvca8RdlL2Ekjh7NWn+50X6UTFxy8CWPuncjjr83lkA8O5sXp83lo0hzufG46X9lpbc47cGPmLV7KL+/KTgGdNHsh9708i3MO2IhlDYlL75+UvVkAvx83iRN2X49OEdw5YQaTZi9snwdGakU0N4bdQaSKCc+SKjSeTXjY5Y+2b0OkDuqqI7eE6tNN3jM3jZ/W5m+i+2w6sEPOjvFKlJIkqTAvJCVJUkkdN8RveyYQkiSpMBMISZJK6shXimxrdiAkSSrJIQxJkqQCTCAkSSqpoY6HMEwgJElSYSYQkiSV5BwISZKkAkwgJEkqqY4DCBMISZJUnAmEJEkldeAvpGxzJhCSJKkwEwhJkkpqaO8GtCMTCEmSVJgJhCRJJTkHQpIkqQATCEmSSqrf/MEEQpIklWAHQpKkklJKbb60JiL6RsS1EfFMRDwdEdtHRP+IuDUinst/9svrRkScHxETIuLxiPhQxXaOyus/FxFHtbZfOxCSJK3cfgHcnFLaCPgg8DTwfeC2lNII4Lb8NsDewIh8GQ1cCBAR/YGTgG2BbYCTGjsd1diBkCSppIYVsLQkInoDHwN+B5BSWpxSmgUcAFyWV7sMODD//wHA5SlzH9A3ItYERgK3ppRmpJRmArcCe7W0bzsQkiStvNYDXgcujYhHIuK3EbEqMCilNBkg/zkwrz8EmFhx/0l5WbXyquxASJJU0oqYAxERoyPiwYpldEUTVgE+BFyYUtoKeJO3hiuaE80dRgvlVXkapyRJJa2I60illMYAY6qsngRMSindn9++lqwDMTUi1kwpTc6HKKZV1B9Wcf+hwGt5+c5Nyu9sqV0mEJIkraRSSlOAiRGxYV60G/AUMBZoPJPiKOBv+f/HAkfmZ2NsB8zOhzhuAfaMiH755Mk987KqTCAkSSqpg1xI6mvAHyKiK/ACcAxZQHBNRHweeAU4NK97E7APMAGYn9clpTQjIk4FHsjrnZJSmtHSTu1ASJK0EkspPQps3cyq3Zqpm4CvVtnOJcAlte7XDoQkSSU1+GVakiRJtTOBkCSppPrNH0wgJElSCSYQkiSVVMuXXb1fmUBIkqTCTCAkSSqptS+7ej8zgZAkSYWZQEiSVFIdT4EwgZAkScWZQEiSVJJXopQkSSrABEKSpJLqOIAwgZAkScWZQEiSVJJzICRJkgowgZAkqaSG+g0g7EBIklRWHY9gOIQhSZKKM4GQJKmkBuo3gjCBkCRJhZlASJJUknMgJEmSCjCBkCSppHo+jdMEQpIkFWYCIUlSSV7KWpIkqQATCEkhCkzoAAAZhklEQVSSSqrjAMIEQpIkFWcCIUlSSZ6FIUmSVIAJhCRJJaU6ngRhAiFJkgozgZAkqSTnQEiSJBVgAiFJUkkmEJIkSQWYQEiSVFKifiMIEwhJklRY1QQiIvq3dMeU0oz3vjmSJK086nkOREtDGA8BCYhm1iVgvTZpkSRJK4k6vo5U9Q5ESmndFdkQSZK08mh1EmVEBPAZYN2U0qkR8QFgcEppXJu3TpKkDqyhjiOIWiZR/hrYHjg8vz0X+FWbtUiSJHV4tZzGuW1K6UMR8QhASmlmRHRt43ZJktTh1fMkyloSiCUR0Zls4iQRsQbQ0KatkiRJHVotCcT5wPXAoIg4HTgEOLFNWyVJ0kqgjqdAtN6BSCn9ISIeAnbLiw5MKT3dts2SJEkdWa2Xsu4JNA5j9Gi75kiStPLwLIwWRMSPgMuA/sDqwKUR4RCGJEl1rJYE4jBgq5TSQoCIOAN4GDitLRsmSVJHV8cBRE1nYbwEdK+43Q14vk1aI0mSVgotfZnWL8nmPCwCxkfErfntPYB7VkzzJEnquOr5mgYtDWE8mP98iOw0zkZ3tllrJEnSSqGlL9O6bEU2RJKklU09n4VRy5dpjQB+CmxCxVyIlJJf5y1JUp2q5SyMS4GTgPOAXYBjgGjLRkmStDKo4wCiprMweqSUbgMipfRySulkYNe2bZYkSerIakkgFkZEJ+C5iDgOeBUY2LbNkiSp4/PbOFv2TbJLWX8d+DDwWeCotmyUJEnq2Gr5Mq0H8v/OI5v/IEmSgFTHkyBaupDU38kuHNWslNL+bdIiSZJWEvU8hNFSAnHOCmuFJElaqbR0Ial/r8iGSJK0sqnnBKKWSZSSJElvU8tpnJIkqRlOouygunfo1knt76ojt2zvJkiqUx36LIweWx3X1ruQVkoLHrkAgB7bfKedWyJ1TAvGrZjzAPw67+Z5FoYkSWqWZ2FIklSScyBa4Nd5S5Kkpvw6b0mSSqrjAMKv85YkScX5dd6SJJXUUMcRhF/nLUmSCvPrvCVJKqmOA4iazsK4g2YuKJVSch6EJEl1qpY5EJWXuusOHAwsbZvmSJK08vA6EC1IKT3UpOg/EeFFpiRJqmO1DGH0r7jZiWwi5eA2a5EkSSuJOg4gahrCeIhsDkSQDV28CHy+LRslSZI6tlo6EBunlBZWFkREtzZqjyRJKw2vA9Gy/zZTdu973RBJklY2aQUstYiIzhHxSETckN/+fUS8GBGP5suWeXlExPkRMSEiHo+ID1Vs46iIeC5fWr3eU9UEIiIGA0OAHhGxFW99/0VvsgtLSZKkjuEbwNNk79GNvptSurZJvb2BEfmyLXAhsG0+3/EkYGuyfstDETE2pTSz2g5bGsIYCRwNDAXO5a0OxBzgBzUekCRJ71sd4TTOiBgK7AucDny7leoHAJenrOH3RUTfiFgT2Bm4NaU0I9/mrcBewFXVNlS1A5FSugy4LCIOTin9pcjBSJKk90ZEjAZGVxSNSSmNqbj9c+B7QK8mdz09In4E3AZ8P6W0iGxkYWJFnUl5WbXyqmqZA/HhiOhbcSD9IuK0Gu4nSdL7WkNq+yWlNCaltHXFsrzzEBGfAKY1c82mE4CNgI8A/YHjG+/SzGGkFsqrqqUDsXdKadbyrWXjIfvUcD9JktS2dgT2j4iXgKuBXSPiypTS5JRZBFwKbJPXnwQMq7j/UOC1FsqrqqUD0bnytM2I6AF4Gqckqe6llNp8aWX/J6SUhqaU1gFGAbenlI7I5zUQEQEcCDyZ32UscGR+NsZ2wOyU0mTgFmDPfJShH7BnXlZVLdeBuBK4LSIuJYszPgdcXsP9JElS+/hDRKxBNjTxKPDlvPwmslGECcB88m/ZTinNiIhTgcZv4D6lcUJlNbV8F8ZZEfE4sHvekFNTSi32SiRJqgcd4CSM5VJKdwJ35v9v9huz87Mvvlpl3SXAJbXur5YEgpTSzcDNABGxY0T8KqXUbAMkSdL7X00diPwKVocBnyb7Lozr2rJRkiStDDrCdSDaS0tXotyAbELGYcB04E9ApJR2WUFtkyRJHVRLCcQzwN3AfimlCQAR8a0V0ipJklYCDfUbQLR4GufBwBTgjoi4OCJ2o/kLTUiSpDrT0qWsrweuj4hVyc4h/RYwKCIuBK5PKf1zBbVRkqQOqZ7nQLR6IamU0psppT+klD5BdmWqR4Hvt3nLJElSh1XLlSiXSynNSCldVO38UkmS6klaAUtHVagDIUmSBDVeB0KSJL1Tg3MgJEmSamcCIUlSSXUcQNiBkCSpLE/jlCRJKsAEQpKkkuo4gDCBkCRJxZlASJJUkqdxSpIkFWACIUlSSXUcQJhASJKk4kwgJEkqyetASJIkFWACIUlSSQ31G0CYQEiSpOJMICRJKilRvxGECYQkSSrMBEKSpJLq+CQMEwhJklScCYQkSSV5HQhJkqQCTCAkSSrJ60BIkiQVYAIhSVJJzoGQJEkqwARCkqSS6jiAsAMhSVJZDXXcg3AIQ5IkFWYCIUlSSXUcQJhASJKk4kwgJEkqydM4JUmSCjCBkCSppDoOIEwgJElScSYQkiSV5BwISZKkAkwgJEkqqY4DCBMISZJUnAmEJEklOQdCkiSpABMISZJKMoGQJEkqwARCkqSS6jiAMIGQJEnFmUBIklSScyAkSZIKMIGQJKmkOg4g7EBIklSWQxiSJEkFmEBIklRSHQcQJhCSJKk4EwhJkkpyDoQkSVIBJhCSJJVUxwGECYQkSSrOBEKSpJKcAyFJklSACYQkSSXVcQBhAiFJkoozgZAkqSTnQEiSJBVgAiFJUkl1HECYQEiSpOJMIN7HRqw9kCvO/Nzy2+sOGcCpF95In149+dwnd+D1mfMAOOmCsdxyz1N8YM3+PHrdifzv5WkAjHviJb5++tXv2G6/3j254szPsfZa/Xn5tRkc8b3fMWvuAgDO/d4hjNxxU+YvXMzok67g0WcmAfCZ/bbl+18YCcAZv72FP/z9/jY9dqlWX/30Thxz4HZEwKV/vZ8Lrr6bH31pJJ/42KY0pMTrM+Yx+pQ/MfmNOQCc+/8OYOQOG2fP8VP+xKPPvvqObW610RDG/GgUPbp14Zb/Ps3/O/dvAPTr3YMrTv8sa6/Zj5cnz+SIH1zx1t9ODdtVx+McCL0vPffyNLYbdQbbjTqDHQ4/k/kLlzD2jscA+OWVdyxfd8s9Ty2/zwuT3lhe3lznAeA7x+zBneOeZfMDTuHOcc/ynWP2BGDkTpuw/gfWYLMDfsxxp13F+T8YBWQdjh+O3puPffYcPnrE2fxw9N707dWjjY9eat0m6w3mmAO346NH/4JtPvMz9t5pY9YftjrnXXkn23zmZ2x3xHn8456nOeELewAwcoeNWH/YGmx28Bkc99NrOf/4g5vd7vnHH8xxP72WzQ4+g/WHrcGe228EwHeO2pU7H3iOzQ85kzsfeI7vHLVroe1KHYkdiDqxyzYb8uKk13ll8sx3va1P7LwFV+YJwpV/v5/9dtkiK//4FvzxhnFAll706dWDwav3Zo8dNua2+55h5pz5zJq7gNvue4Y9d9zkXbdDerc2Wncg4558mQWLlrBsWQN3P/wCB+y8GXPfXLS8Ts8eXZd/yvzExzbljzc9CMC4J1+hT6/uDB7Q623bHDygF71W7c79T7wMwB9vepD9Pr7p8vtfeWN2/ytvfHt5a9tVx5RS2y8d1QrvQETEMSt6n4JDR36Ya25+aPntL4/6GOP+dAK/Oekzb0sD1hkygHuvOp5//vYb7LjV+s1ua+CAXkzJ49wpb8xhjf7ZC91aA/syacpbHZRXp85irYF9WWuNvkyaWlE+bRZrrdH3PT0+qYzxz09hp63Wo3+fnvTo1oW9dtyIoYOy5+bJx+7Fc38/kVF7fYhTL7oFgLUG9mHS1FnL7//qtNmsNbDP27a51sA+vDqt+ToD+/diyvS5AEyZPpc1+q1W83aljqY9EogfV1sREaMj4sGIeHDMmDErsk3va11W6cy+H9+c6259BICL/3w3m+x3MtuOOoMpb8zhjG9/Esg6Axvs/SO2P+xMjj/3On7/k6PptWr3mvcT8c6ylFLz5XTgbrXqxrMvTePcy+/ghl+OZuz5X+Tx5yazdFkDACdfeDMj9juNq29+mC8fuiMAwTufzE3HwJuv03I7atmuOqaUUpsvHVWbdCAi4vEqyxPAoGr3SymNSSltnVLaevTo0W3RtLo0cqdNePSZiUybkX3ymTZjLg0N2RPzkuv+w9abrQ3A4iVLmTH7TQAeeXoiL0x6gxFrD3zH9qZNn8vg1XsDMHj13ryeb/fVqbMYOrjf8npDBvVl8uuzeXXaLIYOqigfmJVLHcFlY8exw5E/Z48v/ZqZs+cz4ZU33rb+mlse4cBds2G67Ln8Vno2ZGAfJr8+5231X502iyEDm9bJnu/TZsxdPjQxeECv5ROZa9muOiY7EO+9QcCRwH7NLNPbaJ+q4lN7bf224YvGN3+AA3b9IE89PxmA1futRqdO2SehdYYMYPgH1uDFSW9/MQW48d9PcMR+2wJwxH7bcsOdjy8vP/wT2wCwzebrMGfeAqa8MYdb//s0u2+/EX179aBvrx7svv1G3Prfp9vmYKWCGocRhg3qywG7bM41/3yE9Yetvnz9vh/bhP+9lJ2ZdOPdT3H4PlsDsM1mH2DOvIXLhyQaTZk+l3nzF7HNZh8A4PB9tuaGu8Zn97/rKY7YN7v/EftWlNewXamjaavTOG8AVkspPdp0RUTc2Ub7VDN6dO/CrttuxHGnXbW87PRvHMgWGw4lpcTLk2fwtXzdTh8azv8duy9Lly1j2bLE106/mplz5gPw6x8dzm+vvYeHn3qFcy69lSvP/BxHHbg9EyfP5DPf+x0AN98znpE7bcr4sScxf+ESvnTylQDMnDOfn158M/dc+T0AfjLm5uXbldrbVWceSf/eq7Jk2TK+efZ1zJq7gAt/eCgj1h5IQ0MDr0yZxdfPuBaAm//zNCN32Ijx130/e46f+qfl27nvym+x3RHnAfD1M/+Sn8a5Cv/877Pc8t9nADjn8tu58ief5aj9t2Hi1Fl85oTLW92uOrYOHBC0uejA8UjqsdVx7d0GqUNa8MgFAPTY5jvt3BKpY1ow7hygmckl77HNTry1zd9EnzxtjzY/jjI8jVOSpJLaew5ERHSPiHER8VhEjI+IH+fl60bE/RHxXET8KSK65uXd8tsT8vXrVGzrhLz82YgY2dqx24GQJGnltQjYNaX0QWBLYK+I2A44EzgvpTQCmAl8Pq//eWBmSmk4cF5ej4jYBBgFbArsBfw6Ijq3tGM7EJIkldTeF5JKmXn5zS75koBdgWvz8suAA/P/H5DfJl+/W0REXn51SmlRSulFYAKwTUv7tgMhSdJKLCI6R8SjwDTgVuB5YFZKaWleZRIwJP//EGAiQL5+NjCgsryZ+zTLL9OSJKmkhoa2PxEhIkYDlRdHGpNSWn61xZTSMmDLiOgLXA9s3MxmGhva3ITM1EJ5VXYgJEnqwPLOQquXZ04pzcovlbAd0DciVslThqHAa3m1ScAwYFJErAL0AWZUlDeqvE+zHMKQJKmk9p4DERFr5MkDEdED2B14GrgDOCSvdhTwt/z/Y/Pb5OtvT9mpHmOBUflZGusCI4BxLe3bBEKSpJXXmsBl+RkTnYBrUko3RMRTwNURcRrwCPC7vP7vgCsiYgJZ8jAKIKU0PiKuAZ4ClgJfzYdGqrIDIUlSSe19McaU0uPAVs2Uv0AzZ1GklBYCh1bZ1unA6bXu2yEMSZJUmAmEJEklddxvg2h7JhCSJKkwEwhJkkpq7zkQ7ckEQpIkFWYCIUlSSXUcQJhASJKk4kwgJEkqqZ7nQNiBkCSppHruQDiEIUmSCjOBkCSprPoNIEwgJElScSYQkiSV5BwISZKkAkwgJEkqyQRCkiSpABMISZJKMoGQJEkqwARCkqSSTCAkSZIKMIGQJKms+g0gTCAkSVJxJhCSJJXkHAhJkqQCTCAkSSrJBEKSJKkAEwhJkkoygZAkSSrABEKSpLLqN4AwgZAkScWZQEiSVFI9z4GwAyFJUkn13IFwCEOSJBVmAiFJUkkmEJIkSQWYQEiSVJIJhCRJUgEmEJIklVW/AYQJhCRJKs4EQpKkkpwDIUmSVIAJhCRJJZlASJIkFWACIUlSSSYQkiRJBZhASJJUVv0GECYQkiSpOBMISZJKcg6EJElSASYQkiSVZAIhSZJUgAmEJEkl1XMCYQdCkqSS6rkD4RCGJEkqzARCkqSy6jeAMIGQJEnFmUBIklSScyAkSZIKMIGQJKkkEwhJkqQCTCAkSSrJBEKSJKkAEwhJkkoygZAkSSrABEKSpLLqN4AwgZAkScWZQEiSVJJzICRJkgowgZAkqSQTCEmSpAJMICRJKssEQpIkqXYmEJIklZUa2rsF7cYOhCRJZTmEIUmSVDsTCEmSyqrjIQwTCEmSVJgJhCRJZTkHQpIkqXYmEJIkleUcCEmSpNqZQEiSVJYJhCRJWhlFxCURMS0inqwoOzkiXo2IR/Nln4p1J0TEhIh4NiJGVpTvlZdNiIjvt7ZfEwhJksrqGGdh/B64ALi8Sfl5KaVzKgsiYhNgFLApsBbwr4jYIF/9K2APYBLwQESMTSk9VW2ndiAkSVqJpZTuioh1aqx+AHB1SmkR8GJETAC2yddNSCm9ABARV+d1q3YgHMKQJKms1NDmS0SMjogHK5bRNbbuuIh4PB/i6JeXDQEmVtSZlJdVK6+qQycQCx65oL2bIHVoC8ad03olSSu1lNIYYEzBu10InAqk/Oe5wOeAaG4XNB8otDg+05E7EM0dpNpRRIzOn8iSmuHfSB3qGHMg3iGlNLXx/xFxMXBDfnMSMKyi6lDgtfz/1cqb5RCGiqg1NpPqlX8j6hAiYs2KmwcBjWdojAVGRUS3iFgXGAGMAx4ARkTEuhHRlWyi5diW9tGREwhJkjq2DnAdiIi4CtgZWD0iJgEnATtHxJZkwxAvAV8CSCmNj4hryCZHLgW+mlJalm/nOOAWoDNwSUppfEv7tQMhSdJKLKV0WDPFv2uh/unA6c2U3wTcVOt+7UCoCMd2pZb5N1JvOugciBXBORCqmZPDpJb5N6J6YgIhSVJZHWAORHsxgVCril4fXaonzX0PgepISm2/dFB2INSiiOhMdn30vYFNgMPya6lLyvwe2Ku9GyGtaA5hqDXbUPD66FI9Kfg9BHq/cQhDqqrw9dElSe9/JhBqTbXrpkuSOvAchbZmAqHWtHTddElSnTKBUGuWXx8deJXs+uiHt2+TJKmDcA6E1LyU0lKg8froTwPXtHZ9dKme5N9DcC+wYURMiojPt3ebpBXBBEKtKnp9dKmeVPkeAtWLBudASJIk1cwEQpKkspwDIUmSVDsTCEmSyjKBkCRJqp0dCKlGEbEsIh6NiCcj4s8R0fNdbGvniLgh///+LX3LaUT0jYivlNjHyRHxnVrLm9T5fUQcUmBf6/htlKpLfhunpBosSCltmVLaDFgMfLlyZWQK/02llMamlM5ooUpfoHAHQpLakh0IqZy7geH5J++nI+LXwMPAsIjYMyLujYiH86RiNYCI2CsinomIe4BPNm4oIo6OiAvy/w+KiOsj4rF82QE4A1g/Tz/Ozut9NyIeiIjHI+LHFdv6YUQ8GxH/AjZs7SAi4ov5dh6LiL80SVV2j4i7I+J/EfGJvH7niDi7Yt9fercPpLRSSw1tv3RQdiCkgiJiFWBv4Im8aEPg8pTSVsCbwInA7imlDwEPAt+OiO7AxcB+wEeBwVU2fz7w75TSB4EPAeOB7wPP5+nHdyNiT2AE2Vetbwl8OCI+FhEfJrvU+FZkHZSP1HA416WUPpLv72mg8iqK6wAfB/YFfpMfw+eB2Smlj+Tb/2J+mXNJdcazMKTa9YiIR/P/3w38DlgLeDmldF9evh2wCfCfiADoSnaZ442AF1NKzwFExJXA6Gb2sStwJEBKaRkwOyL6NamzZ748kt9ejaxD0Qu4PqU0P9/H2BqOabOIOI1smGQ1skuWN7ompdQAPBcRL+THsCewRcX8iD75vv9Xw76k958OPEehrdmBkGq3IKW0ZWVB3kl4s7IIuLXp5Y0jYkveu69BD+CnKaWLmuzjmyX28XvgwJTSYxFxNLBzxbqm20r5vr+WUqrsaBAR6xTcr6SVnEMY0nvrPmDHiBgOEBE9I2ID4Blg3YhYP69X7fsTbgOOze/bOSJ6A3PJ0oVGtwCfq5hbMSQiBgJ3AQdFRI+I6EU2XNKaXsDkiOgCfKbJukMjolPe5vWAZ/N9H5vXJyI2iIhVa9iP9P5Ux3MgTCCk91BK6fX8k/xVEdEtLz4xpfS/iBgN3BgRbwD3AJs1s4lvAGPyb3RcBhybUro3Iv6Tnyb5j3wexMbAvXkCMg84IqX0cET8CXgUeJlsmKU1/wfcn9d/grd3VJ4F/g0MAr6cUloYEb8lmxvxcGQ7fx04sLZHR9L7SaQ6Hr+RJOnd6LHd8W3+JrrgvjOjrfdRhgmEJElldeAhhrbmHAhJklSYCYQkSWXV8TQAEwhJklSYCYQkSWU5B0KSJKl2JhCSJJXlHAhJkqTamUBIklSWcyAkSZJqZwIhSVJZzoGQJEmqnQmEJEll1fEcCL+NU5IkFeYQhiRJKswOhCRJKswOhCRJKswOhCRJKswOhCRJKswOhCRJKswOhCRJKswOhCRJKswOhCRJKswOhCRJKuz/A1Rdvk7OknvPAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 648x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9,9))\n",
"sns.heatmap(confusion_matrix(y_valid, Y_tree), annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
"plt.ylabel('Actual label');\n",
"plt.xlabel('Predicted label');\n",
"all_sample_title = 'Accuracy Score: {0}'.format(tree.score(X_valid_tf , y_valid))\n",
"plt.title(all_sample_title, size = 15);"
]
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'graphviz'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-244-1f22e188c668>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgraphviz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpydotplus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdot_dt_q2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport_graphviz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdte\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"dt_q2.dot\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train_tf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"No\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Yes\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilled\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrounded\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspecial_characters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'graphviz'"
]
}
],
"source": [
"import graphviz\n",
"from IPython.display import Image\n",
"import pydotplus\n",
"from sklearn import tree\n",
"dot_dt_q2 = tree.export_graphviz(dte, out_file=\"dt_q2.dot\", feature_names=X_train_tf.columns, max_depth=2, class_names=[\"No\",\"Yes\"], filled=True, rounded=True, special_characters=True)\n",
"graph_dt_q2 = pydotplus.graph_from_dot_file('dt_q2.dot')\n",
"Image(graph_dt_q2.create_png())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Random Forest Classifier"
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=6, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
" oob_score=False, random_state=42, verbose=0, warm_start=False)"
]
},
"execution_count": 239,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"rf=RandomForestClassifier(max_depth = 6, random_state = 42)\n",
"rf.fit(X_train_tf, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Y_rf=rf.predict(X_valid_tf)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random Forest\n",
"Training:\n",
"AUC:0.674\n",
"accuracy:0.627\n",
"recall:0.568\n",
"precision:0.643\n",
"specificity:0.685\n",
"prevalence:0.500\n",
" \n",
"Validation:\n",
"AUC:0.641\n",
"accuracy:0.641\n",
"recall:0.555\n",
"precision:0.169\n",
"specificity:0.652\n",
"prevalence:0.113\n",
" \n"
]
}
],
"source": [
"y_train_preds = rf.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_rf = rf.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Random Forest')\n",
"print('Training:')\n",
"rf_train_auc, rf_train_accuracy, rf_train_recall, rf_train_precision, rf_train_specificity =print_report(y_train,y_train_preds, thresh)\n",
"print('Validation:')\n",
"rf_valid_auc, rf_valid_accuracy, rf_valid_recall, rf_valid_precision, rf_valid_specificity = print_report(y_valid,y_valid_preds_rf, thresh)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix,roc_curve, auc,roc_auc_score\n",
"roc_auc = roc_auc_score(y_valid, y_valid_preds_rf)\n",
"fp_rate, tp_rate, thresholds = roc_curve(y_valid, y_valid_preds_rf)\n",
"plt.figure()\n",
"plt.plot(fp_rate, tp_rate, label='Random Forest area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1],'r--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC - Random Forest')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[8614 4602]\n",
" [ 750 935]]\n"
]
}
],
"source": [
"print(confusion_matrix(y_valid, Y_rf))"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9,9))\n",
"sns.heatmap(confusion_matrix(y_valid, Y_rf), annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
"plt.ylabel('Actual label');\n",
"plt.xlabel('Predicted label');\n",
"all_sample_title = 'Accuracy Score: {0}'.format(rf.score(X_valid_tf , y_valid))\n",
"plt.title(all_sample_title, size = 15);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gradient Boosting Classifier"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
" learning_rate=1.0, loss='deviance', max_depth=3,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100,\n",
" presort='auto', random_state=42, subsample=1.0, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 241,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import GradientBoostingClassifier\n",
"gbc =GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n",
" max_depth=3, random_state=42)\n",
"gbc.fit(X_train_tf, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Y_gbc=gbc.predict(X_valid_tf)"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gradient Boosting Classifier\n",
"Training:\n",
"AUC:0.772\n",
"accuracy:0.695\n",
"recall:0.673\n",
"precision:0.704\n",
"specificity:0.717\n",
"prevalence:0.500\n",
" \n",
"Validation:\n",
"AUC:0.634\n",
"accuracy:0.618\n",
"recall:0.592\n",
"precision:0.166\n",
"specificity:0.622\n",
"prevalence:0.113\n",
" \n"
]
}
],
"source": [
"y_train_preds = gbc.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_gbc = gbc.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Gradient Boosting Classifier')\n",
"print('Training:')\n",
"gbc_train_auc, gbc_train_accuracy, gbc_train_recall, gbc_train_precision, gbc_train_specificity = print_report(y_train,y_train_preds, thresh)\n",
"print('Validation:')\n",
"gbc_valid_auc, gbc_valid_accuracy, gbc_valid_recall, gbc_valid_precision, gbc_valid_specificity = print_report(y_valid,y_valid_preds_gbc, thresh)"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[8216 5000]\n",
" [ 688 997]]\n"
]
}
],
"source": [
"print(confusion_matrix(y_valid, Y_gbc))"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x648 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9,9))\n",
"sns.heatmap(confusion_matrix(y_valid, Y_gbc), annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
"plt.ylabel('Actual label');\n",
"plt.xlabel('Predicted label');\n",
"all_sample_title = 'Accuracy Score: {0}'.format(gbc.score(X_valid_tf , y_valid))\n",
"plt.title(all_sample_title, size = 15);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Adaboost Classifier"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
" learning_rate=0.2, n_estimators=20, random_state=123)"
]
},
"execution_count": 240,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"#Creating a AdaBoosted Classification model,\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"adaclass = AdaBoostClassifier(n_estimators = 20, learning_rate = 0.2, random_state = 123)\n",
"adaclass.fit(X_train_tf, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"yadaclas = adaclass.predict(X_valid_tf)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[9106 4110]\n",
" [ 780 905]]\n"
]
}
],
"source": [
"#Checking the confusion matrix,\n",
"print(confusion_matrix(y_valid, yadaclas))"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adaboost Classifier\n",
"Training:\n",
"AUC:0.656\n",
"accuracy:0.610\n",
"recall:0.520\n",
"precision:0.634\n",
"specificity:0.699\n",
"prevalence:0.500\n",
" \n",
"Validation:\n",
"AUC:0.654\n",
"accuracy:0.672\n",
"recall:0.537\n",
"precision:0.180\n",
"specificity:0.689\n",
"prevalence:0.113\n",
" \n"
]
}
],
"source": [
"y_train_preds = adaclass.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_adaclass = adaclass.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Adaboost Classifier')\n",
"print('Training:')\n",
"adaclass_train_auc, adaclass_train_accuracy, adaclass_train_recall, adaclass_train_precision, adaclass_train_specificity =print_report(y_train,y_train_preds, thresh)\n",
"print('Validation:')\n",
"adaclass_valid_auc, adaclass_valid_accuracy, adaclass_valid_recall, adaclass_valid_precision, adaclass_valid_specificity = print_report(y_valid,y_valid_preds_adaclass, thresh)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix,roc_curve, auc,roc_auc_score\n",
"roc_auc = roc_auc_score(y_valid, y_valid_preds_adaclass)\n",
"fp_rate, tp_rate, thresholds = roc_curve(y_valid, y_valid_preds_adaclass)\n",
"plt.figure()\n",
"plt.plot(fp_rate, tp_rate, label='Adaboost area = %0.2f)' % roc_auc)\n",
"plt.plot([0, 1], [0, 1],'r--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC - Adaboost')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.xlim([0,1])\n",
"plt.ylim([0,1])\n",
"plt.xticks(np.arange(0,1.1,0.1))\n",
"plt.yticks(np.arange(0,1.1,0.1))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(9,9))\n",
"sns.heatmap(confusion_matrix(y_valid,yadaclas ), annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
"plt.ylabel('Actual label');\n",
"plt.xlabel('Predicted label');\n",
"all_sample_title = 'Accuracy Score: {0}'.format(adaclass.score(X_valid_tf , y_valid))\n",
"plt.title(all_sample_title, size = 15);"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"sns.set(style=\"darkgrid\")"
]
},
{
"cell_type": "code",
"execution_count": 277,
"metadata": {},
"outputs": [],
"source": [
"df_results = pd.DataFrame({'classifier':['KNN','KNN','LR','LR','NB','NB','DT','DT','RF','RF','AB','AB','SGDC','SGDC','GBC','GBC'],\n",
" 'data_set':['train','valid']*8,\n",
" 'auc':[knn_train_auc, knn_valid_auc,lr_train_auc,lr_valid_auc,nb_train_auc,nb_valid_auc,tree_train_auc,tree_valid_auc,rf_train_auc,rf_valid_auc,adaclass_train_auc,adaclass_valid_auc,sgdc_train_auc,sgdc_valid_auc,gbc_train_auc,gbc_valid_auc],\n",
" 'accuracy':[knn_train_accuracy, knn_valid_accuracy,lr_train_accuracy,lr_valid_accuracy,nb_train_accuracy,nb_valid_accuracy,tree_train_accuracy,tree_valid_accuracy,rf_train_accuracy,rf_valid_accuracy,adaclass_train_accuracy,adaclass_valid_accuracy,sgdc_train_accuracy,sgdc_valid_accuracy,gbc_train_accuracy,gbc_valid_accuracy],\n",
" 'recall':[knn_train_recall, knn_valid_recall,lr_train_recall,lr_valid_recall,nb_train_recall,nb_valid_recall,tree_train_recall,tree_valid_recall,rf_train_recall,rf_valid_recall,adaclass_train_recall,adaclass_valid_recall,sgdc_train_recall,sgdc_valid_recall,gbc_train_recall,gbc_valid_recall],\n",
" 'precision':[knn_train_precision, knn_valid_precision,lr_train_precision,lr_valid_precision,nb_train_precision,nb_valid_precision,tree_train_precision,tree_valid_precision,rf_train_precision,rf_valid_precision,adaclass_train_precision,adaclass_valid_precision,sgdc_train_precision,sgdc_valid_precision,gbc_train_precision,gbc_valid_precision],\n",
" 'specificity':[knn_train_specificity, knn_valid_specificity,lr_train_specificity,lr_valid_specificity,nb_train_specificity,nb_valid_specificity,tree_train_specificity,tree_valid_specificity,rf_train_specificity,rf_valid_specificity,adaclass_train_specificity,adaclass_valid_specificity,sgdc_train_specificity,sgdc_valid_specificity,gbc_train_specificity,gbc_valid_specificity]})"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.barplot(x=\"classifier\", y=\"auc\", hue=\"data_set\", data=df_results)\n",
"ax.set_xlabel('Classifier',fontsize = 15)\n",
"ax.set_ylabel('AUC', fontsize = 15)\n",
"ax.tick_params(labelsize=15)\n",
"\n",
"# Put the legend out of the figure\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize = 15)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.barplot(x=\"classifier\", y=\"recall\", hue=\"data_set\", data=df_results)\n",
"#sns.color_palette()\n",
"ax.set_xlabel('Classifier',fontsize = 15)\n",
"ax.set_ylabel('Recall', fontsize = 15)\n",
"ax.tick_params(labelsize=15)\n",
"\n",
"# Put the legend out of the figure\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize = 15)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.barplot(x=\"classifier\", y=\"precision\", hue=\"data_set\", data=df_results)\n",
"#sns.color_palette()\n",
"ax.set_xlabel('Classifier',fontsize = 15)\n",
"ax.set_ylabel('Precision', fontsize = 15)\n",
"ax.tick_params(labelsize=15)\n",
"\n",
"# Put the legend out of the figure\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize = 15)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 310,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.barplot(x=\"classifier\", y=\"specificity\", hue=\"data_set\", data=df_results)\n",
"#sns.color_palette()\n",
"ax.set_xlabel('Classifier',fontsize = 15)\n",
"ax.set_ylabel('Specificity', fontsize = 15)\n",
"ax.tick_params(labelsize=15)\n",
"\n",
"# Put the legend out of the figure\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize = 15)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 314,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
"Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" \"Adding an axes using the same arguments as a previous axes \"\n",
"/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
"Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" \"Adding an axes using the same arguments as a previous axes \"\n",
"/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
"Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" \"Adding an axes using the same arguments as a previous axes \"\n",
"/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
"Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" \"Adding an axes using the same arguments as a previous axes \"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1008x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(14, 5))\n",
"ax = plt.subplot(111)\n",
"\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_train_auc, lr_train_auc, nb_train_auc, rf_train_auc, tree_train_auc,sgdc_train_auc,gbc_train_auc,adaclass_train_auc]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model, values, align='center', width = 0.15, alpha=0.7, color = 'red', label= 'AUC')\n",
"plt.xticks(model, models)\n",
"\n",
"\n",
"\n",
"ax = plt.subplot(111)\n",
"\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_train_accuracy, lr_train_accuracy, nb_train_accuracy, rf_train_accuracy, tree_train_accuracy,sgdc_train_accuracy,gbc_train_accuracy,adaclass_train_accuracy]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model+0.15, values, align='center', width = 0.15, alpha=0.7, color = 'blue', label = 'auccuracy')\n",
"plt.xticks(model, models)\n",
"\n",
"\n",
"\n",
"ax = plt.subplot(111)\n",
"\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_train_recall, lr_train_recall, nb_train_recall, rf_train_recall, tree_train_recall,sgdc_train_recall,gbc_train_recall,adaclass_train_recall]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model+0.3, values, align='center', width = 0.15, alpha=0.7, color = 'green', label = 'recall')\n",
"plt.xticks(model, models)\n",
"\n",
"\n",
"\n",
"\n",
"ax = plt.subplot(111)\n",
"ax.invert_yaxis()\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_train_precision, lr_train_precision, nb_train_precision, rf_train_precision, tree_train_precision,sgdc_train_precision,gbc_train_precision,adaclass_train_precision]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model+0.45, values, align='center', width = 0.15, alpha=0.7, color = 'orange', label = 'precision')\n",
"plt.xticks(model, models,rotation=90)\n",
"\n",
"\n",
"\n",
"\n",
"ax = plt.subplot(111)\n",
"\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_train_specificity, lr_train_specificity, nb_train_specificity, rf_train_specificity, tree_train_specificity,sgdc_train_specificity,gbc_train_specificity,adaclass_train_specificity]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model+0.60, values, align='center', width = 0.15, alpha=0.7, color = 'black', label = 'specifity')\n",
"plt.xticks(model, models)\n",
"\n",
"\n",
"\n",
"plt.ylabel('Performance Metrics for Different models')\n",
"plt.title('Model evalated on Training test')\n",
" \n",
"# removing the axis on the top and right of the plot window\n",
"ax.spines['right'].set_visible(False)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 315,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
"Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" \"Adding an axes using the same arguments as a previous axes \"\n",
"/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
"Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" \"Adding an axes using the same arguments as a previous axes \"\n",
"/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
"Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" \"Adding an axes using the same arguments as a previous axes \"\n",
"/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
"Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" \"Adding an axes using the same arguments as a previous axes \"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1008x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(14, 5))\n",
"ax = plt.subplot(111)\n",
"\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_valid_auc, lr_valid_auc, nb_valid_auc, rf_valid_auc, tree_valid_auc,sgdc_valid_auc,gbc_valid_auc,adaclass_valid_auc]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model, values, align='center', width = 0.15, alpha=0.7, color = 'red', label= 'AUC')\n",
"plt.xticks(model, models)\n",
"\n",
"\n",
"\n",
"ax = plt.subplot(111)\n",
"\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_valid_accuracy, lr_valid_accuracy, nb_valid_accuracy, rf_valid_accuracy, tree_valid_accuracy,sgdc_valid_accuracy,gbc_valid_accuracy,adaclass_valid_accuracy]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model+0.15, values, align='center', width = 0.15, alpha=0.7, color = 'blue', label = 'auccuracy')\n",
"plt.xticks(model, models)\n",
"\n",
"\n",
"\n",
"ax = plt.subplot(111)\n",
"\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_train_recall, lr_train_recall, nb_train_recall, rf_train_recall, tree_train_recall,sgdc_train_recall,gbc_train_recall,adaclass_train_recall]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model+0.3, values, align='center', width = 0.15, alpha=0.7, color = 'green', label = 'recall')\n",
"plt.xticks(model, models)\n",
"\n",
"\n",
"\n",
"\n",
"ax = plt.subplot(111)\n",
"\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_valid_precision, lr_valid_precision, nb_valid_precision, rf_valid_precision, tree_valid_precision,sgdc_valid_precision,gbc_valid_precision,adaclass_valid_precision]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model+0.45, values, align='center', width = 0.15, alpha=0.7, color = 'orange', label = 'precision')\n",
"plt.xticks(model, models,rotation=90)\n",
"\n",
"\n",
"\n",
"\n",
"ax = plt.subplot(111)\n",
"\n",
"models = ['KNN','Logistic Regression','Stochastic Gradient Descent Classifier','Naive Bayes','Random Forest','Decision Tree','Adaboost Classifier','Gradient Bossting Classifier' ]\n",
"values = [knn_valid_specificity, lr_valid_specificity, nb_valid_specificity, rf_valid_specificity, tree_valid_specificity,sgdc_valid_specificity,gbc_valid_specificity,adaclass_valid_specificity]\n",
"model = np.arange(len(models))\n",
"\n",
"plt.bar(model+0.60, values, align='center', width = 0.15, alpha=0.7, color = 'black', label = 'specifity')\n",
"plt.xticks(model, models)\n",
"\n",
"\n",
"\n",
"plt.ylabel('Performance Metrics for Different models on Validation set')\n",
"plt.title('Model')\n",
" \n",
"# removing the axis on the top and right of the plot window\n",
"ax.spines['right'].set_visible(False)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [],
"source": [
"fpr_knn, tpr_knn, thresholds = roc_curve(y_valid, y_valid_preds_knn)#knn\n",
"fpr_lr, tpr_lr, thresholds = roc_curve(y_valid, y_valid_preds_lr)#logistic regression\n",
"fpr_rf, tpr_rf, thresholds = roc_curve(y_valid, y_valid_preds_rf)#random forest classifier\n",
"fpr_adaclf, tpr_adaclf, thresholds = roc_curve(y_valid, y_valid_preds_adaclass)#Ada boost classifier\n",
"fpr_nb, tpr_nb, thresholds = roc_curve(y_valid,y_valid_preds_nb )#Hyperparameters Tunning for AdaBoosted\n",
"fpr_dt, tpr_dt, thresholds = roc_curve(y_valid,y_valid_preds_tree )#decision tree\n",
"fpr_sgdc, tpr_sgdc, thresholds = roc_curve(y_valid,y_valid_preds_sgdc )#decision tree\n",
"fpr_gbc, tpr_gbc, thresholds = roc_curve(y_valid,y_valid_preds_gbc )#decision tree\n",
"roc_auc_rf = roc_auc_score(y_valid, y_valid_preds_rf)\n",
"roc_auc_knn = roc_auc_score(y_valid, y_valid_preds_knn)\n",
"roc_auc_nb = roc_auc_score(y_valid, y_valid_preds_nb)\n",
"roc_auc_tree = roc_auc_score(y_valid, y_valid_preds_tree)\n",
"roc_auc_lr = roc_auc_score(y_valid, y_valid_preds_lr)\n",
"roc_auc_adaclass = roc_auc_score(y_valid, y_valid_preds_adaclass)\n",
"roc_auc_sgdc = roc_auc_score(y_valid, y_valid_preds_sgdc)\n",
"roc_auc_gbc = roc_auc_score(y_valid, y_valid_preds_gbc)"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
"Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" \"Adding an axes using the same arguments as a previous axes \"\n"
]
},
{
"data": {
"text/plain": [
"Text(0, 0.5, 'True Positive Rate')"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"#compare the ROC curve between different models\n",
"plt.figure(figsize=(8,8))\n",
"plt.plot(fpr_knn, tpr_knn)\n",
"plt.plot(fpr_lr, tpr_lr)\n",
"plt.plot(fpr_adaclf, tpr_adaclf)\n",
"plt.plot(fpr_nb, tpr_nb)\n",
"plt.plot(fpr_rf, tpr_rf)\n",
"plt.plot(fpr_sgdc, tpr_sgdc)\n",
"plt.plot(fpr_gbc, tpr_gbc)\n",
"#plt.plot(fpr_adamod, tpr_adamod, label='Adaboost with the best Pars')\n",
"plt.plot(fpr_dt, tpr_dt)\n",
"plt.plot(fpr_adaclf, tpr_adaclf, label='Adaboost area = %0.2f)' % roc_auc_adaclass)\n",
"plt.plot(fpr_knn, tpr_knn, label='KNN area = %0.2f)' % roc_auc_knn)\n",
"plt.plot(fpr_rf, tpr_rf, label='Random Forest area = %0.2f)' % roc_auc_rf)\n",
"plt.plot(fpr_nb, tpr_nb, label='Naive Bayes area = %0.2f)' % roc_auc_nb)\n",
"plt.plot(fpr_lr, tpr_lr, label='Logistic Regression area = %0.2f)' % roc_auc_lr)\n",
"plt.plot(fpr_dt, tpr_dt, label='Decision Tree area = %0.2f)' % roc_auc_tree)\n",
"plt.plot(fpr_sgdc, tpr_sgdc, label='SGDC area = %0.2f)' % roc_auc_sgdc)\n",
"plt.plot(fpr_gbc, tpr_gbc, label='GBC area = %0.2f)' % roc_auc_gbc)\n",
"\n",
"plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n",
" label='random', alpha=.8)\n",
"plt.xlim([0,1])\n",
"plt.ylim([0,1])\n",
"plt.xticks(np.arange(0,1.1,0.1))\n",
"plt.yticks(np.arange(0,1.1,0.1))\n",
"#plt.grid()\n",
"plt.legend()\n",
"plt.axes().set_aspect('equal')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Selection: Learning Curve\n",
"We can diagnose how our models are doing by plotting a learning curve. In this section, we will make use of the learning curve code from scikit-learn's website with a small change of plotting the AUC instead of accuracy. http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html."
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.model_selection import learning_curve\n",
"from sklearn.model_selection import ShuffleSplit\n",
"\n",
"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
" n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
" \"\"\"\n",
" Generate a simple plot of the test and training learning curve.\n",
"\n",
" Parameters\n",
" ----------\n",
" estimator : object type that implements the \"fit\" and \"predict\" methods\n",
" An object of that type which is cloned for each validation.\n",
"\n",
" title : string\n",
" Title for the chart.\n",
"\n",
" X : array-like, shape (n_samples, n_features)\n",
" Training vector, where n_samples is the number of samples and\n",
" n_features is the number of features.\n",
"\n",
" y : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
" Target relative to X for classification or regression;\n",
" None for unsupervised learning.\n",
"\n",
" ylim : tuple, shape (ymin, ymax), optional\n",
" Defines minimum and maximum yvalues plotted.\n",
"\n",
" cv : int, cross-validation generator or an iterable, optional\n",
" Determines the cross-validation splitting strategy.\n",
" Possible inputs for cv are:\n",
" - None, to use the default 3-fold cross-validation,\n",
" - integer, to specify the number of folds.\n",
" - An object to be used as a cross-validation generator.\n",
" - An iterable yielding train/test splits.\n",
"\n",
" For integer/None inputs, if ``y`` is binary or multiclass,\n",
" :class:`StratifiedKFold` used. If the estimator is not a classifier\n",
" or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n",
"\n",
" Refer :ref:`User Guide <cross_validation>` for the various\n",
" cross-validators that can be used here.\n",
"\n",
" n_jobs : integer, optional\n",
" Number of jobs to run in parallel (default 1).\n",
" \"\"\"\n",
" plt.figure()\n",
" plt.title(title)\n",
" if ylim is not None:\n",
" plt.ylim(*ylim)\n",
" plt.xlabel(\"Training examples\")\n",
" plt.ylabel(\"AUC\")\n",
" train_sizes, train_scores, test_scores = learning_curve(\n",
" estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring = 'roc_auc')\n",
" train_scores_mean = np.mean(train_scores, axis=1)\n",
" train_scores_std = np.std(train_scores, axis=1)\n",
" test_scores_mean = np.mean(test_scores, axis=1)\n",
" test_scores_std = np.std(test_scores, axis=1)\n",
" plt.grid()\n",
"\n",
" plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
" train_scores_mean + train_scores_std, alpha=0.1,\n",
" color=\"r\")\n",
" plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
" test_scores_mean + test_scores_std, alpha=0.1, color=\"b\")\n",
" plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
" label=\"Training score\")\n",
" plt.plot(train_sizes, test_scores_mean, 'o-', color=\"b\",\n",
" label=\"Cross-validation score\")\n",
"\n",
" plt.legend(loc=\"best\")\n",
" return plt"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"title = \"Learning Curves (Random Forest)\"\n",
"# Cross validation with 5 iterations to get smoother mean test and train\n",
"# score curves, each time with 20% data randomly selected as a validation set.\n",
"cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n",
"estimator = RandomForestClassifier(max_depth = 6, random_state = 42)\n",
"plot_learning_curve(estimator, title, X_train_tf, y_train, ylim=(0.2, 1.01), cv=cv, n_jobs=4)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {},
"outputs": [],
"source": [
"feature_importances = pd.DataFrame(lr.coef_[0],\n",
" index = col2use,\n",
" columns=['importance']).sort_values('importance',\n",
" ascending=True)"
]
},
{
"cell_type": "code",
"execution_count": 248,
"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>importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>medical_specialty_Family/GeneralPractice</th>\n",
" <td>-0.340793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>nateglinide_No</th>\n",
" <td>-0.250629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>nateglinide_Steady</th>\n",
" <td>-0.241247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pioglitazone_Steady</th>\n",
" <td>-0.211965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>medical_specialty_Gynecology</th>\n",
" <td>-0.189600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>acarbose_Steady</th>\n",
" <td>-0.176716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>medical_specialty_ObstetricsandGynecology</th>\n",
" <td>-0.162099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>acarbose_No</th>\n",
" <td>-0.151517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pioglitazone_No</th>\n",
" <td>-0.146205</td>\n",
" </tr>\n",
" <tr>\n",
" <th>medical_specialty_Cardiology</th>\n",
" <td>-0.139933</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" importance\n",
"medical_specialty_Family/GeneralPractice -0.340793\n",
"nateglinide_No -0.250629\n",
"nateglinide_Steady -0.241247\n",
"pioglitazone_Steady -0.211965\n",
"medical_specialty_Gynecology -0.189600\n",
"acarbose_Steady -0.176716\n",
"medical_specialty_ObstetricsandGynecology -0.162099\n",
"acarbose_No -0.151517\n",
"pioglitazone_No -0.146205\n",
"medical_specialty_Cardiology -0.139933"
]
},
"execution_count": 248,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feature_importances.head(10)\n"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x1200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"num = 50\n",
"ylocs = np.arange(num)\n",
"# get the feature importance for top num and sort in reverse order\n",
"values_to_plot = feature_importances.iloc[:num].values.ravel()[::-1]\n",
"feature_labels = list(feature_importances.iloc[:num].index)[::-1]\n",
"\n",
"plt.figure(num=None, figsize=(8, 15), dpi=80, facecolor='w', edgecolor='k');\n",
"plt.barh(ylocs, values_to_plot, align = 'center')\n",
"plt.ylabel('Features')\n",
"plt.xlabel('Importance Score')\n",
"plt.title('Positive Feature Importance Score - Logistic Regression')\n",
"plt.yticks(ylocs, feature_labels)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x1200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"values_to_plot = feature_importances.iloc[-num:].values.ravel()\n",
"feature_labels = list(feature_importances.iloc[-num:].index)\n",
"\n",
"plt.figure(num=None, figsize=(8, 15), dpi=80, facecolor='w', edgecolor='k');\n",
"plt.barh(ylocs, values_to_plot, align = 'center')\n",
"plt.ylabel('Features')\n",
"plt.xlabel('Importance Score')\n",
"plt.title('Negative Feature Importance Score - Logistic Regression')\n",
"plt.yticks(ylocs, feature_labels)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {},
"outputs": [],
"source": [
"feature_importances = pd.DataFrame(rf.feature_importances_,\n",
" index = col2use,\n",
" columns=['importance']).sort_values('importance',\n",
" ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 253,
"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>importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>number_inpatient</th>\n",
" <td>0.280212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>discharge_disposition_id_22</th>\n",
" <td>0.082529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_diagnoses</th>\n",
" <td>0.065160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>num_medications</th>\n",
" <td>0.063708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time_in_hospital</th>\n",
" <td>0.060102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_outpatient</th>\n",
" <td>0.045047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_emergency</th>\n",
" <td>0.037178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>num_lab_procedures</th>\n",
" <td>0.030113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>insulin_No</th>\n",
" <td>0.027694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>num_procedures</th>\n",
" <td>0.025334</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" importance\n",
"number_inpatient 0.280212\n",
"discharge_disposition_id_22 0.082529\n",
"number_diagnoses 0.065160\n",
"num_medications 0.063708\n",
"time_in_hospital 0.060102\n",
"number_outpatient 0.045047\n",
"number_emergency 0.037178\n",
"num_lab_procedures 0.030113\n",
"insulin_No 0.027694\n",
"num_procedures 0.025334"
]
},
"execution_count": 253,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feature_importances.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x1200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"num = 50\n",
"ylocs = np.arange(num)\n",
"# get the feature importance for top num and sort in reverse order\n",
"values_to_plot = feature_importances.iloc[:num].values.ravel()[::-1]\n",
"feature_labels = list(feature_importances.iloc[:num].index)[::-1]\n",
"\n",
"plt.figure(num=None, figsize=(8, 15), dpi=80, facecolor='w', edgecolor='k');\n",
"plt.barh(ylocs, values_to_plot, align = 'center')\n",
"plt.ylabel('Features')\n",
"plt.xlabel('Importance Score')\n",
"plt.title('Feature Importance Score - Random Forest')\n",
"plt.yticks(ylocs, feature_labels)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" As you can see here, most of the important variables for random forest are continuous variables. This makes sense since you can split continuous variables more times than categorical variables.\n",
"\n",
"## Feature Importance: Summary\n",
"After reviewing these plots, I got inspired to get some new data related to the most important features. For example, in both models the most important feature is number_inpatient, which is the number of inpatient visits in the last year. This means that if patients have been to the hospital in the last year they are more likely to be re-hospitalized again. This might inspire you to get (if you have it) more data about their prior admissions.Another example is discharge_disposition_id_22 which is used if a patient is discharged to a rehab facility. For your company, you might be able to research rules for being discharged to a rehab facility and add features related to those rules. Since most of the data analysts / data scientists won't have the deep domain knowledge. I probably would take a few of these features to other experts (e.g. doctors) and ask them about the medications.\n",
"\n",
"In the case of high variance, one strategy is to reduce the number of variables to minimize overfitting. After this analyis, you could use the top N positive and negative features or the top N important random forest features. You might need to adjust N so that your performance does not drop drastically. For example, only using the top feature will likely drop the performance by a lot. Another strategy that you could use to reduce the number of variables is called PCA (principle component analysis). This is also implemented in scikit-learn if you are interested.\n",
"\n",
"The last thing that I want to mention is that the feature importance plots may also point out errors in your predictive model. Perhaps, you have some data leakage in the cleaning process. Data leakage can be thought of as the process of accidentally including something in the training that allows the machine learning algorithm to artificially cheat. For example, I built a model based on the doctor's discharge notes. When I performed this same analysis on the most important words, I discovered that the top word for predicting someone would not be re-admitted was 'death'. This made me realize that I made a mistake and forgot to exclude patients who expired in the current hospital visit. Learning from my mistakes, I had you exclude the discharge codes related to death. Similar things can also happen when you merge datasets. Perhaps when you merged the datasets one of the classes ended up with nan for some of the variables. The analysis above will help you catch some of these cases.\n",
"\n",
"## Model Selection: Hyperparameter tuning\n",
"The next thing that we should investigate is hyperparameter tuning. Hyperparameter tuning are essentially the design decisions that you made when you set up the machine learning model. For example, what is the maximum depth for your random forest? Each of these hyperparameters can be optimized to improve the model.\n",
"\n",
"In this section, we will only optimize the hyper parameters for stochastic gradient descent, random forest and gradient boosting classifier. We will not optimize KNN since it took a while to train. We will not optimize Logistic regression since it performs similarly to stochastic gradient descent. We will not optimize decision trees since they tend to overfit and perform worse that random forests and gradient boosting classifiers.\n",
"\n",
"one technique for hyperparameter tuning is called a Grid search where you test all possible combinations over a grid of values. This is very computationally intensive. The other option is to randomly test a permutation of them. This technique called Random Search is also implemented in scikit-learn. Most of this section is based on this medium blog post (https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74)by William Koehrsen. \n",
"we can get a list of the parameters inside a model with get_params. Here are the parameters in the random forest model. Wow there are so many of them!"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'bootstrap': True,\n",
" 'class_weight': None,\n",
" 'criterion': 'gini',\n",
" 'max_depth': 6,\n",
" 'max_features': 'auto',\n",
" 'max_leaf_nodes': None,\n",
" 'min_impurity_decrease': 0.0,\n",
" 'min_impurity_split': None,\n",
" 'min_samples_leaf': 1,\n",
" 'min_samples_split': 2,\n",
" 'min_weight_fraction_leaf': 0.0,\n",
" 'n_estimators': 10,\n",
" 'n_jobs': 1,\n",
" 'oob_score': False,\n",
" 'random_state': 42,\n",
" 'verbose': 0,\n",
" 'warm_start': False}"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf.get_params()"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'n_estimators': range(200, 1000, 200), 'max_features': ['auto', 'sqrt'], 'max_depth': range(1, 10), 'min_samples_split': range(2, 10, 2), 'criterion': ['gini', 'entropy']}\n"
]
}
],
"source": [
"from sklearn.model_selection import RandomizedSearchCV\n",
"\n",
"# number of trees\n",
"n_estimators = range(200,1000,200)\n",
"# maximum number of features to use at each split\n",
"max_features = ['auto','sqrt']\n",
"# maximum depth of the tree\n",
"max_depth = range(1,10,1)\n",
"# minimum number of samples to split a node\n",
"min_samples_split = range(2,10,2)\n",
"# criterion for evaluating a split\n",
"criterion = ['gini','entropy']\n",
"\n",
"# random grid\n",
"\n",
"random_grid = {'n_estimators':n_estimators,\n",
" 'max_features':max_features,\n",
" 'max_depth':max_depth,\n",
" 'min_samples_split':min_samples_split,\n",
" 'criterion':criterion}\n",
"\n",
"print(random_grid)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To use the RandomizedSearchCV function, we need something to score or evaluate a set of hyperparameters. Here we will use the auc."
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import make_scorer, roc_auc_score\n",
"auc_scoring = make_scorer(roc_auc_score)"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {},
"outputs": [],
"source": [
"# create the randomized search cross-validation\n",
"rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, \n",
" n_iter = 20, cv = 5, scoring=auc_scoring,\n",
" verbose = 1, random_state = 42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Three important parameters of RandomizedSearchCV are\n",
"\n",
"scoring = evaluation metric used to pick the best model\n",
"n_iter = number of different combinations\n",
"cv = number of cross-validation splits\n",
"increasing the last two of these will increase the run-time, but will decrease chance of overfitting. Note that the number of variables and grid size also influences the runtime. Cross-validation is a technique for splitting the data multiple times to get a better estimate of the performance metric. For the purposes of this tutorial, we will restrict to 2 CV to reduce the time."
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 6.2min finished\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"377.44798517227173\n"
]
}
],
"source": [
"# fit the random search model (this will take a few minutes)\n",
"t1 = time.time()\n",
"rf_random.fit(X_train_tf, y_train)\n",
"t2 = time.time()\n",
"print(t2-t1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'n_estimators': 800,\n",
" 'min_samples_split': 2,\n",
" 'max_features': 'auto',\n",
" 'max_depth': 9,\n",
" 'criterion': 'gini'}"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf_random.best_params_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Let's analyze the performance of the best model compared to the baseline model."
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Baseline Random Forest\n",
"Training AUC:0.674\n",
"Validation AUC:0.641\n",
"Optimized Random Forest\n",
"Training AUC:0.746\n",
"Validation AUC:0.663\n"
]
}
],
"source": [
"y_train_preds = rf.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds = rf.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Baseline Random Forest')\n",
"rf_train_auc_base = roc_auc_score(y_train, y_train_preds)\n",
"rf_valid_auc_base = roc_auc_score(y_valid, y_valid_preds)\n",
"\n",
"print('Training AUC:%.3f'%(rf_train_auc_base))\n",
"print('Validation AUC:%.3f'%(rf_valid_auc_base))\n",
"\n",
"print('Optimized Random Forest')\n",
"y_train_preds_random = rf_random.best_estimator_.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_random = rf_random.best_estimator_.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"rf_train_auc = roc_auc_score(y_train, y_train_preds_random)\n",
"rf_valid_auc = roc_auc_score(y_valid, y_valid_preds_random)\n",
"\n",
"print('Training AUC:%.3f'%(rf_train_auc))\n",
"print('Validation AUC:%.3f'%(rf_valid_auc))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optimize stochastic gradient descent"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"83.78665399551392\n"
]
}
],
"source": [
"penalty = ['none','l2','l1']\n",
"max_iter = range(100,500,100)\n",
"alpha = [0.001,0.003,0.01,0.03,0.1,0.3]\n",
"random_grid_sgdc = {'penalty':penalty,\n",
" 'max_iter':max_iter,\n",
" 'alpha':alpha}\n",
"# create the randomized search cross-validation\n",
"sgdc_random = RandomizedSearchCV(estimator = sgdc, param_distributions = random_grid_sgdc, \n",
" n_iter = 20, cv = 2, scoring=auc_scoring,verbose = 0, \n",
" random_state = 42)\n",
"\n",
"t1 = time.time()\n",
"sgdc_random.fit(X_train_tf, y_train)\n",
"t2 = time.time()\n",
"print(t2-t1)"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'penalty': 'l2', 'max_iter': 100, 'alpha': 0.1}"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sgdc_random.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Baseline sgdc\n",
"Training AUC:0.679\n",
"Validation AUC:0.662\n",
"Optimized sgdc\n",
"Training AUC:0.679\n",
"Validation AUC:0.662\n"
]
}
],
"source": [
"y_train_preds = sgdc.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds = sgdc.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Baseline sgdc')\n",
"sgdc_train_auc_base = roc_auc_score(y_train, y_train_preds)\n",
"sgdc_valid_auc_base = roc_auc_score(y_valid, y_valid_preds)\n",
"\n",
"print('Training AUC:%.3f'%(sgdc_train_auc_base))\n",
"print('Validation AUC:%.3f'%(sgdc_valid_auc_base))\n",
"print('Optimized sgdc')\n",
"y_train_preds_random = sgdc_random.best_estimator_.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_random = sgdc_random.best_estimator_.predict_proba(X_valid_tf)[:,1]\n",
"sgdc_train_auc = roc_auc_score(y_train, y_train_preds_random)\n",
"sgdc_valid_auc = roc_auc_score(y_valid, y_valid_preds_random)\n",
"\n",
"print('Training AUC:%.3f'%(sgdc_train_auc))\n",
"print('Validation AUC:%.3f'%(sgdc_valid_auc))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ## Optimize gradient boosting classifier"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"336.04998087882996\n"
]
}
],
"source": [
"# number of trees\n",
"n_estimators = range(100,500,100)\n",
"\n",
"# maximum depth of the tree\n",
"max_depth = range(1,5,1)\n",
"\n",
"# learning rate\n",
"learning_rate = [0.001,0.01,0.1]\n",
"\n",
"# random grid\n",
"\n",
"random_grid_gbc = {'n_estimators':n_estimators,\n",
" 'max_depth':max_depth,\n",
" 'learning_rate':learning_rate}\n",
"\n",
"# create the randomized search cross-validation\n",
"gbc_random = RandomizedSearchCV(estimator = gbc, param_distributions = random_grid_gbc,\n",
" n_iter = 20, cv = 2, scoring=auc_scoring,\n",
" verbose = 0, random_state = 42)\n",
"\n",
"\n",
"t1 = time.time()\n",
"gbc_random.fit(X_train_tf, y_train)\n",
"t2 = time.time()\n",
"print(t2-t1)"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'n_estimators': 200, 'max_depth': 2, 'learning_rate': 0.1}"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gbc_random.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Baseline gbc\n",
"Training AUC:0.772\n",
"Validation AUC:0.634\n",
"Optimized gbc\n",
"Training AUC:0.691\n",
"Validation AUC:0.672\n"
]
}
],
"source": [
"y_train_preds = gbc.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds = gbc.predict_proba(X_valid_tf)[:,1]\n",
"\n",
"print('Baseline gbc')\n",
"gbc_train_auc_base = roc_auc_score(y_train, y_train_preds)\n",
"gbc_valid_auc_base = roc_auc_score(y_valid, y_valid_preds)\n",
"\n",
"print('Training AUC:%.3f'%(gbc_train_auc_base))\n",
"print('Validation AUC:%.3f'%(gbc_valid_auc_base))\n",
"\n",
"print('Optimized gbc')\n",
"y_train_preds_random = gbc_random.best_estimator_.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds_random = gbc_random.best_estimator_.predict_proba(X_valid_tf)[:,1]\n",
"gbc_train_auc = roc_auc_score(y_train, y_train_preds_random)\n",
"gbc_valid_auc = roc_auc_score(y_valid, y_valid_preds_random)\n",
"\n",
"print('Training AUC:%.3f'%(gbc_train_auc))\n",
"print('Validation AUC:%.3f'%(gbc_valid_auc))"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [],
"source": [
"\n",
"df_results = pd.DataFrame({'classifier':['SGD','SGD','RF','RF','GB','GB'],\n",
" 'data_set':['base','optimized']*3,\n",
" 'auc':[sgdc_valid_auc_base,sgdc_valid_auc,\n",
" rf_valid_auc_base,rf_valid_auc,\n",
" gbc_valid_auc_base,gbc_valid_auc,],\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 263,
"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>classifier</th>\n",
" <th>data_set</th>\n",
" <th>auc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>SGD</td>\n",
" <td>base</td>\n",
" <td>0.662268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SGD</td>\n",
" <td>optimized</td>\n",
" <td>0.662137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>RF</td>\n",
" <td>base</td>\n",
" <td>0.640833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>RF</td>\n",
" <td>optimized</td>\n",
" <td>0.663166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>GB</td>\n",
" <td>base</td>\n",
" <td>0.634114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>GB</td>\n",
" <td>optimized</td>\n",
" <td>0.671838</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" classifier data_set auc\n",
"0 SGD base 0.662268\n",
"1 SGD optimized 0.662137\n",
"2 RF base 0.640833\n",
"3 RF optimized 0.663166\n",
"4 GB base 0.634114\n",
"5 GB optimized 0.671838"
]
},
"execution_count": 263,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"df_results"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAEXCAYAAAD1Bt3WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XlcVXX+x/H3ZRdUZBNFhEwdTEXEBTXRNM2RynHJZsy1ScudlDRpMvc9xYVUciuJ3DIlTaecmsq01EFsGdcRl5REQcWFkMv2+8Oftwgp8MhF8vV8PHg8uOd8z/l+LudxuO/7PZspPz8/XwAAAAbYlHUBAACg/CNQAAAAwwgUAADAMAIFAAAwjEABAAAMI1AAAADDCBQAAMAwAgUAADCMQAEAAAwjUAAAAMMIFAAAwDACBQAAMMyurAsAAPwxXblyRefPX1B2dnZZlwKD7O3t5e1dVa6urkW2uS8CxeXLGcrL46GqAPB7bGxMcnNzMbyeK1eu6Ny5FFWp4ikHB0eZTKa7UB3KQn5+vszmLJ07lyJJRYaK+yJQ5OXlEygAwIrOn7+gKlU85ejoVNalwCCTySRHRydVqeKp8+cvFBkoOIcCAHDXZWdny8HBsazLwF3k4OD4m4evCBQAgFLBYY4/lt/bngQKAABg2H1xDgUA4N7g7OIoRwfrf/RkmXP0U0ZWiZZp2bKJJk6cqrCwJ0qpqj8WAgUAwGocHezU++V3rd7vmjl9ShwoUDIc8gAAAIYxQgEAQBFOnjyh557rr//976geeKCWxo37hxo2bCRJysrK0tKlb+jzzz9VWlqaKlasqNDQthozZpycnCooMzNTc+fO0ldf7VZGxnXVrfsnDR06Qs2ahUiSzGazli59Qzt2fKTMzEwFBARo+PBwy/rLGwIFANwBN1cH2ZWjyyJzzFm6fMVc1mWUO+vXr9XYsePUsGEjbdy4XsOHD9Z778WralVvRUfP1549X2vSpOmqWtVbBw9+r2nTJqlOnbrq1auPli1bqpMnT2rhwsVycXFRXFysXn75JW3btkMVKlTQ5Mmv6ccfkzV9+iy5u3tox46PNHz4YL3zzjr5+fmX9VsvMQIFANwBOwdH7Z8zqKzLKLamL6+QRKAoqb/+tZeefLKrJCki4mXt2fOVNm3aqCFDhqtBg0A99lhnBQU1liT5+Pho06b3lJR0XJJ09uwZOTs7y8fHRxUrVlJ4+Gi1b99BNjY2OnPmB3366b+0Zs17evDB2pKkQYMG69tvv9GaNe8oMnJ82bxhAwgUAAAUITAwyPK7jY2NAgIe0okTSZKksLAntHfv14qOXqAzZ37QiRNJSk4+q+rVa0iS+vTpr5dfHq3OnTsoMDBIrVo9rLCwJ+Xo6Khjx45KkgYO7F+gP7M5W9nZ5TP4ESh+oVJlJzk52pd1GcWWm22Wrb1DWZdRIgy7/qy8DZlLbD/cf2xtC167kJeXJ3v7m58TM2ZM1c6dn+nxx7uoXbtHNWTIcM2dO9vStnHjYG3Z8k/t2fO19u7do40bN2jNmne0dOkKyzqWL39bjo4F/w84OJSv/+u3ECh+wcnRvkwuZ7pTa+b0KVdDrhLDrr9U3obMJbYf7j9Hjx5V69ZtJEk5Odk6fPigunTppoyMDH344QeaPn222rfv8P/zc5ScfFbVqlWTJK1cuUwNGzZSu3aPql27R5WVlaUuXf6sXbt2ql27RyVJly5dUkhIC0t/r78+Uw88UEtPP93Lyu/UOC4bBQCgCHFxq7Vjx0c6efKEZsyYpoyMDD311NNydHRQhQrO+vLLL3T27BkdPXpEEye+qvPnU2Q23wzd5879qNdfn6n9+xN07tyP+uij7bp+/boaNAhUzZp+6tixk2bNmqavvtqts2fPaOnSN7R58/t64IFaZfyu7wwjFAAAFOG55wbpnXfe1qlTJxUQUE8LFy5RlSpukqTp02dp0aL56tPnr6pSxU0PP9xavXv3086dn0m6eRLnokVRmjjxH7py5Yp8fWtq/PhJatKkqSTpH/+YoCVLFmnatEnKyLguf/9amjVrrpo3b1FENfc2AgUAwGqyzDlaM6dPmfRbUnv2JEqS+vYdcNv5LVs+rJYtHy40PTx8tCTJ2dn5N6/WcHZ21pgxkRozJrLEtd2LCBQAAKv5KSOLW2D/QXEOBQAAMIwRCvwhlLdLfgHgj4ZAgT+E8nbJr6QyOY4MAKWFQAGgzDHCBJR/BAoAZY4RJqD846RMAABgGIECAAAYRqAAAACGWTVQ5Obmat68eQoNDVVwcLDCw8OVlpZWZPuUlBSFh4crODhYrVq10qRJk5SZmWnFigEAQHFYNVBER0dr8+bNmj17tuLi4pSSkqKRI0fetq3ZbNbf//53paena+3atZo/f74+//xzvf7669YsGQBwF1VysZebm4vVfyq5WP8qovz8fG3f/qEuXbokSdq/P0EtWzbRhQvn72h9Rpf/PRcunFfLlk20f3/CHS1vtas8zGazYmNjNX78eLVu3VqSFBUVpQ4dOigxMVFNmjQp0H7r1q1KTU3VunXr5OrqKkkaMWKE1q1bZ62SAQB3mZ2Dg/bPGWT1fpu+vELKyLZqn999962mTJmgTZs+lCQ1ahSkbdt2yM3N/Y7WZ3T50ma1EYojR44oIyNDISEhlmm+vr6qUaOGEhIKp6Fdu3bp4YcftoQJSerZs6c2btxolXoBADAiPz+/wGt7e3t5eHjKxubOPnqNLl/arDZCkZKSIkny9vYuML1q1aqWeb906tQptWzZUgsWLNCWLVtkMpnUqVMnjRo1So6OjlapGQBwf7tyJV1Ll76h3bu/1NWr1xQYGKiRI0crIKCehg59XvXrN1By8ll9/fVuubt7qF+/Z9WjR0/9+OOPGjJkoCSpR48nNXDgC2rSpJmGD39BW7b8U1WreqtbtyfUt29/ff31biUk/EceHp4aPXqMcnJytHjxIl28mKbGjZto4sQpqlLFTfv3J1iW37dvr6ZNm3Tbmm89JfWbbxK1ePEiHTt2VB4enurYsZMGDnzB8hl67tyPev31WTpwYL/c3Nw1YMBzhv5WVgsUmZmZsrGxkb19weNYDg4Oysoq/OS569eva+PGjWrbtq0WLlyo8+fPa+rUqbp06ZJmz55dor49PCoaqh13l5dXpbIuAQaw/covtl3J5ObmauTIYZKkadNmy8XFRW+9tUJDhz6vuLj1kqQNG9aqR4+eWr16jRIS9mnevNmqWLGiOnR4THPmzNfLL4/WqlXv6IEHaunw4UOF+liyJFpjxkRq9OixWrRoviZOHK9atWpp6tSZ+umnDL3yyljFxcVqxIgXCyzXsWMntWr186PTz537UeHhw9WrV29J0rFjR/XiiyP0wgtDNGHCFJ0/n6KoqNd18WKaXnttsnJysjVq1Ah5eHhq2bK3dPXqFc2cOdXQ38tqgcLJyUl5eXnKycmRnd3P3ZrNZlWoUKFwYXZ2cnV11Zw5c2Rra6vAwEDl5OToxRdfVGRkpNzc3Ird98WL15WXl/+77djZrCM19dpdXyfbznrYfuVXcbadjY2JL2H/b+/er3Xs2BFt2LBZfn7+kqRJk6apZ8+uev/9m4HiwQdra/TosZKkBx6opYMH/6sNG9aqU6fOqly5siSpShU3OTs737aP0NBH9PjjT0qSunbtrp07P9ewYSP10EP1JUnNm7fQiRNJhZZzcnKSk5OTJCkjI0MzZ05T8+Yhev75IZKkd9+NVevWoerTp78kqWZNP40b96oGD35OQ4eO0LFjx/TDD6e1cOFiVatWXZIUEfGyIiLC7/jvZbVAUb36zYJTU1Mtv0vShQsXCh0GkW4eGnF0dJStra1lWp06dSRJycnJJQoUAACUVFLScVWpUsUSJqSb5zE0aNBQSUk3P+SDg5sWWKZBg0B9/vm/i91HzZq+lt+dnG5+ua5R4+dpjo5OunLlSpHL5+Xl6bXXXpHJZNKkSdNkMpkk3RyhOHPmB7Vv39rS9tY5HadOndSJEzff260wcat2I6wWKOrVqycXFxft27dPXbt2lSSdPXtWycnJat68eaH2zZo104YNG5SdnW05THLs2DHZ2tqqRo0a1iobAHCfcnC4/fl6ubm5srOzU1ZWVoERd+nmB7zJVPyTJm1tC1/OWpKTLhctitLhwwe1atU7BUb77e3t9fjjXdSv34BCy3h6euno0SO3PWnUCKudKurg4KDevXtrzpw52rlzpw4ePKiIiAiFhISocePGMpvNSk1NldlsliT16tVLWVlZioyMVFJSkr766iu9/vrr6tq1K6MTAIBS9+CDDyo9PV2nT5+yTMvOztbhw4dUq9aDkqQjRw4XWOa///1eAQEBkmQZLSgt8fGbtHHjBs2c+bqqV/cpMK9WrQd16tRJ1azpZ/lJT7+s6OgF+umnDNWtG6D09HT98MMPlmVud45HSVj12pNRo0apS5cuGjt2rPr37y8fHx8tXLhQknTgwAGFhobqwIEDkiRPT0+9++67Sk9PV48ePfTSSy+pU6dOmjx5sjVLBgDcp5o1C1FgYCNNmPAPffvtN0pKOq6pUyfq+vVr6tathyQpIWGf3n57pX744bTWr1+rTz/dod69+0mS5byJY8eO6Pr1u3vu0f79CZo7d5bCwyPk719LFy+mWX6ys7PVr9+zOnjwv1qwYJ5OnTqpxMT9mjx5gq5duyYPD081bdpM9eo9pEmTxuvw4UP67rtvFRU1x1BNVn18uZ2dnSIjIxUZGVloXosWLXT06NEC0+rUqaOVK1daqzwAQCnLMZtv3mSqDPotKZPJpFmz5mnhwii99FK4cnNzFRgYpJiYlZbzHNq1e1SHDh3UW2+tULVq1TVp0jS1afOIpJujBO3bd9Brr72iHj16qm3b9nft/WzbtlU5OTmKippTKAgsXrxMTZs2U1TUQr355lJt2vSeXFwqKjS0rUaOHCVJsrW1VVRUtObOnaXhw1+Qi4uLBg8epmnT7vxLu1UDBQDg/nYtI9vqd6w0wsPDQ1OmTC9yfqVKlfXqqxNuO8/Ozk4zZxZ8XMSte0RIUnz8tgLzmjZtVmC+JE2YMPm28ydMmFxg3u20aNFKLVq0KnK+u7u7ZswoGEaefLLrb67zt9ybt9sCAADlCoECAAAYxiEPAADuwNKly8u6hHsKIxQAAMAwAgUAoFT8+sZJKN9+b3sSKAAAd529vb3M5sIPfkT5ZTZn/ebdNAkUAIC7ztu7qtLT05SVdYORinIuPz9fWVk3lJ6eJm/vqkW246RMAMBd5+rqKkk6f/6CsrPLz30ncHv29vaqXr2aZbveDoECAFAqXF1df/MDCH8sHPIAAACGESgAAIBhBAoAAGAYgQIAABhGoAAAAIYRKAAAgGEECgAAYBiBAgAAGEagAAAAhhEoAACAYQQKAABgGIECAAAYRqAAAACGESgAAIBhBAoAAGAYgQIAABhGoAAAAIYRKAAAgGEECgAAYBiBAgAAGEagAAAAhhEoAACAYQQKAABgGIECAAAYRqAAAACGESgAAIBhBAoAAGAYgQIAABhGoAAAAIYRKAAAgGEECgAAYBiBAgAAGEagAAAAhhEoAACAYQQKAABgGIECAAAYRqAAAACGESgAAIBhBAoAAGAYgQIAABhm1UCRm5urefPmKTQ0VMHBwQoPD1daWlqxlh08eLD69etXyhUCAIA7YdVAER0drc2bN2v27NmKi4tTSkqKRo4c+bvLrVu3Tp9//nnpFwgAAO6I1QKF2WxWbGysIiIi1Lp1azVo0EBRUVFKTExUYmJikcudPn1a8+fPV3BwsLVKBQAAJWS1QHHkyBFlZGQoJCTEMs3X11c1atRQQkLCbZfJzc3VuHHjNGjQINWuXdtapQIAgBKyWqBISUmRJHl7exeYXrVqVcu8X3vzzTclSQMHDizd4gAAgCF21uooMzNTNjY2sre3LzDdwcFBWVlZhdofPHhQb731ljZu3CgbG2O5x8OjoqHlcXd5eVUq6xJgANuv/GLboTT9ZqBIS0vT4sWLNWTIkAIjCxMnTlR+fr5GjRold3f3YnXk5OSkvLw85eTkyM7u527NZrMqVKhQoG1WVpbGjh2rUaNGyd/fvyTv57YuXryuvLz8323HzmYdqanX7vo62XbWw/Yrv4qz7WxsTHwJwx0p8qv/hQsX1KtXL/3rX//SxYsXC8zz9/fXZ599pmeeeUaXLl0qVkfVq1eXJKWmphbq59eHQb799lslJSVp7ty5Cg4OVnBwsOLj45WQkKDg4GD9+OOPxeoTAABYR5GBYsmSJfL09NRHH32k+vXrF5j33HPPacuWLXJyctLSpUuL1VG9evXk4uKiffv2WaadPXtWycnJat68eYG2jRo10o4dOxQfH2/56dixoxo2bKj4+HhVrVq1JO8RAACUsiIDxc6dOzV69GhVrHj7oS83NzeNHj262PeHcHBwUO/evTVnzhzt3LlTBw8eVEREhEJCQtS4cWOZzWalpqbKbDbLyclJ/v7+BX4qVqxomf7LQyYAAKDsFRkoLl68KF9f399cuE6dOrpw4UKxOxs1apS6dOmisWPHqn///vLx8dHChQslSQcOHFBoaKgOHDhQ7PUBAIB7Q5Ff9atWraoffvhBNWrUKHLhM2fOyMPDo/id2dkpMjJSkZGRhea1aNFCR48eLXLZ6dOnF7sfAABgXUWOULRr104xMTHKzc297fzc3Fy9+eabatWqVakVBwAAyociA8ULL7ygpKQkDRgwQF988YXS09OVl5enS5cu6bPPPlPfvn115MgRDRkyxJr1AgCAe1CRhzy8vLz09ttva+zYsRo8eLBMJpNlXn5+vho1aqTVq1erZs2aVikUAADcu37zcok6depo8+bN+u6773To0CFdvXpVbm5uaty4serWrWutGgEAwD2uWNdfNmrUSI0aNSrtWgAAQDlVZKCIiYm5/QJ2dnJ1dVVgYKDq1atXaoUBAIDyo8hAsWHDhttOz8/P15UrV5SZman27dtr4cKFhR74BQAA7i9FBop///vfv7ngkSNHFBERoSVLlujFF1+864UBAIDy446fC16vXj1FRERo+/btd7MeAABQDt1xoJCkgIAApaSk3K1aAABAOWUoUGRkZMjZ2flu1QIAAMopQ4Fi7dq1CgoKulu1AACAcqrEl43m5eXp+vXrSkxM1OHDh/Xuu++WWnEAAKB8KPFlo/b29qpcubIaNGig6dOnq3bt2qVWHAAAKB/u+LLRa9eu6YMPPtCoUaO0devWu14YAAAoP4p16+1fSkxM1IYNG/TRRx/pxo0b3C0TAAAUL1Bcu3ZN8fHx2rBhg44fPy5Jat26tQYNGqSWLVuWaoEAAODe95uBYv/+/dqwYYM+/vhj3bhxQ/Xr11dERIQWLFigyMhI1alTx1p1AgCAe1iRgeLJJ59UUlKSHnroIQ0ZMkRhYWHy9/eXJC1YsMBqBQIAgHtfkfehOHHihPz9/dW+fXs1a9bMEiYAAAB+rcgRip07d+qDDz5QfHy8lixZIg8PD3Xu3Fl//vOfZTKZrFkjAAC4xxU5QuHp6amBAwdq69atWr9+vR577DFt3bpV/fv3V25urtatW6dz585Zs1YAAHCPKtattxs1aqSJEydq165dioqKUtu2bbV27Vp17NhRI0aMKO0aAQDAPa5E96Gwt7dXWFiYwsLClJaWpvj4eH3wwQelVRsAACgn7vjhYJ6enho0aBB3yQQAAMaeNgoAACARKAAAwF1AoAAAAIYRKAAAgGEECgAAYBiBAgAAGEagAAAAhhEoAACAYQQKAABgGIECAAAYRqAAAACGESgAAIBhBAoAAGAYgQIAABhGoAAAAIYRKAAAgGEECgAAYBiBAgAAGEagAAAAhhEoAACAYQQKAABgGIECAAAYRqAAAACGESgAAIBhVg0Uubm5mjdvnkJDQxUcHKzw8HClpaUV2X779u3q2rWrGjdurMcee0zLli1Tbm6uFSsGAADFYdVAER0drc2bN2v27NmKi4tTSkqKRo4cedu2X3zxhcaMGaOnn35aW7Zs0UsvvaTly5crJibGmiUDAIBisFqgMJvNio2NVUREhFq3bq0GDRooKipKiYmJSkxMLNR+3bp16tSpk/r27Ss/Pz917txZzz77rDZt2mStkgEAQDHZWaujI0eOKCMjQyEhIZZpvr6+qlGjhhISEtSkSZMC7YcOHSpnZ+cC02xsbHT16lWr1AsAAIrPaoEiJSVFkuTt7V1getWqVS3zfqlRo0YFXl+/fl1r165VmzZtSq9IAABwR6x2yCMzM1M2Njayt7cvMN3BwUFZWVm/u+ywYcOUlZWll156qTTLBAAAd8BqIxROTk7Ky8tTTk6O7Ox+7tZsNqtChQpFLnfp0iUNGzZMx48f16pVq1SjRo0S9+3hUfGOakbp8PKqVNYlwAC2X/nFtkNpslqgqF69uiQpNTXV8rskXbhwodBhkFvOnj2rgQMHKiMjQ3FxcapXr94d9X3x4nXl5eX/bjt2NutITb1219fJtrMetl/5VZxtZ2Nj4ksY7ojVDnnUq1dPLi4u2rdvn2Xa2bNnlZycrObNmxdqf/HiRfXv3195eXlau3btHYcJAABQ+qw2QuHg4KDevXtrzpw5cnNzk4eHhyZPnqyQkBA1btxYZrNZV65ckaurqxwcHDR58mRdvnxZq1evlpOTk1JTUyVJJpNJnp6e1iobAAAUg9UChSSNGjVKOTk5Gjt2rHJyctSmTRtNmDBBknTgwAH1799fsbGxCgoK0r/+9S/l5eXp6aefLrAOW1tbHTp0yJplAwCA32HVQGFnZ6fIyEhFRkYWmteiRQsdPXrU8vrw4cPWLA0AABjAw8EAAIBhBAoAAGAYgQIAABhGoAAAAIYRKAAAgGEECgAAYBiBAgAAGEagAAAAhhEoAACAYQQKAABgGIECAAAYRqAAAACGESgAAIBhBAoAAGAYgQIAABhGoAAAAIYRKAAAgGEECgAAYBiBAgAAGEagAAAAhhEoAACAYQQKAABgGIECAAAYRqAAAACGESgAAIBhBAoAAGAYgQIAABhGoAAAAIYRKAAAgGEECgAAYBiBAgAAGEagAAAAhhEoAACAYQQKAABgGIECAAAYRqAAAACGESgAAIBhBAoAAGAYgQIAABhGoAAAAIYRKAAAgGEECgAAYBiBAgAAGEagAAAAhhEoAACAYQQKAABgGIECAAAYRqAAAACGESgAAIBhBAoAAGCYVQNFbm6u5s2bp9DQUAUHBys8PFxpaWlFtv/+++/Vq1cvBQUFqVOnToqPj7ditQAAoLisGiiio6O1efNmzZ49W3FxcUpJSdHIkSNv2/bSpUsaNGiQGjRooE2bNqlfv3569dVXtWvXLmuWDAAAisHOWh2ZzWbFxsZq/Pjxat26tSQpKipKHTp0UGJiopo0aVKg/XvvvaeKFSvq1VdflY2NjWrXrq1Dhw5p1apVCg0NtVbZAACgGKw2QnHkyBFlZGQoJCTEMs3X11c1atRQQkJCofYJCQlq3ry5bGx+LjEkJESJiYnKy8uzSs0AAKB4rDZCkZKSIkny9vYuML1q1aqWeb9uX79+/UJtMzMzlZ6eLnd392L3bWNjKnZbTzeXYre9FzhU9ijrEkqsJNujJMrbtpPYfr/E9it9xdl2pbV98cdntUCRmZkpGxsb2dvbF5ju4OCgrKysQu1v3LghBweHQm2lm4dPSsKtBP+oFr3SrUTrLmuBQ2aXdQkl5uFRsVTWW962ncT2+yW2X+krrW0HSFY85OHk5KS8vDzl5OQUmG42m1WhQoXbtv91cLj1+nbtAQBA2bFaoKhevbokKTU1tcD0CxcuFDoMIknVqlW7bVtnZ2dVqlSp9AoFAAAlZrVAUa9ePbm4uGjfvn2WaWfPnlVycrKaN29eqH3Tpk2VkJCg/Px8y7S9e/eqSZMmBU7UBAAAZc9qn8wODg7q3bu35syZo507d+rgwYOKiIhQSEiIGjduLLPZrNTUVMthjZ49e+rSpUuaOHGikpKS9M477+jDDz/UoEGDrFUyAAAoJlP+L4cASllOTo7mzp2rzZs3KycnR23atNGECRPk7u6uvXv3qn///oqNjVWLFi0kSd98842mTZumo0ePysfHR+Hh4XriiSesVS4AACgmqwYKAADwx8TJCAAAwDACBQAAMIxAAQAADLPanTLx2+Lj4xUXF6fjx4/LZDIpICBA/fv31+OPP25pk5eXp/Xr1ys+Pl4nTpxQVlaW/P399cQTT+jvf/+7HB0dJclygustJpNJFSpUUN26dTVgwABObLWCRx99VMnJyQWmOTk5ycfHR3/729/07LPPFtnulpiYGLVv3760S0URirO//Xpfk6RKlSopODhYkZGRql27dhlVD1gfgeIesH79es2ePVvjx49X06ZNlZ2drU8++UQRERHKyspS9+7dlZOTo8GDB+vQoUMaPny4WrVqJUdHRx04cEALFizQnj179NZbb8lk+vk+/Js3b5aXl5fy8vJ0+fJlbdu2TS+99JLS09PVp0+fMnzH94fnn39eAwYMsLxOT0/XunXrNHPmTFWtWtUSFn/d7hZXV1er1YqCiru/3fLrfe2NN97QwIED9fHHH1uCPvBHR6C4B6xfv15//etf1aNHD8u0OnXq6OTJk4qNjVX37t21atUq7d27V++//74CAgIs7Xx9fRUUFKSwsDB98cUXateunWWeu7u7vLy8JN18KFu9evWUmZmpuXPnKiwsrEQPWEPJOTs7W/7+kuTl5aXXXntNO3fu1Pbt2y2B4tftUPaKu7/degzAr/e1CRMmqE2bNtqzZ48eeeSRMnkPgLVxDsU9wMbGRomJibp27VqB6ePGjVN0dLTy8/O1Zs0adevWrcA/t1v8/Py0ffv2Yv3jGjBggH766Sd9/vnnd6t8lJC9vb3s7Mjy96q7sb85OztLUoERQ+CPjkBxDxg4cKC+++47tWnTRkOGDNHKlSt1+PBhubu7y9fXV2fPntW5c+fUsmXLItfh7+9frH9eNWvWVIUKFXTs2LG7+RZQDJmZmVqxYoWSkpLUpUuXsi4HRTC6v/30009auHCh/Pz8fnMdwB8NX5PuAWFhYfL29tbq1au1e/fBqotIAAAJOElEQVRuffbZZ5Kk+vXra86cObp+/bokyc3NrcByf/nLX3TmzBnL6y5dumjKlCm/21/lypUt60TpWbJkiZYvXy7p5rferKwsBQQEKCoqSh06dLhtu1sGDRqk4cOHW7Ve3JSWliapePvbrROcO3fuLJPJpPz8fN24cUOSFBUVJQcHBytVDZQ9AsU9okmTJmrSpIlyc3N18OBB/fvf/1ZcXJyef/55y8lfV65cKbBMTEyMsrOzJd08PPLrx70X5fr16zyx1Qr69Omj3r17Kzc3V59++qmWLFmiHj16FLrK5la7X+KEzLJTpUoVSSXb31asWCEvLy/l5+fr2rVr+uyzzzRmzBjl5+dzVRXuGwSKMnbu3Dm9+eabGj58uLy8vGRra6tGjRqpUaNGatasmQYOHKirV6/K09NTCQkJBS4j9fHxsfzu5ORUrP5Onz6tjIwMNWjQ4K6/FxTk6uoqf39/SdKDDz4oGxsbTZ8+Xe7u7nryySdv2w5lz8/Pr8T7m6+vr6pVq2Z5HRgYqAMHDmjVqlUECtw3OIeijDk6Omrjxo368MMPC82rXLmyTCaTvLy81KdPH23atElJSUmF2pnNZl26dKlY/a1Zs0YVK1YscDUIrOO5555T06ZNNXnyZKWmppZ1OSiCra3tXdnf8vPzxaOScD9hhKKMubu7a+DAgZo3b56uX7+uTp06ycnJSceOHdOCBQvUvXt3+fj46IUXXtD333+vZ555RkOHDlVoaKicnJz0zTffaNmyZTp58qT69etXYN2XLl2Sra2t5dr4zZs3KzY2VlOmTFHFihXL6B3fv0wmk6ZOnapu3bpp2rRpWrhwYVmXhCKUdH+7ta9JUlZWlj7++GPt2bNHkZGRZfUWAKsjUNwDRo8eLX9/f23YsEFvv/22srKy5Ofnp+7du1vuqGhnZ6clS5bogw8+0KZNmxQTE6OffvpJPj4+Cg0NVXR0tB544IEC6+3evbukmx9kHh4eCggIUExMDNfFl6HatWtr8ODBio6O1qefflrW5aAIxd3f9u7dK+nnfU2SHBwcVKtWLb366qvq27dvWb0FwOp4fDkAADCMcygAAIBhBAoAAGAYgQIAABhGoAAAAIYRKAAAgGEECgAAYBiBAvh/ZrNZK1euVLdu3RQcHKyHH35YQ4YM0ffffy/p5lMoAwIClJCQUOq1REdH67HHHrO8/vLLL/Xoo48qMDBQsbGxevTRR7VkyZJSrwMAiov7UAC6+Wjx/v376/LlywoPD1dQUJAyMjIUGxur7du3a9myZfL19VWHDh307rvvqlmzZqVaT0ZGhrKysuTu7i5Jeuqpp1SlShVNnjxZVapUkdlslpOTk5ydnUu1DgAoLu6UCUhasGCBTp06pQ8//FDe3t6W6bNmzdLFixc1depUxcTEWK0eFxcXubi4WF5fu3ZNjzzyiHx9fa1WAwCUBIc8cN8zm83atGmTevbsWSBM3DJhwgTNmzdPJpOpwPT09HS98sorCg0NVYMGDRQaGqrZs2crLy9PkpSWlqYRI0aoRYsWaty4sZ599lkdPnzYsvymTZsUFhamhg0bqn379lq0aJFl2V8e8ggICNDp06e1ePFiBQQESFKhQx6ffPKJ/vKXvygwMFCdO3fWypUrLeu6dagmJiZGrVq1UlhYWLEfdQ8AxcUIBe57Z86c0dWrVxUUFHTb+TVr1pR084P5l8aNG6fLly9r6dKlqlKlinbu3KmpU6eqadOm6tixoyZPnqycnBytWbNGJpNJ8+bN08iRI/XJJ5/oyJEjmjBhgqKiotSwYUMdPHhQY8aMkZ+fn7p161agn127dulvf/ub/vznP+u5554rVN8XX3yhMWPGaPz48QoJCdH//vc/TZkyRZmZmRoxYoSl3bZt2xQXF6cbN27IwcHB6J8NAAogUOC+d/XqVUk3HxdfEm3atFGLFi1Ut25dSVKfPn20YsUKHT16VB07dtTp06cVEBAgX19fOTo6asqUKTp+/Ljy8vJ05swZmUwm+fj4WH7eeustVatWrVA/Xl5esrW1lbOzs7y8vArNj4mJ0TPPPKOePXtKkvz8/JSRkaHXXntNw4YNs7Tr06ePateuXaL3CADFRaDAfc/NzU3SzUMYJfHMM8/o008/1XvvvadTp07p6NGjSklJsRxqGDZsmMaNG6cdO3aoefPmatu2rbp16yYbGxu1adNGQUFBeuqpp+Tv76/Q0FA9/vjj8vHxKXH9hw8f1vfff69169ZZpuXl5enGjRtKTk62HKq5NdICAKWBcyhw3/Pz85OHh4e+/fbb287fu3evhgwZotTUVMu0/Px8vfDCC5o1a5YqVKigrl27Ki4uTjVq1LC06dy5s7788ktNmzZNXl5eWrJkibp166a0tDQ5OTkpLi5OGzduVNeuXXXo0CH17dtXy5cvL3H99vb2GjJkiOLj4y0/W7Zs0Y4dOwqcE+Lo6FjidQNAcREocN+zsbFR9+7d9f777+v8+fMF5uXn52vZsmU6efKkPD09LdOPHz+uXbt2KTo6WqNHj9YTTzwhNzc3paamKj8/Xzk5OZo9e7aSk5PVpUsXzZw5U9u2bVNycrL27dun3bt3a/HixQoMDNTw4cO1bt069erVS5s3by5x/XXq1NGpU6fk7+9v+Tl27Jjmz59v+G8DAMXFIQ9ANw9P7N69W71799bo0aMVFBSktLQ0rVq1Sv/5z3+0atWqAld5VK5cWXZ2dvrnP/8pV1dXpaamav78+TKbzTKbzbKzs9PBgweVkJCg8ePHy93dXVu3bpW9vb0aNGig8+fPa/HixapUqZLat2+vtLQ07d27V40bNy5x7UOHDtXgwYP1pz/9SZ06ddKpU6c0YcIEPfLII5x8CcBqCBSAbt73IS4uTsuXL9cbb7yhc+fOqVKlSgoKCtL69ev10EMPFbjKw9vbWzNmzFB0dLRWr14tb29vhYWFydvb23JnzXnz5mnGjBkaPHiwMjIyVLduXS1evNgyijBjxgytWLFCc+fOVcWKFdWxY0e9/PLLJa69bdu2mjNnjpYtW6ZFixbJ3d1d3bp10+jRo+/a3wcAfg93ygQAAIZxDgUAADCMQAEAAAwjUAAAAMMIFAAAwDACBQAAMIxAAQAADCNQAAAAwwgUAADAMAIFAAAw7P8AMxEAbB7CUTgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.barplot(x=\"classifier\", y=\"auc\", hue=\"data_set\", data=df_results)\n",
"ax.set_xlabel('Classifier',fontsize = 15)\n",
"ax.set_ylabel('AUC', fontsize = 15)\n",
"ax.tick_params(labelsize=15)\n",
"# Put the legend out of the figure\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize = 15)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the hyperparameter tuning improved the models, but not by much."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Selection: Best Classifier\n",
"Here we will chose the gradient boosting classifier since it has the best AUC on the validation set. You won't want to train your best classifier every time you want to run new predictions. Therefore, we need to save the classifier. We will use the package pickle."
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [],
"source": [
"pickle.dump(gbc_random.best_estimator_, open('best_classifier.pkl', 'wb'),protocol = 4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Evaluation\n",
"Now that we have selected our best model. Let's evaluate the performance of the test set."
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
}
],
"source": [
"X_test = df_test[col2use].values\n",
"y_test = df_test['OUTPUT_LABEL'].values\n",
"\n",
"scaler = pickle.load(open('scaler.sav', 'rb'))\n",
"X_test_tf = scaler.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [],
"source": [
"best_model = pickle.load(open('best_classifier.pkl','rb'))"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [],
"source": [
"y_train_preds = best_model.predict_proba(X_train_tf)[:,1]\n",
"y_valid_preds = best_model.predict_proba(X_valid_tf)[:,1]\n",
"y_test_preds = best_model.predict_proba(X_test_tf)[:,1]"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training:\n",
"AUC:0.691\n",
"accuracy:0.640\n",
"recall:0.586\n",
"precision:0.657\n",
"specificity:0.694\n",
"prevalence:0.500\n",
" \n",
"Validation:\n",
"AUC:0.672\n",
"accuracy:0.660\n",
"recall:0.583\n",
"precision:0.184\n",
"specificity:0.670\n",
"prevalence:0.113\n",
" \n",
"Test:\n",
"AUC:0.668\n",
"accuracy:0.652\n",
"recall:0.582\n",
"precision:0.186\n",
"specificity:0.661\n",
"prevalence:0.117\n",
" \n"
]
}
],
"source": [
"thresh = 0.5\n",
"\n",
"print('Training:')\n",
"train_auc, train_accuracy, train_recall, train_precision, train_specificity = print_report(y_train,y_train_preds, thresh)\n",
"print('Validation:')\n",
"valid_auc, valid_accuracy, valid_recall, valid_precision, valid_specificity = print_report(y_valid,y_valid_preds, thresh)\n",
"print('Test:')\n",
"test_auc, test_accuracy, test_recall, test_precision, test_specificity = print_report(y_test,y_test_preds, thresh)"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import roc_curve \n",
"\n",
"fpr_train, tpr_train, thresholds_train = roc_curve(y_train, y_train_preds)\n",
"auc_train = roc_auc_score(y_train, y_train_preds)\n",
"\n",
"fpr_valid, tpr_valid, thresholds_valid = roc_curve(y_valid, y_valid_preds)\n",
"auc_valid = roc_auc_score(y_valid, y_valid_preds)\n",
"\n",
"fpr_test, tpr_test, thresholds_test = roc_curve(y_test, y_test_preds)\n",
"auc_test = roc_auc_score(y_test, y_test_preds)\n",
"\n",
"plt.plot(fpr_train, tpr_train, 'r-',label ='Train AUC:%.3f'%auc_train)\n",
"plt.plot(fpr_valid, tpr_valid, 'b-',label ='Valid AUC:%.3f'%auc_valid)\n",
"plt.plot(fpr_test, tpr_test, 'g-',label ='Test AUC:%.3f'%auc_test)\n",
"plt.plot([0,1],[0,1],'k--')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conclusion\n",
"Through this project, we created a binary classifier to predict the probability that a patient with diabetes would be readmitted to the hospital within 30 days. On held out test data, our best model had an AUC of of 0.67. Using this model, we are able to catch 58% of the readmissions from our model that performs approximately 1.5 times better than randomly selecting patients."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}