1 기계학습 모형 개발 30분 ¹ ²

최적 기계학습 모형 아키텍처(GLM, NN, DL, SVM, GBM, xgBoost, Random Forest…)를 선택하고, 이에 대한 모델 튜닝을 진행하여 최적모형을 개발하는 것도 중요한 일이지만, 사업분야에 적용하기 위해서는 여러가지 제약조건을 따르고 주어진 제약조건에 최적화하는 것이 필요하다.

이러한 면에서 최고 성능의 기계학습 모형을 개발하는 것보다 “애자일 기계학습모형 개발(Agile ML development)” 개념을 접목하여 극단적으로 최고성능의 90%를 내는 기계학습 예측모형을 30분내에 개발하는 것이 더 의미가 클 수 있다.

Matt Dancho(August 7, 2018), “KAGGLE COMPETITION IN 30 MINUTES: PREDICT HOME CREDIT DEFAULT RISK WITH R” Business Science.에 제시된 사례를 바탕으로 30분 내에 기계학습 모형 구축 및 배포를 시연해 보자.

2 자동화 전략

기계학습 예측모형 개발 자동화 전략은 원본데이터를 있는 그대로 두고, 각 변수에 맞게 적절한 데이터 변형 전략을 구현하고 결측값에 대한 채워넣기를 수행한 후에 \(H_2O\) AutoML 모형을 개발하여 운영계에 배포하는 것으로 마무리한다.

AutoML 자동화 전략 파이프라인

가장 먼저 원본 데이터(application_train.csv)를 가져와서 AutoML 엔진인 \(H_2O\)에 넣기 전에 결측값 처리 및 자료형 변수변환에 대한 사항을 적시한다.

2.1 데이터 가져오기

Kaggle - Home Credit Default Risk 데이터셋을 다운로드 받아 kable 함수로 데이터가 원본 데이터에서 데이터프레임으로 제대로 변환이 되었는지 확인한다.

# 0. 환경설정 -----
# General 
library(tidyverse)
library(skimr)

# Preprocessing
library(recipes)

# Machine Learning
library(h2o)

# 1. 데이터 -----
## 1.1. 데이터 가져오기 
application_train_tbl <- read_csv("data/application_train.csv")

## 1.2. 데이터 일별하기
application_train_tbl %>%
    slice(1:10) %>%
    kable() %>% 
    kable_styling() %>%
    scroll_box(width = "800px")

SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	ORGANIZATION_TYPE	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	FONDKAPREMONT_MODE	HOUSETYPE_MODE	TOTALAREA_MODE	WALLSMATERIAL_MODE	EMERGENCYSTATE_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	FLAG_DOCUMENT_8	FLAG_DOCUMENT_14	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
100002	1	Cash loans	M	N	Y	0	202500	406597.5	24700.5	351000	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.018801	-9461	-637	-3648	-2120	NA	1	1	0	1	1	Laborers	1	2	2	WEDNESDAY	10	0	0	Business Entity Type 3	0.0830370	0.2629486	0.1393758	0.0247	0.0369	0.9722	0.6192	0.0143	0.00	0.0690	0.0833	0.1250	0.0369	0.0202	0.0190	0.0000	0.0000	0.0252	0.0383	0.9722	0.6341	0.0144	0.0000	0.0690	0.0833	0.1250	0.0377	0.022	0.0198	0	0	0.0250	0.0369	0.9722	0.6243	0.0144	0.00	0.0690	0.0833	0.1250	0.0375	0.0205	0.0193	0.0000	0.00	reg oper account	block of flats	0.0149	Stone, brick	No	2	2	2	2	-1134	1	0	0	0	0	0	0	0	1
100003	0	Cash loans	F	N	N	0	270000	1293502.5	35698.5	1129500	Family	State servant	Higher education	Married	House / apartment	0.003541	-16765	-1188	-1186	-291	NA	1	1	0	1	1	Core staff	2	1	1	MONDAY	11	0	0	School	0.3112673	0.6222458	NA	0.0959	0.0529	0.9851	0.7960	0.0605	0.08	0.0345	0.2917	0.3333	0.0130	0.0773	0.0549	0.0039	0.0098	0.0924	0.0538	0.9851	0.8040	0.0497	0.0806	0.0345	0.2917	0.3333	0.0128	0.079	0.0554	0	0	0.0968	0.0529	0.9851	0.7987	0.0608	0.08	0.0345	0.2917	0.3333	0.0132	0.0787	0.0558	0.0039	0.01	reg oper account	block of flats	0.0714	Block	No	1	0	1	0	-828	1	0	0	0	0	0	0	0	0
100004	0	Revolving loans	M	Y	Y	0	67500	135000.0	6750.0	135000	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.010032	-19046	-225	-4260	-2531	26	1	1	1	1	1	Laborers	1	2	2	MONDAY	9	0	0	Government	NA	0.5559121	0.7295667	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	0	0	0	-815	0	0	0	0	0	0	0	0	0
100006	0	Cash loans	F	N	Y	0	135000	312682.5	29686.5	297000	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.008019	-19005	-3039	-9833	-2437	NA	1	1	0	1	0	Laborers	2	2	2	WEDNESDAY	17	0	0	Business Entity Type 3	NA	0.6504417	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2	0	2	0	-617	1	0	0	NA	NA	NA	NA	NA	NA
100007	0	Cash loans	M	N	Y	0	121500	513000.0	21865.5	513000	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.028663	-19932	-3038	-4311	-3458	NA	1	1	0	1	0	Core staff	1	2	2	THURSDAY	11	1	1	Religion	NA	0.3227383	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	0	0	0	-1106	0	1	0	0	0	0	0	0	0
100008	0	Cash loans	M	N	Y	0	99000	490495.5	27517.5	454500	Spouse, partner	State servant	Secondary / secondary special	Married	House / apartment	0.035792	-16941	-1588	-4970	-477	NA	1	1	1	1	1	Laborers	2	2	2	WEDNESDAY	16	0	0	Other	NA	0.3542247	0.6212263	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	0	0	0	-2536	1	0	0	0	0	0	0	1	1
100009	0	Cash loans	F	Y	Y	1	171000	1560726.0	41301.0	1395000	Unaccompanied	Commercial associate	Higher education	Married	House / apartment	0.035792	-13778	-3130	-1213	-619	17	1	1	0	1	1	Accountants	3	2	2	SUNDAY	16	0	0	Business Entity Type 3	0.7747614	0.7239999	0.4920601	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	0	1	0	-1562	0	1	1	0	0	0	1	1	2
100010	0	Cash loans	M	Y	Y	0	360000	1530000.0	42075.0	1530000	Unaccompanied	State servant	Higher education	Married	House / apartment	0.003122	-18850	-449	-4597	-2379	8	1	1	1	1	0	Managers	2	3	3	MONDAY	16	1	1	Other	NA	0.7142793	0.5406545	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2	0	2	0	-1070	1	0	0	0	0	0	0	0	0
100011	0	Cash loans	F	N	Y	0	112500	1019610.0	33826.5	913500	Children	Pensioner	Secondary / secondary special	Married	House / apartment	0.018634	-20099	365243	-7427	-3514	NA	1	0	0	1	0	NA	2	2	2	WEDNESDAY	14	0	0	XNA	0.5873340	0.2057473	0.7517237	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	0	1	0	0	1	0	0	0	0	0	0	0	1
100012	0	Revolving loans	M	N	Y	0	135000	405000.0	20250.0	405000	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.019689	-14469	-2019	-14437	-3992	NA	1	1	0	1	0	Laborers	1	2	2	THURSDAY	8	0	0	Electricity	NA	0.7466436	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2	0	2	0	-1673	0	0	0	NA	NA	NA	NA	NA	NA

## 1.3. 훈련/시험 데이터 분할
x_train_tbl <- application_train_tbl %>% select(-TARGET)
y_train_tbl <- application_train_tbl %>% select(TARGET)   

# 메모리 절약을 위해서 불필요해진 객체 제거
rm(application_train_tbl)

2.2 데이터 변환

원본 데이터를 불러왔다면 다음 단계로 문자형 변수를 식별하여 string_2_factor_names 벡터로 준비하고, 숫자형 변수 중 범주형으로 코딩할 수 있는 것을 범주 수준이 7개를 기준으로 미만이면 범주 요인형변수로 식별하여 num_2_factor_names 벡터로 준비한다. 마지막으로 결측값은 숫자형 결측값은 평균으로, 범주형 결측값은 최빈치로 채워 넣도록 준비한다.

recipe 팩키지를 활용하여 변수변환 과정과 결측값 채워넣는 과정에 대해서 명세를 기록한다.

# 2. 데이터 탐색 -----
## 2.1. 데이터 자료형 살펴보기
skim_to_list(x_train_tbl)

$character
# A tibble: 16 x 8
   variable              missing complete n     min   max   empty n_unique
 * <chr>                 <chr>   <chr>    <chr> <chr> <chr> <chr> <chr>   
 1 CODE_GENDER           0       307511   3075~ 1     3     0     3       
 2 EMERGENCYSTATE_MODE   145755  161756   3075~ 2     3     0     2       
 3 FLAG_OWN_CAR          0       307511   3075~ 1     1     0     2       
 4 FLAG_OWN_REALTY       0       307511   3075~ 1     1     0     2       
 5 FONDKAPREMONT_MODE    210295  97216    3075~ 13    21    0     4       
 6 HOUSETYPE_MODE        154297  153214   3075~ 14    16    0     3       
 7 NAME_CONTRACT_TYPE    0       307511   3075~ 10    15    0     2       
 8 NAME_EDUCATION_TYPE   0       307511   3075~ 15    29    0     5       
 9 NAME_FAMILY_STATUS    0       307511   3075~ 5     20    0     6       
10 NAME_HOUSING_TYPE     0       307511   3075~ 12    19    0     6       
11 NAME_INCOME_TYPE      0       307511   3075~ 7     20    0     8       
12 NAME_TYPE_SUITE       1292    306219   3075~ 6     15    0     7       
13 OCCUPATION_TYPE       96391   211120   3075~ 7     21    0     18      
14 ORGANIZATION_TYPE     0       307511   3075~ 3     22    0     58      
15 WALLSMATERIAL_MODE    156341  151170   3075~ 5     12    0     7       
16 WEEKDAY_APPR_PROCESS~ 0       307511   3075~ 6     9     0     7       

$integer
# A tibble: 40 x 12
   variable  missing complete n      mean   sd     p0    p25   p50   p75  
 * <chr>     <chr>   <chr>    <chr>  <chr>  <chr>  <chr> <chr> <chr> <chr>
 1 CNT_CHIL~ 0       307511   307511 "    ~ "    ~ 0     "   ~ 0     "   ~
 2 DAYS_BIR~ 0       307511   307511 "-160~ "  43~ -252~ "-19~ -157~ "-12~
 3 DAYS_EMP~ 0       307511   307511 " 638~ "1412~ -179~ " -2~ -1213 "  -~
 4 DAYS_ID_~ 0       307511   307511 " -29~ "  15~ -7197 " -4~ -3254 " -1~
 5 FLAG_CON~ 0       307511   307511 "    ~ "    ~ 0     "   ~ 1     "   ~
 6 FLAG_DOC~ 0       307511   307511 "    ~ "    ~ 0     "   ~ 0     "   ~
 7 FLAG_DOC~ 0       307511   307511 "    ~ "    ~ 0     "   ~ 0     "   ~
 8 FLAG_DOC~ 0       307511   307511 "    ~ "    ~ 0     "   ~ 0     "   ~
 9 FLAG_DOC~ 0       307511   307511 "    ~ "    ~ 0     "   ~ 0     "   ~
10 FLAG_DOC~ 0       307511   307511 "    ~ "    ~ 0     "   ~ 0     "   ~
# ... with 30 more rows, and 2 more variables: p100 <chr>, hist <chr>

$numeric
# A tibble: 65 x 12
   variable   missing complete n      mean   sd    p0    p25   p50   p75  
 * <chr>      <chr>   <chr>    <chr>  <chr>  <chr> <chr> <chr> <chr> <chr>
 1 AMT_ANNUI~ 12      307499   307511 " 271~ " 14~ "  1~ " 16~ " 24~ " 34~
 2 AMT_CREDIT 0       307511   307511 " 6e+~ " 4e~ " 45~ "270~ "513~ "808~
 3 AMT_GOODS~ 278     307233   307511 "5383~ "369~ " 40~ "238~ "450~ "679~
 4 AMT_INCOM~ 0       307511   307511 "1687~ "237~ " 25~ "112~ "147~ " 2e~
 5 AMT_REQ_C~ 41519   265992   307511 "    ~ "   ~ "   ~ "   ~ "   ~ "   ~
 6 AMT_REQ_C~ 41519   265992   307511 "    ~ "   ~ "   ~ "   ~ "   ~ "   ~
 7 AMT_REQ_C~ 41519   265992   307511 "    ~ "   ~ "   ~ "   ~ "   ~ "   ~
 8 AMT_REQ_C~ 41519   265992   307511 "    ~ "   ~ "   ~ "   ~ "   ~ "   ~
 9 AMT_REQ_C~ 41519   265992   307511 "    ~ "   ~ "   ~ "   ~ "   ~ "   ~
10 AMT_REQ_C~ 41519   265992   307511 "    ~ "   ~ "   ~ "   ~ "   ~ "   ~
# ... with 55 more rows, and 2 more variables: p100 <chr>, hist <chr>

## 2.2. 전처리 전략
### 문자형 
string_2_factor_names <- x_train_tbl %>%
    select_if(is.character) %>%
    names()

# string_2_factor_names

### 숫자형 숫자
unique_numeric_values_tbl <- x_train_tbl %>%
    select_if(is.numeric) %>%
    map_df(~ unique(.) %>% length()) %>%
    gather() %>%
    arrange(value) %>%
    mutate(key = as_factor(key))

# unique_numeric_values_tbl

factor_limit <- 7

num_2_factor_names <- unique_numeric_values_tbl %>%
    filter(value < factor_limit) %>%
    arrange(desc(value)) %>%
    pull(key) %>%
    as.character()

# num_2_factor_names

### 결측값
missing_tbl <- x_train_tbl %>%
    summarize_all(.funs = ~ sum(is.na(.)) / length(.)) %>%
    gather() %>%
    arrange(desc(value)) %>%
    filter(value > 0)

# missing_tbl

## 요리법에 따른 데이터 전처리

rec_obj <- recipe(~ ., data = x_train_tbl) %>%
    step_string2factor(string_2_factor_names) %>%
    step_num2factor(num_2_factor_names) %>%
    step_meanimpute(all_numeric()) %>%
    step_modeimpute(all_nominal()) %>%
    prep(stringsAsFactors = FALSE)

# rec_obj

x_train_processed_tbl <- bake(rec_obj, x_train_tbl) 

### Y 변수 변환
y_train_processed_tbl <- y_train_tbl %>%
    mutate(TARGET = TARGET %>% as.character() %>% as.factor())

데이터 변환 전

### 데이터 변환 전 
x_train_tbl %>%
    select(1:30) %>%
    glimpse()

Observations: 307,511
Variables: 30
$ SK_ID_CURR                 <int> 100002, 100003, 100004, 100006, 100...
$ NAME_CONTRACT_TYPE         <chr> "Cash loans", "Cash loans", "Revolv...
$ CODE_GENDER                <chr> "M", "F", "M", "F", "M", "M", "F", ...
$ FLAG_OWN_CAR               <chr> "N", "N", "Y", "N", "N", "N", "Y", ...
$ FLAG_OWN_REALTY            <chr> "Y", "N", "Y", "Y", "Y", "Y", "Y", ...
$ CNT_CHILDREN               <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,...
$ AMT_INCOME_TOTAL           <dbl> 202500.00, 270000.00, 67500.00, 135...
$ AMT_CREDIT                 <dbl> 406597.5, 1293502.5, 135000.0, 3126...
$ AMT_ANNUITY                <dbl> 24700.5, 35698.5, 6750.0, 29686.5, ...
$ AMT_GOODS_PRICE            <dbl> 351000, 1129500, 135000, 297000, 51...
$ NAME_TYPE_SUITE            <chr> "Unaccompanied", "Family", "Unaccom...
$ NAME_INCOME_TYPE           <chr> "Working", "State servant", "Workin...
$ NAME_EDUCATION_TYPE        <chr> "Secondary / secondary special", "H...
$ NAME_FAMILY_STATUS         <chr> "Single / not married", "Married", ...
$ NAME_HOUSING_TYPE          <chr> "House / apartment", "House / apart...
$ REGION_POPULATION_RELATIVE <dbl> 0.018801, 0.003541, 0.010032, 0.008...
$ DAYS_BIRTH                 <int> -9461, -16765, -19046, -19005, -199...
$ DAYS_EMPLOYED              <int> -637, -1188, -225, -3039, -3038, -1...
$ DAYS_REGISTRATION          <dbl> -3648, -1186, -4260, -9833, -4311, ...
$ DAYS_ID_PUBLISH            <int> -2120, -291, -2531, -2437, -3458, -...
$ OWN_CAR_AGE                <dbl> NA, NA, 26, NA, NA, NA, 17, 8, NA, ...
$ FLAG_MOBIL                 <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ FLAG_EMP_PHONE             <int> 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,...
$ FLAG_WORK_PHONE            <int> 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,...
$ FLAG_CONT_MOBILE           <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ FLAG_PHONE                 <int> 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,...
$ FLAG_EMAIL                 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ OCCUPATION_TYPE            <chr> "Laborers", "Core staff", "Laborers...
$ CNT_FAM_MEMBERS            <dbl> 1, 2, 1, 2, 1, 2, 3, 2, 2, 1, 3, 2,...
$ REGION_RATING_CLIENT       <int> 2, 1, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2,...

데이터 변환 후

### 데이터 변환 후
x_train_processed_tbl %>%
    select(1:30) %>%
    glimpse()

Observations: 307,511
Variables: 30
$ SK_ID_CURR                 <int> 100002, 100003, 100004, 100006, 100...
$ NAME_CONTRACT_TYPE         <fct> Cash loans, Cash loans, Revolving l...
$ CODE_GENDER                <fct> M, F, M, F, M, M, F, M, F, M, F, F,...
$ FLAG_OWN_CAR               <fct> N, N, Y, N, N, N, Y, Y, N, N, N, N,...
$ FLAG_OWN_REALTY            <fct> Y, N, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y,...
$ CNT_CHILDREN               <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,...
$ AMT_INCOME_TOTAL           <dbl> 202500.00, 270000.00, 67500.00, 135...
$ AMT_CREDIT                 <dbl> 406597.5, 1293502.5, 135000.0, 3126...
$ AMT_ANNUITY                <dbl> 24700.5, 35698.5, 6750.0, 29686.5, ...
$ AMT_GOODS_PRICE            <dbl> 351000, 1129500, 135000, 297000, 51...
$ NAME_TYPE_SUITE            <fct> Unaccompanied, Family, Unaccompanie...
$ NAME_INCOME_TYPE           <fct> Working, State servant, Working, Wo...
$ NAME_EDUCATION_TYPE        <fct> Secondary / secondary special, High...
$ NAME_FAMILY_STATUS         <fct> Single / not married, Married, Sing...
$ NAME_HOUSING_TYPE          <fct> House / apartment, House / apartmen...
$ REGION_POPULATION_RELATIVE <dbl> 0.018801, 0.003541, 0.010032, 0.008...
$ DAYS_BIRTH                 <int> -9461, -16765, -19046, -19005, -199...
$ DAYS_EMPLOYED              <int> -637, -1188, -225, -3039, -3038, -1...
$ DAYS_REGISTRATION          <dbl> -3648, -1186, -4260, -9833, -4311, ...
$ DAYS_ID_PUBLISH            <int> -2120, -291, -2531, -2437, -3458, -...
$ OWN_CAR_AGE                <dbl> 12.06109, 12.06109, 26.00000, 12.06...
$ FLAG_MOBIL                 <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ FLAG_EMP_PHONE             <fct> 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,...
$ FLAG_WORK_PHONE            <fct> 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,...
$ FLAG_CONT_MOBILE           <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ FLAG_PHONE                 <fct> 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,...
$ FLAG_EMAIL                 <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ OCCUPATION_TYPE            <fct> Laborers, Core staff, Laborers, Lab...
$ CNT_FAM_MEMBERS            <dbl> 1, 2, 1, 2, 1, 2, 3, 2, 2, 1, 3, 2,...
$ REGION_RATING_CLIENT       <fct> 2, 1, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2,...

# 메모리 절약을 위해서 불필요해진 객체 제거
rm(rec_obj)
rm(x_train_tbl)
rm(y_train_tbl)

2.3 자동 기계학습

자동 기계학습 과정은 \(H_2O\)를 활용하기 때문에 h2o.init()으로 \(H_2O\) 클러스터를 띄워서 준비를 하고 훈련, 교차검증, 시험 데이터로 나눠 데이터를 준비하고 h2o.automl()함수를 호출하여 학습을 수행하여 최적 모형을 추출한다.

# 3. 모형 개발 ------
## 3.0. H2O 환경설정 -----
h2o.init()


H2O is not running yet, starting it now...

Note:  In case of errors look at the following log files:
    C:\Users\chongmu\AppData\Local\Temp\RtmpMLycSU/h2o_victorlee_started_from_r.out
    C:\Users\chongmu\AppData\Local\Temp\RtmpMLycSU/h2o_victorlee_started_from_r.err


Starting H2O JVM and connecting:  Connection successful!

R is connected to the H2O cluster: 
    H2O cluster uptime:         5 seconds 956 milliseconds 
    H2O cluster timezone:       Asia/Seoul 
    H2O data parsing timezone:  UTC 
    H2O cluster version:        3.20.0.2 
    H2O cluster version age:    1 month and 23 days  
    H2O cluster name:           H2O_started_from_R_victorlee_muv244 
    H2O cluster total nodes:    1 
    H2O cluster total memory:   3.54 GB 
    H2O cluster total cores:    4 
    H2O cluster allowed cores:  4 
    H2O cluster healthy:        TRUE 
    H2O Connection ip:          localhost 
    H2O Connection port:        54321 
    H2O Connection proxy:       NA 
    H2O Internal Security:      FALSE 
    H2O API Extensions:         Algos, AutoML, Core V3, Core V4 
    R Version:                  R version 3.5.1 (2018-07-02)

h2o.removeAll()

[1] 0

h2o.no_progress()

## 3.1. 훈련/시험 데이터 -----
data_h2o <- as.h2o(bind_cols(y_train_processed_tbl, x_train_processed_tbl))

splits_h2o <- h2o.splitFrame(data_h2o, ratios = c(0.5, 0.3), seed = 1234)

train_h2o <- splits_h2o[[1]]
valid_h2o <- splits_h2o[[2]]
test_h2o  <- splits_h2o[[3]]

## 3.2. H2O 모형 -----

y <- "TARGET"
x <- setdiff(names(train_h2o), y)

automl_models_h2o <- h2o.automl(
    x = x,
    y = y,
    training_frame    = train_h2o,
    validation_frame  = valid_h2o,
    leaderboard_frame = test_h2o,
    max_runtime_secs  = 10
)

2.4 자동 기계학습 모형성능

자동 기계학습된 automl_models_h2o 객체의 성능을 살펴본다.

## 3.3. H2O 모형 성능평가 -----
automl_leader <- automl_models_h2o@leader

performance_h2o <- h2o.performance(automl_leader, newdata = test_h2o)

performance_h2o %>%
    h2o.confusionMatrix()

# Confusion Matrix (vertical: actual; across: predicted)  for max f1 @ # threshold = 0.106174235650871:
#            0    1    Error         Rate
# 0      36250 6137 0.144785  =6137/42387
# 1       2347 1366 0.632103   =2347/3713
# Totals 38597 7503 0.184035  =8484/46100

performance_h2o %>%
    h2o.auc()

# 0.6962854

xwMOOC 모형

기계학습 모형개발 30분 - recipe + \(H_2 O\) AutoML

2018-08-08

1 기계학습 모형 개발 30분 ¹ ²

2 자동화 전략

2.1 데이터 가져오기

2.2 데이터 변환

2.3 자동 기계학습

2.4 자동 기계학습 모형성능

xwMOOC 모형

기계학습 모형개발 30분 - recipe + \(H_2 O\) AutoML

2018-08-08

1 기계학습 모형 개발 30분 1 2

2 자동화 전략

2.1 데이터 가져오기

2.2 데이터 변환

2.3 자동 기계학습

2.4 자동 기계학습 모형성능

1 기계학습 모형 개발 30분 ¹ ²