class: center, middle, inverse, title-slide # Tidyverse와 기계학습(ML) ## <br/> 데이터뽀개기 2018 - Hello Kaggler! ### 이광춘 <br/> (페북 그룹:Tidyverse Korea) ### 2018/10/07 --- class: inverse, middle, center # `Tidyverse` 기계학습 들어가며 --- background-image: url("fig/programming_machine_learning_comparison.png") background-size: 570px --- ### 예측모형 데이터프레임 데이터 랭글링(data wrangling)의 목적은 예측모형을 위한 데이터프레임 생성: **Analytic Basetable** .center[ <img src="fig/analytic-basetable.png" alt="데이터프레임" width="77%" /> ] --- ### 예측모형 데이터프레임 작업흐름 축적된 데이터를 통해서 예측변수와 목표변수를 활용하여 예측모형을 개발하여 예측변수를 입력값으로 하여 목표예측을 수행. .center[ <img src="fig/analytic-basetable-workflow.png" alt="데이터프레임 작업흐름" width="100%" /> ] --- background-image: url("fig/ds-gartner.png") background-size: 800px --- # 자동화된 기계학습 <br> <br> .center[ <img src="fig/automated-machine-learning.png" alt="자동화된 기계학습" width="97%" /> ] .footnote[ [xwMOOC 모형: 기계학습 - gapminer + rsample + purrr](https://statkclee.github.io/model/model-ml-purrr.html) ] --- background-image: url("fig/kaggle-tidyverse.png") background-size: 570px .footnote[ ##### `tidyverse` 2015년 버젼 - [xwMOOC 데이터 과학 -tidyverse 데이터 과학 기본체계](https://statkclee.github.io/data-science/ds-tidyverse.html) - [Tidyverse](https://www.tidyverse.org/) ] --- ### `tidyverse` 성명서(manifesto) 엉망진창인 R 도구상자(messyverse)와 비교하여 깔끔한 R 도구상자(tidyverse)는 깔끔한(tidy) API에 다음과 같은 4가지 원칙을 제시한다. - 기존 자료구조를 재사용: Reuse existing data structures. - 파이프 연산자로 간단한 함수를 조합: Compose simple functions with the pipe. - 함수형 프로그래밍을 적극 사용: Embrace functional programming. - 기계가 아닌 인간을 위한 설계: Design for humans. .footnote[ - [xwMOOC 데이터 과학 -tidyverse 데이터 과학 기본체계](https://statkclee.github.io/data-science/ds-tidyverse.html) - [Hadley Wickham(2017-11-13), "The tidy tools manifesto"](https://cran.r-project.org/web/packages/tidyverse/vignettes/manifesto.html) ] --- class: inverse, middle, center # 기계학습 데이터셋 --- ### `gapminder` Hans Rosling's 200 Countries, 200 Years, 4 Minutes - The Joy of Stats - BBC Four <iframe width="560" height="315" src="https://www.youtube.com/embed/jbkSRLYSojo" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe> ```r # 0. 환경설정 ----- library(tidyverse) library(dslabs) ``` --- ### 회귀모형 - `purrr` + `trelliscopejs` .footnote[ - [xwMOOC 모형: 회귀모형 - purrr + trelliscopejs](https://statkclee.github.io/model/model_purrr_trelliscopejs.html) ] --- ### `gapminder` 데이터셋 [`dslabs`](https://cran.r-project.org/web/packages/dslabs/index.html) 팩키지 내장 `gapminder` 데이터셋 10% 표본 추출 &rarr; 데이터 랭글링 <div id="htmlwidget-2ecf248d14826525b846" style="width:100%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-2ecf248d14826525b846">{"x":{"filter":"none","fillContainer":false,"data":[["Nigeria","Norway","Lithuania","Kiribati","Turkmenistan","Sudan","Montenegro","Djibouti","Algeria","Cuba","Zambia","Bulgaria","Lao","Nigeria","Jamaica","Dominican Republic","Liberia","Mauritania","Cuba","Swaziland","Turkey","Namibia","Ukraine","Suriname","Bahrain","Montenegro","Botswana","Maldives","Cote d'Ivoire","Micronesia, Fed. 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Africa","Southern Asia","Central America","Northern Europe","Caribbean","Northern Europe","Middle Africa","Northern Africa","Central America","Middle Africa","South-Eastern Asia","Northern Europe","Caribbean","Western Africa","Western Asia","Caribbean","Southern Europe","Western Asia","Western Europe","Central America","Northern America","Eastern Africa","Western Africa","Eastern Africa","Southern Europe","Eastern Europe","Middle Africa","Western Asia","Eastern Africa","Eastern Africa","Northern Europe","Central America","Southern Asia","Southern Asia","South-Eastern Asia","Central Asia","Eastern Europe","Micronesia","South-Eastern Asia","Eastern Africa","Western Asia","Eastern Africa","Southern Asia","Eastern Africa","Eastern Africa","South America","Eastern Europe","Central Asia","Western Asia","Eastern Africa","Eastern Asia","Eastern Europe","Northern Europe","Western Africa","Northern Africa","Polynesia","Northern Europe","Melanesia","Southern Asia","Western Europe","Southern Asia","Southern Asia","Northern Africa","Central Asia","Northern Africa","Central Asia","Southern Europe","Middle Africa","Western Europe","Northern Africa","Caribbean","Eastern Africa","Southern Europe","Eastern Europe","Central America","Eastern Africa","Melanesia","Southern Europe","Caribbean","Melanesia","Western Africa","Australia and New Zealand","Middle Africa","Northern Europe","Western Europe","Central Asia","Micronesia","Eastern Africa"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th>country<\/th>\n <th>year<\/th>\n <th>infant_mortality<\/th>\n <th>life_expectancy<\/th>\n <th>fertility<\/th>\n <th>population<\/th>\n <th>gdp<\/th>\n <th>continent<\/th>\n <th>region<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"scrollX":true,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6]}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script> --- ### `gapminder` 정제작업 결측값(NA)을 바탕으로 패턴을 추출하고, 1960 ~ 2011년까지 데이터를 예측모형 데이터로 활용함. ```r # 1. 데이터 정제 ----- gapminder_lc <- gapminder %>% group_by(country) %>% nest() gapminder_lc_df <- gapminder_lc %>% mutate(na_cnt = map_int(data, ~ sum(is.na(.x)))) %>% filter(na_cnt == 8) %>% unnest(data) %>% filter(year <= 2011) %>% select(-na_cnt) %>% select(-continent, -region) ``` --- ### 훈련/검증/시험 데이터셋 (코드) ```r library(rsample) ## 훈련/시험 데이터 분할 gapminder_split <- initial_split(gapminder_lc_df, prop = 0.70) train_df <- training(gapminder_split) test_df <- testing(gapminder_split) ## 훈련 데이터를 검증(Cross Validation) 데이터 분할 gapminder_cv_split <- vfold_cv(train_df, v = 5) cv_df <- gapminder_cv_split %>% mutate(train = map(splits, ~training(.x)), validate = map(splits, ~testing(.x))) cv_df ``` --- ### 훈련/검증/시험 데이터셋 ``` # 5-fold cross-validation # A tibble: 5 x 4 splits id train validate * <list> <chr> <list> <list> 1 <S3: rsplit> Fold1 <tibble [2,242 x 7]> <tibble [561 x 7]> 2 <S3: rsplit> Fold2 <tibble [2,242 x 7]> <tibble [561 x 7]> 3 <S3: rsplit> Fold3 <tibble [2,242 x 7]> <tibble [561 x 7]> 4 <S3: rsplit> Fold4 <tibble [2,243 x 7]> <tibble [560 x 7]> 5 <S3: rsplit> Fold5 <tibble [2,243 x 7]> <tibble [560 x 7]> ``` --- class: inverse, middle, center # `Tidyverse` 기계학습 --- ### many models .center[ <img src="fig/nested-dataframe-with-many-models.png" alt="many models" width="97%" /> ] .footnote[ [R 병렬 프로그래밍 - R 함수형 프로그래밍](https://statkclee.github.io/parallel-r/ds-fp-purrr.html) ] --- ### 회귀모형 적합: 맛보기 (코드) ```r library(broom); library(Metrics) # 회귀모형 적합 model_cv_df <- cv_df %>% mutate(lm_model = map(train, ~lm(life_expectancy ~ ., data=.x))) # 회귀모형 성능 평가 model_cv_df <- model_cv_df %>% mutate(valid_actual = map(validate, ~.x$life_expectancy), valid_pred = map2(lm_model, validate, ~predict(.x, .y))) %>% mutate(valid_mae = map2_dbl(valid_actual, valid_pred, ~mae(actual = .x, predicted = .y)), valid_rmse = map2_dbl(valid_actual, valid_pred, ~rmse(actual = .x, predicted = .y))) model_cv_df$valid_mae # model_cv_df$valid_rmse mean(model_cv_df$valid_mae) ``` --- ### 회귀모형 적합: 맛보기 ``` 1 2 3 4 5 1.519359 1.570984 1.509646 1.548877 1.543030 ``` ``` [1] 1.538379 ``` --- ### 예측모형 아키텍처 (코드) ```r library(broom); library(e1071);library(ranger);library(extrafont); loadfonts() # 회귀모형 적합 model_cv_df <- model_cv_df %>% mutate(lm_model = map(train, ~lm(life_expectancy ~ ., data=.x)), rf_model = map(train, ~ranger(life_expectancy ~ ., data=.x)), svm_model = map(train, ~svm(life_expectancy ~ ., data=.x, probability = TRUE))) # 회귀모형 성능평가 model_cv_df <- model_cv_df %>% mutate(valid_actual = map(validate, ~.x$life_expectancy), valid_lm_pred = map2(lm_model, validate, ~predict(.x, .y)), valid_rf_pred = map2(rf_model, validate, ~predict(.x, .y)$predictions), valid_svm_pred = map2(svm_model, validate, ~predict(.x, .y))) %>% mutate(valid_lm_mae = map2_dbl(valid_actual, valid_lm_pred, ~mae(actual = .x, predicted = .y)), valid_rf_mae = map2_dbl(valid_actual, valid_rf_pred, ~mae(actual = .x, predicted = .y)), valid_svm_mae = map2_dbl(valid_actual, valid_svm_pred, ~mae(actual = .x, predicted = .y))) ``` --- ### 예측모형 아키텍처 ``` # 5-fold cross-validation # A tibble: 5 x 17 splits id train validate lm_model valid_actual valid_pred valid_mae * <list> <chr> <list> <list> <list> <list> <list> <dbl> 1 <S3: r~ Fold1 <tibb~ <tibble~ <S3: lm> <dbl [561]> <dbl [561~ 1.52 2 <S3: r~ Fold2 <tibb~ <tibble~ <S3: lm> <dbl [561]> <dbl [561~ 1.57 3 <S3: r~ Fold3 <tibb~ <tibble~ <S3: lm> <dbl [561]> <dbl [561~ 1.51 4 <S3: r~ Fold4 <tibb~ <tibble~ <S3: lm> <dbl [560]> <dbl [560~ 1.55 5 <S3: r~ Fold5 <tibb~ <tibble~ <S3: lm> <dbl [560]> <dbl [560~ 1.54 # ... with 9 more variables: valid_rmse <dbl>, rf_model <list>, # svm_model <list>, valid_lm_pred <list>, valid_rf_pred <list>, # valid_svm_pred <list>, valid_lm_mae <dbl>, valid_rf_mae <dbl>, # valid_svm_mae <dbl> ``` --- ### 예측모형 아키텍처 성능 비교 (코드) ```r model_df <- data.frame(LM = model_cv_df$valid_lm_mae, RF = model_cv_df$valid_rf_mae, SVM = model_cv_df$valid_svm_mae) model_df %>% gather(model, MAE) %>% ggplot(aes(x=model, y=MAE, color=model)) + geom_point(size=3) + labs(x="예측모형", y="MAE (Mean Absolute Error)", color="예측모형", title="GAPMINDER 데이터 - 기대수명 예측모형") ``` --- ### 예측모형 아키텍처 성능 비교 <img src="machine_learning_tidyverse_20181007_files/figure-html/gapminder-pm-architecture-comp-1.png" style="display: block; margin: auto;" /> --- ### 예측모형 튜닝 (코드) ```r # Random Forest 모형적합 model_cv_df <- model_cv_df %>% crossing(mtry = c(2,ceiling(sqrt(ncol(gapminder_lc_df)-2)),5), num.trees = c(500, 1000)) %>% mutate(rf_tune_model = pmap(list(train, mtry, num.trees), ~ranger(life_expectancy ~ ., data=.x, mtry=.y))) # RandomForest 성능평가 model_cv_df <- model_cv_df %>% mutate(valid_actual = map(validate, ~.x$life_expectancy), valid_rf_tune_pred = map2(rf_tune_model, validate, ~predict(.x, .y)$predictions)) %>% mutate(valid_rf_tune_mae = map2_dbl(valid_actual, valid_rf_tune_pred, ~mae(actual = .x, predicted = .y))) model_cv_df %>% group_by(mtry, num.trees) %>% summarise(mean_mae = mean(valid_rf_tune_mae)) ``` --- ### 예측모형 튜닝 ``` # A tibble: 6 x 3 # Groups: mtry [?] mtry num.trees mean_mae <dbl> <dbl> <dbl> 1 2 500 0.862 2 2 1000 0.868 3 3 500 0.868 4 3 1000 0.871 5 5 500 0.879 6 5 1000 0.876 ``` --- ### 예측 성능: 시험데이터 (코드) ```r gapminder_pm <- ranger(life_expectancy ~ ., data = train_df, mtry = 3, num.trees = 500) test_df$pred <- predict(gapminder_pm, test_df)$predictions test_df %>% mutate(absolute_err = abs(life_expectancy-pred)) %>% summarise(mae = mean(absolute_err)) ``` --- ### 예측 성능: 시험데이터 ``` # A tibble: 1 x 1 mae <dbl> 1 0.766 ``` --- class: inverse, middle, center # 열심히 한 캐글 그리고 ... --- ### Tidyverse와 A/B 검정 .center[ <img src="fig/ab-testing-tidyverse.png" alt="tidyverse" width="100%" /> ] .footnote[ - [데이터 과학 - tidyverse 데이터 과학 기본체계](https://statkclee.github.io/data-science/ds-tidyverse.html) - [Tidyverse와 함께 하는 A/B 테스팅](https://statkclee.github.io/ds-authoring/ab_testing_tidyverse_20181007.html#1) ] --- class: middle, center # [`Tidyverse Korea` 페북 그룹](https://www.facebook.com/groups/1404219106509417/) .footnote[ <https://www.facebook.com/groups/tidyverse/> ] --- class: inverse, middle, center # 혹시... 시간이 남으면... --- ### 직사각형 모형데이터를 넘어 - 지리정보: [xwMOOC 모형 - 나무모형과 지리정보 만남 - 택시](https://statkclee.github.io/model/model_geospatial_taxi.html) - 자연어: [xwMOOC 자연어 처리 - 텍스트 SMS 스팸분류 - Random Forest](https://statkclee.github.io/text/nlp-spam-machine-learning.html) - 이미지: [xwMOOC 딥러닝 - R 케라스(keras), 패션 MNIST](https://statkclee.github.io/deep-learning/r-keras-fashion-mnist.html) - 추천: [스파크 + MovieLens 데이터](https://statkclee.github.io/parallel-r/recommendation-sparklyr.html) - 소리 - ...