Last updated: 2021-10-11

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Rmd a034f06 opus1993 2021-10-11 adopt common ggplot theming

SLICED is like the TV Show Chopped but for data science. Competitors get a never-before-seen dataset and two-hours to code a solution to a prediction challenge. Contestants get points for the best model plus bonus points for data visualization, votes from the audience, and more.

Season 1 Episode 5 featured a challenge to predict AirBnb pricing for properties in New York City. The evaluation metric in this competition is residual mean log squared error.

To make the best use of the resources that we have, we will explore the data set features to select those with the most predictive power, build a random forest to confirm the recipe, and then use the rest of the time to build one or more catboost ensemble models. Let’s load up some packages:

suppressPackageStartupMessages({
library(tidyverse)
library(hrbrthemes)
library(lubridate)
library(tidytext)

library(tidymodels)
library(textrecipes)
library(treesnip)
library(finetune)
library(stacks)
library(themis)
library(baguette)
  
library(catboost)

})

source(here::here("code","_common.R"),
       verbose = FALSE,
       local = knitr::knit_global())

ggplot2::theme_set(theme_jim(base_size = 12))

#create a data directory
data_dir <- here::here("data",Sys.Date())
if (!file.exists(data_dir)) dir.create(data_dir)

# set a competition metric
mset <- metric_set(mn_log_loss)

# set the competition name from the web address
competition_name <- "sliced-s01e05-WXx7h8"

zipfile <- paste0(data_dir,"/", competition_name, ".zip")

path_export <- here::here("data",Sys.Date(),paste0(competition_name,".csv"))

A quick reminder before downloading the dataset: Go to the web site and accept the competition terms!!!

Import and EDA

I was out on holiday this past week and missed the live competition. This file is a compilation of some of the best parts of what the contestants demonstrated (live coding from scratch) and Julia Silge’s custom metric blog post.

We have basic shell commands available to interact with Kaggle here:

# from the Kaggle api https://github.com/Kaggle/kaggle-api

# the leaderboard
shell(glue::glue("kaggle competitions leaderboard { competition_name } -s"))

# the files to download
shell(glue::glue("kaggle competitions files -c { competition_name }"))

# the command to download files
shell(glue::glue("kaggle competitions download -c { competition_name } -p { data_dir }"))

# unzip the files received
shell(glue::glue("unzip { zipfile } -d { data_dir }"))

Reading in the contents of the datafiles here:

train_df <-
  read_csv(file = glue::glue(
    {
      data_dir
    },
    "/train.csv"
  )) %>%
  mutate(across(c(id, host_id), as.character)) %>%
  mutate(across(
    c(host_name, neighbourhood_group, neighbourhood, room_type),
    as.factor
  )) %>%
  mutate(price = log10(price + 1))

test_df <-
  read_csv(file = glue::glue(
    {
      data_dir
    },
    "/test.csv"
  )) %>%
  mutate(across(c(id, host_id), as.character)) %>%
  mutate_if(is.character, as.factor)

Some questions to answer here: What features have missing data, and imputations may be required? What does the outcome variable look like, in terms of imbalance?

skimr::skim(train_df)

Outcome variable log10(price) has a mean of 2.06 and a range between 0 and 4. Numerical feature reviews_per_month and date last_review are missing about 20% of the time in training data. Categorical variable neighbourhood has 217 levels.

summarize_prices <- function(tbl) {
  tbl %>%
    summarize(
      median_price = 10^median(price) - 1,
      n = n(),
      mean_price = 10^mean(price) - 1
    ) %>%
    arrange(desc(n))
}

train_df %>%
  group_by(neighbourhood_group = withfreq(neighbourhood_group)) %>%
  ggplot(aes(10^price,
    neighbourhood_group,
    color = neighbourhood_group
  )) +
  ggdist::stat_dots(
    aes(fill = neighbourhood_group),
    side = "top",
    alpha = 0.2,
    justification = -0.1,
    binwidth = 0.03,
    dotsize = .5,
    stackratio = .1,
    normalize = "groups",
    show.legend = FALSE
  ) +
  geom_boxplot(
    width = 0.1,
    outlier.shape = NA,
    show.legend = FALSE
  ) +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(y = NULL, x = "Price", title = "NYC Airbnb by Neighborhood Groups") +
  theme(panel.grid.major.y = element_blank())

train_df %>%
  mutate(
    host_id = fct_lump(host_id, 30),
    host_id = fct_reorder(host_id, price)
  ) %>%
  group_by(host_id = withfreq(host_id)) %>%
  ggplot(aes(10^price, host_id)) +
  geom_boxplot(show.legend = FALSE) +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(y = NULL, x = "Price", title = "NYC Airbnb by Host IDs")

train_df %>%
  mutate(room_type = fct_reorder(room_type, price)) %>%
  group_by(room_type) %>%
  ggplot(aes(10^price,
    room_type,
    color = room_type
  )) +
  ggdist::stat_dots(
    aes(fill = room_type),
    side = "top",
    alpha = 0.2,
    justification = -0.1,
    binwidth = 0.03,
    dotsize = .5,
    stackratio = .08,
    normalize = "groups",
    show.legend = FALSE
  ) +
  geom_boxplot(
    width = 0.1,
    outlier.shape = NA,
    show.legend = FALSE
  ) +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(y = NULL, x = "Price", title = "NYC Airbnb by Room Type")

train_df %>%
  unnest_tokens(word, name) %>%
  anti_join(stop_words) %>%
  group_by(word) %>%
  summarize_prices() %>%
  head(30) %>%
  mutate(word = fct_reorder(word, mean_price)) %>%
  ggplot(aes(mean_price, word, size = n)) +
  geom_point() +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(size = "Number of Listings", title = "NYC AirBNB Property Description NLP", y = NULL) +
  theme(
    legend.position = c(0.8, 0.3),
    legend.background = element_rect(color = "white")
  )

train_df %>%
  group_by(neighbourhood) %>%
  summarize_prices() %>%
  arrange(-median_price)
ABCDEFGHIJ0123456789
neighbourhood
<fct>
median_price
<dbl>
n
<int>
mean_price
<dbl>
Woodrow700.000001700.00000
Tribeca305.00000128335.38020
Neponsit264.614382264.61438
Flatiron District255.0000055267.77228
NoHo250.0000053254.44736
Breezy Point220.801712220.80171
Midtown219.000001113230.03264
Financial District204.49939538200.08540
West Village200.00000525216.93939
Chelsea199.00000764192.39307
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The outcome variable itself is skewed across all observations in the training data, as prices often are.

train_df %>%
  ggplot(aes(10^price + 1, fill = neighbourhood_group)) +
  geom_histogram(
    position = "identity",
    alpha = 0.5,
    bins = 40
  ) +
  scale_x_log10(
    labels = scales::dollar_format(accuracy = 1),
    breaks = c(5, 10, 100, 1000)
  ) +
  labs(
    fill = NULL, x = "price per night",
    title = "NYC Airbnb"
  ) +
  theme(
    legend.position = c(0.8, 0.5),
    legend.background = element_rect(color = "white")
  )

Let’s explore the relationships between the features and the numeric outcome.

A plot of the time series last_review by year and the corresponding ranges of price

train_df %>%
  group_by(year = year(last_review)) %>%
  summarize(
    mean_price = mean(10^price - 1),
    n = n(),
    low = quantile(10^price - 1, probs = 0.25, na.rm = TRUE),
    high = quantile(10^price - 1, probs = 0.75, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  filter(!is.na(year)) %>%
  ggplot(aes(year, mean_price)) +
  geom_line() +
  geom_point(aes(size = n)) +
  geom_ribbon(aes(ymin = low, ymax = high), alpha = .1) +
  expand_limits(y = 0) +
  scale_x_continuous(breaks = seq(2011, 2017, 2)) +
  scale_y_continuous(labels = scales::dollar_format(accuracy = 1)) +
  labs(
    x = "Year", y = "Mean Price and IQR",
    size = "Reviews", title = "NYC Airbnb"
  ) +
  theme(
    legend.position = c(0.85, 0.2),
    legend.background = element_rect(color = "white")
  )

Histograms of the distributions of each numeric feature:

train_numeric <- train_df %>%
  keep(is.numeric) %>%
  colnames()

chart <- c(train_numeric)

train_df %>%
  select_at(vars(all_of(chart))) %>%
  select(-price) %>%
  pivot_longer(
    cols = everything(),
    names_to = "key",
    values_to = "value"
  ) %>%
  filter(!is.na(value)) %>%
  ggplot() +
  geom_histogram(
    mapping = aes(x = value, fill = key),
    bins = 50, show.legend = FALSE
  ) +
  facet_wrap(~key, scales = "free", ncol = 3) +
  labs(title = "NYC Airbnb Numeric Feature Histograms")

And the outcome variable by each numeric feature:

train_df %>%
  select_at(vars(all_of(chart))) %>%
  pivot_longer(
    cols = -price,
    names_to = "key",
    values_to = "value"
  ) %>%
  filter(!is.na(value)) %>%
  ggplot() +
  geom_point(
    mapping = aes(x = value, y = 10^price + 1),
    alpha = 0.1,
    shape = 20,
    show.legend = FALSE
  ) +
  geom_smooth(aes(
    x = value,
    y = 10^price + 1,
    color = key
  ),
  method = "gam", show.legend = FALSE
  ) +
  facet_wrap(~key, scales = "free", ncol = 3) +
  scale_y_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(x = NULL, y = "Price", title = "NYC Airbnb")

Adapted from Julia Silge’s demonstration

usa_data <- map_data("usa")

train_df %>%
  ggplot(aes(longitude, latitude, z = price)) +
  stat_summary_hex(bins = 100) +
  labs(fill = "Mean price(log10)") +
  cowplot::theme_map() +
  labs(title = "NYC Airbnb Price Ranges")

theme(
  legend.position = c(0.1, 0.8),
  legend.background = element_rect(color = "white")
)
List of 2
 $ legend.background:List of 5
  ..$ fill         : NULL
  ..$ colour       : chr "white"
  ..$ size         : NULL
  ..$ linetype     : NULL
  ..$ inherit.blank: logi FALSE
  ..- attr(*, "class")= chr [1:2] "element_rect" "element"
 $ legend.position  : num [1:2] 0.1 0.8
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi FALSE
 - attr(*, "validate")= logi TRUE
cr1 <- train_df %>%
  select(-price) %>%
  keep(is.numeric) %>%
  cor(use = "pair")

corrplot::corrplot(cr1, type = "upper")

Conclusions:

Preprocessiong

I am going to train on almost all of the train data. The 1% left is a quick confirmation of validity. The provided file labeled test has no labels.

set.seed(2021)
split <- train_df %>%
  initial_split(
    strata = price,
    prop = .99
  )
train <- training(split)
valid <- testing(split)

There are only 344 held out from training as a last check before submission.

basic_rec <-
  recipe(price ~ ., train) %>%
  # ---- set aside the row id's
  update_role(id, host_name, new_role = "id") %>%
  step_novel(neighbourhood) %>%
  step_other(neighbourhood, threshold = 0.01) %>%
  step_novel(host_id) %>%
  step_other(host_id, threshold = 0.01) %>%
  step_tokenize(name) %>%
  step_stopwords(name) %>%
  step_texthash(name, num_terms = 16) %>%
  step_indicate_na(last_review, reviews_per_month) %>%
  step_mutate(last_review = if_else(is.na(last_review),
    min(train$last_review, na.rm = TRUE), last_review
  )) %>%
  step_holiday(last_review) %>%
  step_date(last_review,
    features = "year",
    keep_original_cols = FALSE
  ) %>%
  step_ns(longitude, latitude, deg_free = 4) %>%
  step_normalize(all_numeric_predictors()) %>%
  step_nzv(all_predictors())
basic_rec %>%
  #  finalize_recipe(list(num_comp = 2)) %>%
  prep() %>%
  juice()
ABCDEFGHIJ0123456789
name_hash01
<dbl>
name_hash02
<dbl>
name_hash03
<dbl>
name_hash04
<dbl>
name_hash05
<dbl>
name_hash06
<dbl>
name_hash07
<dbl>
name_hash08
<dbl>
-0.24802552.3659184-0.7355386-0.0020337080.006263972-0.059547240.1912205-0.0847194
1.95417590.2280928-0.7355386-0.0020337080.006263972-0.059547240.1912205-2.2381047
1.95417590.22809280.5180589-0.0020337082.338526663-2.450044580.1912205-0.0847194
-0.24802550.22809280.5180589-0.0020337080.006263972-0.059547240.1912205-0.0847194
-0.24802550.22809280.5180589-0.0020337080.006263972-2.450044580.1912205-0.0847194
-0.2480255-1.90973280.5180589-0.0020337082.338526663-0.059547240.19122054.2220511
1.95417590.22809280.51805893.8260823270.006263972-0.059547240.1912205-0.0847194
-0.24802550.2280928-0.7355386-0.0020337080.006263972-0.059547240.1912205-0.0847194
-0.2480255-1.9097328-0.7355386-0.0020337080.006263972-0.059547240.1912205-0.0847194
-0.24802550.22809280.5180589-0.0020337080.006263972-0.059547240.1912205-0.0847194
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We will use 5-fold cross validation and stratify between the rain and no-rain classes.

train_folds <- vfold_cv(
  data = train,
  strata = price,
  v = 5
)

Machine Learning

We will build a specification for simple shallow random forest and a specification for catboost.

catboost_spec <- boost_tree(
  trees = 1000,
  min_n = tune(),
  learn_rate = tune(),
  tree_depth = tune()
) %>%
  set_engine("catboost") %>%
  set_mode("regression")

bag_spec <-
  bag_tree(min_n = 10) %>%
  set_engine("rpart", times = 50) %>%
  set_mode("regression")

The yardstick default RMSE is on the log of price, not RMSLE on price. This will create a custom function to track RMSLE:

library(rlang)

rmsle_vec <- function(truth, estimate, na_rm = TRUE, ...) {
  rmsle_impl <- function(truth, estimate) {
    sqrt(mean((log(truth + 1) - log(estimate + 1))^2))
  }

  metric_vec_template(
    metric_impl = rmsle_impl,
    truth = truth,
    estimate = estimate,
    na_rm = na_rm,
    cls = "numeric",
    ...
  )
}

rmsle <- function(data, ...) {
  UseMethod("rmsle")
}

rmsle <- new_numeric_metric(rmsle, direction = "minimize")

rmsle.data.frame <- function(data, truth, estimate, na_rm = TRUE, ...) {
  metric_summarizer(
    metric_nm = "rmsle",
    metric_fn = rmsle_vec,
    data = data,
    truth = !!enquo(truth),
    estimate = !!enquo(estimate),
    na_rm = na_rm,
    ...
  )
}

mset <- metric_set(rmsle)

To speed up computation we will use a parallel backend.

all_cores <- parallelly::availableCores(omit = 1)
all_cores
system 
    11 
future::plan("multisession", workers = all_cores) # on Windows

Lets make a cursory check of the recipe and variable importance, which comes out of rpart for free. This workflow also handles factors without dummies.

bag_wf <-
  workflow() %>%
  add_recipe(basic_rec) %>%
  add_model(bag_spec)

set.seed(123)
bag_fit <- parsnip::fit(bag_wf, data = train)

extract_fit_parsnip(bag_fit)$fit$imp %>%
  mutate(term = fct_reorder(term, value)) %>%
  ggplot(aes(value, term)) +
  geom_point() +
  geom_errorbarh(aes(
    xmin = value - `std.error` / 2,
    xmax = value + `std.error` / 2
  ),
  height = .3
  ) +
  labs(
    title = "Feature Importance",
    x = NULL, y = NULL
  )

We see that room_type and the geographical information will be very important for this model.

Now that we have some confidence that the features have predictive power, lets tune up a set of catboost models.

catboost_params <-
  dials::parameters(
    min_n(), # min data in leaf
    tree_depth(range = c(4, 15)),
    learn_rate(
      range = c(-3, -0.7),
      trans = log10_trans()
    )
  )

cbst_grid <- dials::grid_max_entropy(catboost_params,
  size = 40
)
cbst_grid
ABCDEFGHIJ0123456789
min_n
<int>
tree_depth
<int>
learn_rate
<dbl>
5140.008347083
11110.132580588
2750.001677371
1260.113515427
2250.009718247
37130.009451095
3110.052376601
3680.198293765
840.005399231
23120.001388250
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cv_res_catboost <-
  workflow() %>%
  add_recipe(basic_rec) %>%
  add_model(catboost_spec) %>%
  tune_grid(
    resamples = train_folds,
    grid = cbst_grid,
    control = control_race(
      verbose = FALSE,
      save_pred = TRUE,
      save_workflow = TRUE,
      extract = extract_model,
      parallel_over = "resamples"
    ),
    metrics = mset
  )
autoplot(cv_res_catboost)

show_best(cv_res_catboost) %>%
  select(-.estimator)
ABCDEFGHIJ0123456789
min_n
<int>
tree_depth
<int>
learn_rate
<dbl>
.metric
<chr>
mean
<dbl>
n
<int>
std_err
<dbl>
.config
<chr>
2390.1883862rmsle0.0606115650.0005664685Preprocessor1_Model35
790.1655137rmsle0.0607264850.0005829796Preprocessor1_Model17
11110.1325806rmsle0.0607353950.0006244535Preprocessor1_Model02
3680.1982938rmsle0.0607478450.0006251578Preprocessor1_Model08
13140.1300412rmsle0.0607503450.0006326557Preprocessor1_Model38
5 rows
cat_wf_best <-
  workflow() %>%
  add_recipe(basic_rec) %>%
  add_model(catboost_spec) %>%
  finalize_workflow(select_best(cv_res_catboost))

cat_fit_best <- cat_wf_best %>%
  parsnip::fit(data = train)

Catboost model performance

predict(cat_fit_best, new_data = valid) %>%
  cbind(valid) %>%
  ggplot(aes(10^price + 1,
    10^.pred + 1,
    color = neighbourhood_group
  )) +
  geom_abline(
    slope = 1,
    lty = 2,
    color = "gray50",
    alpha = 0.5
  ) +
  geom_point(alpha = 0.8, shape = 21) +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  scale_y_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(
    color = NULL,
    x = "True price",
    y = "Predicted price"
  )

predict(cat_fit_best, new_data = valid) %>%
  cbind(valid) %>%
  rmsle(10^price - 1, 10^.pred - 1)
ABCDEFGHIJ0123456789
.metric
<chr>
.estimator
<chr>
.estimate
<dbl>
rmslestandard0.3922326
1 row

This catboost figure is somewhat better than the leaderboard RMSLE of 0.40758, so it would have been worthy of submission.

Lastly, we build the submission file

bind_cols(predict(cat_fit_best, test_df), test_df) %>%
  select(id, price = .pred) %>%
  write_csv(file = path_export)

and make the submission to the Kaggle board

shell(glue::glue('kaggle competitions submit -c { competition_name } -f { path_export } -m "Catboosted"'))

sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] rlang_0.4.11        catboost_0.26       baguette_0.1.1     
 [4] themis_0.1.4        stacks_0.2.1        finetune_0.1.0     
 [7] treesnip_0.1.0.9000 textrecipes_0.4.1   yardstick_0.0.8    
[10] workflowsets_0.1.0  workflows_0.2.3     tune_0.1.6         
[13] rsample_0.1.0       recipes_0.1.17      parsnip_0.1.7.900  
[16] modeldata_0.1.1     infer_1.0.0         dials_0.0.10       
[19] scales_1.1.1        broom_0.7.9         tidymodels_0.1.4   
[22] tidytext_0.3.2      lubridate_1.7.10    hrbrthemes_0.8.0   
[25] forcats_0.5.1       stringr_1.4.0       dplyr_1.0.7        
[28] purrr_0.3.4         readr_2.0.2         tidyr_1.1.4        
[31] tibble_3.1.4        ggplot2_3.3.5       tidyverse_1.3.1    
[34] workflowr_1.6.2    

loaded via a namespace (and not attached):
  [1] utf8_1.2.2           R.utils_2.11.0       tidyselect_1.1.1    
  [4] grid_4.1.1           pROC_1.18.0          munsell_0.5.0       
  [7] codetools_0.2-18     ragg_1.1.3           future_1.22.1       
 [10] withr_2.4.2          colorspace_2.0-2     highr_0.9           
 [13] knitr_1.36           rstudioapi_0.13      Rttf2pt1_1.3.8      
 [16] listenv_0.8.0        labeling_0.4.2       git2r_0.28.0        
 [19] TeachingDemos_2.12   lgr_0.4.3            farver_2.1.0        
 [22] bit64_4.0.5          DiceDesign_1.9       rprojroot_2.0.2     
 [25] mlr_2.19.0           parallelly_1.28.1    vctrs_0.3.8         
 [28] generics_0.1.0       float_0.2-6          ipred_0.9-12        
 [31] xfun_0.26            R6_2.5.1             doParallel_1.0.16   
 [34] lhs_1.1.3            cachem_1.0.6         assertthat_0.2.1    
 [37] vroom_1.5.5          promises_1.2.0.1     nnet_7.3-16         
 [40] gtable_0.3.0         Cubist_0.3.0         globals_0.14.0      
 [43] timeDate_3043.102    BBmisc_1.11          systemfonts_1.0.2   
 [46] text2vec_0.6         splines_4.1.1        extrafontdb_1.0     
 [49] butcher_0.1.5        stopwords_2.2        hexbin_1.28.2       
 [52] earth_5.3.1          checkmate_2.0.0      yaml_2.2.1          
 [55] reshape2_1.4.4       modelr_0.1.8         backports_1.2.1     
 [58] httpuv_1.6.3         tokenizers_0.2.1     extrafont_0.17      
 [61] inum_1.0-4           tools_4.1.1          lava_1.6.10         
 [64] usethis_2.0.1        ellipsis_0.3.2       jquerylib_0.1.4     
 [67] Rcpp_1.0.7           plyr_1.8.6           parallelMap_1.5.1   
 [70] rpart_4.1-15         ParamHelpers_1.14    viridis_0.6.1       
 [73] cowplot_1.1.1        haven_2.4.3          fs_1.5.0            
 [76] here_1.0.1           furrr_0.2.3          unbalanced_2.0      
 [79] magrittr_2.0.1       data.table_1.14.2    ggdist_3.0.0        
 [82] reprex_2.0.1         RANN_2.6.1           GPfit_1.0-8         
 [85] mlapi_0.1.0          mvtnorm_1.1-2        SnowballC_0.7.0     
 [88] whisker_0.4          ROSE_0.0-4           R.cache_0.15.0      
 [91] hms_1.1.1            evaluate_0.14        RhpcBLASctl_0.21-247
 [94] readxl_1.3.1         gridExtra_2.3        compiler_4.1.1      
 [97] maps_3.4.0           crayon_1.4.1         R.oo_1.24.0         
[100] htmltools_0.5.2      mgcv_1.8-36          later_1.3.0         
[103] tzdb_0.1.2           Formula_1.2-4        libcoin_1.0-9       
[106] DBI_1.1.1            corrplot_0.90        dbplyr_2.1.1        
[109] MASS_7.3-54          Matrix_1.3-4         cli_3.0.1           
[112] C50_0.1.5            R.methodsS3_1.8.1    parallel_4.1.1      
[115] gower_0.2.2          pkgconfig_2.0.3      rsparse_0.4.0       
[118] xml2_1.3.2           foreach_1.5.1        bslib_0.3.0         
[121] hardhat_0.1.6        plotmo_3.6.1         prodlim_2019.11.13  
[124] rvest_1.0.1          distributional_0.2.2 janeaustenr_0.1.5   
[127] digest_0.6.28        rmarkdown_2.11       cellranger_1.1.0    
[130] fastmatch_1.1-3      gdtools_0.2.3        nlme_3.1-152        
[133] lifecycle_1.0.1      jsonlite_1.7.2       viridisLite_0.4.0   
[136] fansi_0.5.0          pillar_1.6.3         lattice_0.20-44     
[139] fastmap_1.1.0        httr_1.4.2           plotrix_3.8-2       
[142] survival_3.2-11      glue_1.4.2           conflicted_1.0.4    
[145] FNN_1.1.3            iterators_1.0.13     bit_4.0.4           
[148] class_7.3-19         stringi_1.7.5        sass_0.4.0          
[151] rematch2_2.1.2       textshaping_0.3.5    partykit_1.2-15     
[154] styler_1.6.2         future.apply_1.8.1
---
title: "Sliced NYC Airbnb"
author: "Jim Gruman"
date: "June 29, 2021"
output:
  workflowr::wflow_html:
    toc: no
    code_folding: hide
    code_download: true
    df_print: paged
editor_options:
  chunk_output_type: console
---

[SLICED](https://www.notion.so/SLICED-Show-c7bd26356e3a42279e2dfbafb0480073) is like the TV Show Chopped but for data science. Competitors get a never-before-seen dataset and two-hours to code a solution to a prediction challenge. Contestants get points for the best model plus bonus points for data visualization, votes from the audience, and more.
 
[Season 1 Episode 5](https://www.kaggle.com/c/sliced-s01e05-WXx7h8/data) featured a challenge to predict AirBnb pricing for properties in New York City. The evaluation metric in this competition is residual mean log squared error.

![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2F7f7ba5f9-d7bd-4101-8933-a112b4f78570%2FFrame_3.png?table=block&id=c7bd2635-6e3a-4227-9e2d-fbafb0480073&spaceId=2cc404e6-fe20-483d-9ea5-5d44eb3dd586&width=1510&userId=&cache=v2)

To make the best use of the resources that we have, we will explore the data set features to select those with the most predictive power, build a random forest to confirm the recipe, and then use the rest of the time to build one or more catboost ensemble models. 
Let's load up some packages:

```{r setup}

suppressPackageStartupMessages({
library(tidyverse)
library(hrbrthemes)
library(lubridate)
library(tidytext)

library(tidymodels)
library(textrecipes)
library(treesnip)
library(finetune)
library(stacks)
library(themis)
library(baguette)
  
library(catboost)

})

source(here::here("code","_common.R"),
       verbose = FALSE,
       local = knitr::knit_global())

ggplot2::theme_set(theme_jim(base_size = 12))

#create a data directory
data_dir <- here::here("data",Sys.Date())
if (!file.exists(data_dir)) dir.create(data_dir)

# set a competition metric
mset <- metric_set(mn_log_loss)

# set the competition name from the web address
competition_name <- "sliced-s01e05-WXx7h8"

zipfile <- paste0(data_dir,"/", competition_name, ".zip")

path_export <- here::here("data",Sys.Date(),paste0(competition_name,".csv"))
```

A quick reminder before downloading the dataset:  Go to the web site and accept the competition terms!!!

# Import and EDA {.tabset}

I was out on holiday this past week and missed the live competition. This file is a compilation of some of the best parts of what the contestants demonstrated (live coding from scratch) and Julia Silge's [custom metric blog post](https://juliasilge.com/blog/nyc-airbnb/).

## Direct Import and Skim

We have basic shell commands available to interact with Kaggle here:

```{r kaggle competitions terminal commands, eval=FALSE}
# from the Kaggle api https://github.com/Kaggle/kaggle-api

# the leaderboard
shell(glue::glue('kaggle competitions leaderboard { competition_name } -s'))

# the files to download
shell(glue::glue('kaggle competitions files -c { competition_name }'))

# the command to download files
shell(glue::glue('kaggle competitions download -c { competition_name } -p { data_dir }'))

# unzip the files received
shell(glue::glue('unzip { zipfile } -d { data_dir }'))

```

Reading in the contents of the datafiles here:

```{r}
train_df <-
  read_csv(file = glue::glue({
    data_dir
  }, "/train.csv")) %>%
  mutate(across(c(id, host_id), as.character)) %>%
  mutate(across(
    c(host_name, neighbourhood_group, neighbourhood, room_type),
    as.factor
  )) %>%
  mutate(price = log10(price + 1))

test_df <-
  read_csv(file = glue::glue({
    data_dir
  }, "/test.csv")) %>%
  mutate(across(c(id, host_id), as.character)) %>%
  mutate_if(is.character, as.factor) 

```

Some questions to answer here:
What features have missing data, and imputations may be required?
What does the outcome variable look like, in terms of imbalance?

```{r, eval=FALSE}
skimr::skim(train_df)
```

Outcome variable `log10(price)` has a mean of 2.06 and a range between 0 and 4. Numerical feature `reviews_per_month` and date `last_review` are missing about 20% of the time in training data. Categorical variable `neighbourhood` has 217 levels.

## Categorical Feature Plots

```{r, fig.asp=1.2}
summarize_prices <- function(tbl){
  tbl %>% 
    summarize(median_price = 10^median(price) - 1,
              n = n(),
              mean_price = 10^mean(price) - 1) %>% 
    arrange(desc(n))
}

train_df %>%
  group_by(neighbourhood_group = withfreq(neighbourhood_group)) %>%
  ggplot(aes(10 ^ price, 
             neighbourhood_group, 
             color = neighbourhood_group)) +
  ggdist::stat_dots(
    aes(fill = neighbourhood_group),
    side = "top",
    alpha = 0.2,
    justification = -0.1,
    binwidth = 0.03,
    dotsize = .5,
    stackratio = .1,
    normalize = "groups",
    show.legend = FALSE
  ) +
  geom_boxplot(
    width = 0.1,
    outlier.shape = NA,
    show.legend = FALSE
  ) +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(y = NULL, x = "Price", title = "NYC Airbnb by Neighborhood Groups") +
  theme(panel.grid.major.y = element_blank())

train_df %>%
  mutate(host_id = fct_lump(host_id, 30),
         host_id = fct_reorder(host_id, price)) %>%
  group_by(host_id = withfreq(host_id)) %>%
  ggplot(aes(10 ^ price , host_id)) +
  geom_boxplot(show.legend = FALSE) +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(y = NULL, x = "Price", title = "NYC Airbnb by Host IDs")

train_df %>%
  mutate(room_type = fct_reorder(room_type, price)) %>%
  group_by(room_type) %>%
  ggplot(aes(10 ^ price, 
             room_type,
             color = room_type)) +
  ggdist::stat_dots(
    aes(fill = room_type),
    side = "top",
    alpha = 0.2,
    justification = -0.1,
    binwidth = 0.03,
    dotsize = .5,
    stackratio = .08,
    normalize = "groups",
    show.legend = FALSE
  ) +
  geom_boxplot(
    width = 0.1,
    outlier.shape = NA,
    show.legend = FALSE
  ) +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(y = NULL, x = "Price", title = "NYC Airbnb by Room Type")

train_df %>%
  unnest_tokens(word, name) %>%
  anti_join(stop_words) %>% 
  group_by(word ) %>%
  summarize_prices() %>%
  head(30) %>%
  mutate(word = fct_reorder(word, mean_price)) %>%
  ggplot(aes(mean_price, word, size = n)) +
  geom_point() +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(size = "Number of Listings", title = "NYC AirBNB Property Description NLP", y = NULL) +
  theme(legend.position = c(0.8, 0.3),
        legend.background = element_rect(color = "white"))

train_df %>% 
  group_by(neighbourhood) %>% 
  summarize_prices() %>% 
  arrange(-median_price) 

```

## Numeric Feature Plots

The outcome variable itself is skewed across all observations in the training data, as prices often are.

```{r}
train_df %>%
  ggplot(aes(10^price + 1, fill = neighbourhood_group)) +
  geom_histogram(position = "identity", 
                 alpha = 0.5, 
                 bins = 40) +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1),
                breaks = c(5, 10, 100, 1000)) +
  labs(fill = NULL, x = "price per night",
       title = "NYC Airbnb") +
  theme(legend.position = c(0.8, 0.5),
        legend.background = element_rect(color = "white"))
```

Let's explore the relationships between the features and the numeric outcome. 

A plot of the time series `last_review` by year and the corresponding ranges of price

```{r}
train_df %>%
  group_by(year = year(last_review)) %>%
  summarize(mean_price = mean(10^price - 1),
            n = n(),
            low = quantile(10^price - 1, probs = 0.25, na.rm = TRUE),
            high = quantile(10^price - 1, probs = 0.75, na.rm = TRUE),
              .groups = "drop") %>%
  filter(!is.na(year)) %>% 
  ggplot(aes(year, mean_price)) +
  geom_line() +
  geom_point(aes(size = n)) +
  geom_ribbon(aes(ymin = low, ymax = high), alpha = .1) +
  expand_limits(y = 0) +
  scale_x_continuous(breaks = seq(2011, 2017, 2)) +
  scale_y_continuous(labels = scales::dollar_format(accuracy = 1)) +
  labs(x = "Year", y = "Mean Price and IQR",
       size = "Reviews", title = "NYC Airbnb") +
  theme(legend.position = c(0.85, 0.2),
        legend.background = element_rect(color = "white"))
```

Histograms of the distributions of each numeric feature:

```{r numeric_feature1, fig.asp=1}
train_numeric <- train_df %>% keep(is.numeric) %>% colnames()

chart <- c(train_numeric)

train_df %>%
  select_at(vars(all_of(chart))) %>%
  select(-price) %>% 
  pivot_longer(cols = everything(),
    names_to = "key",
    values_to = "value"
  ) %>% 
  filter(!is.na(value)) %>% 
  ggplot() +
  geom_histogram(mapping = aes(x = value, fill = key),
                 bins = 50, show.legend = FALSE) +
  facet_wrap(~ key, scales = "free", ncol = 3) +
  labs(title = "NYC Airbnb Numeric Feature Histograms")

```

And the outcome variable by each numeric feature:

```{r}
train_df %>%
  select_at(vars(all_of(chart))) %>%
  pivot_longer(cols = -price,
               names_to = "key",
               values_to = "value") %>%
  filter(!is.na(value)) %>%
  ggplot() +
  geom_point(
    mapping = aes(x = value, y = 10 ^ price + 1),
    alpha = 0.1,
    shape = 20,
    show.legend = FALSE
  ) +
  geom_smooth(aes(
    x = value,
    y = 10 ^ price + 1 ,
    color = key),
    method = "gam", show.legend = FALSE) +
  facet_wrap(~ key, scales = "free", ncol = 3) +
  scale_y_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(x = NULL, y = "Price", title = "NYC Airbnb")
```

## Map of prices 

Adapted from Julia Silge's demonstration

```{r, fig.asp=1}
usa_data = map_data("usa")

train_df %>%
  ggplot(aes(longitude, latitude, z = price)) +
  stat_summary_hex(bins = 100) +
  labs(fill = "Mean price(log10)") +
  cowplot::theme_map() +
  labs(title = "NYC Airbnb Price Ranges")
  theme(legend.position = c(0.1, 0.8),
        legend.background = element_rect(color = "white"))
```

## Numeric Feature Correlations

```{r corrplot, fig.asp=1}
cr1 <- train_df %>%
  select(-price) %>%
  keep(is.numeric) %>%
  cor(use = "pair")

corrplot::corrplot(cr1, type = "upper")

```

# {-}

Conclusions:

* Neighborhood location drives price, through both named neighborhood and through longitude and latitude.
* The listing names have some interesting words.
* Some of the hosts draw higher pricing.
* the numeric features do not provide as much predictive power alone
* there are AirBNB listings that have neither reviews nor last_review dates. 

# Preprocessiong {.tabset}

## The recipe framework

I am going to train on almost all of the train data. The 1% left is a quick confirmation of validity.  The provided file labeled `test` has no labels.

```{r}
set.seed(2021)
split <- train_df %>% 
  initial_split(strata = price,
                       prop = .99)
train <- training(split)
valid <- testing(split)
```

There are only `r nrow(valid)` held out from training as a last check before submission.

```{r}
basic_rec <-
  recipe(price ~ ., train) %>%
  # ---- set aside the row id's
  update_role(id, host_name, new_role = "id") %>% 
  step_novel(neighbourhood) %>%
  step_other(neighbourhood, threshold = 0.01) %>%
  step_novel(host_id) %>%
  step_other(host_id, threshold = 0.01) %>%
  step_tokenize(name) %>%
  step_stopwords(name) %>%
  step_texthash(name, num_terms = 16) %>% 
  step_indicate_na(last_review, reviews_per_month) %>% 
  step_mutate(last_review = if_else(is.na(last_review),
                                    min(train$last_review, na.rm = TRUE), last_review)) %>% 
  step_holiday(last_review) %>% 
  step_date(last_review, features = "year",
            keep_original_cols = FALSE) %>% 
  step_ns(longitude, latitude, deg_free = 4) %>%
  step_normalize(all_numeric_predictors()) %>% 
  step_nzv(all_predictors())
    
```

## the pre-processed data

```{r}
basic_rec %>% 
#  finalize_recipe(list(num_comp = 2)) %>% 
  prep() %>% 
  juice() 
```

## Cross Validation

We will use 5-fold cross validation and stratify between the rain and no-rain classes.

```{r}
train_folds <- vfold_cv(data = train,
                        strata = price,
                        v = 5)

```

# {-}

# Machine Learning {.tabset}

## Model Specifications

We will build a specification for simple shallow random forest and a specification for catboost. 

```{r}
catboost_spec <- boost_tree(trees = 1000,
                            min_n = tune(),
                            learn_rate = tune(),
                            tree_depth = tune()) %>% 
  set_engine("catboost") %>%
  set_mode("regression")

bag_spec <-
  bag_tree(min_n = 10) %>%
  set_engine("rpart", times = 50) %>%
  set_mode("regression")

```

## The RMSLE custom metric

The yardstick default RMSE is on the log of price, not RMSLE on price. This will create a custom function to track RMSLE:

```{r}
library(rlang)

rmsle_vec <- function(truth, estimate, na_rm = TRUE, ...) {
  rmsle_impl <- function(truth, estimate) {
    sqrt(mean((log(truth + 1) - log(estimate + 1))^2))
  }

  metric_vec_template(
    metric_impl = rmsle_impl,
    truth = truth,
    estimate = estimate,
    na_rm = na_rm,
    cls = "numeric",
    ...
  )
}

rmsle <- function(data, ...) {
  UseMethod("rmsle")
}

rmsle <- new_numeric_metric(rmsle, direction = "minimize")

rmsle.data.frame <- function(data, truth, estimate, na_rm = TRUE, ...) {
  metric_summarizer(
    metric_nm = "rmsle",
    metric_fn = rmsle_vec,
    data = data,
    truth = !!enquo(truth),
    estimate = !!enquo(estimate),
    na_rm = na_rm,
    ...
  )
}

mset = metric_set(rmsle)
```

## Parallel backend

To speed up computation we will use a parallel backend.

```{r}
all_cores <- parallelly::availableCores(omit = 1)
all_cores

future::plan("multisession", workers = all_cores) # on Windows

```

## A quick Random Forest

Lets make a cursory check of the recipe and variable importance, which comes out of `rpart` for free. This workflow also handles factors without dummies.

```{r random_forest_fit, fig.asp=1}
bag_wf <-
  workflow() %>%
  add_recipe(basic_rec) %>%
  add_model(bag_spec)

set.seed(123)
bag_fit <- parsnip::fit(bag_wf, data = train)

extract_fit_parsnip(bag_fit)$fit$imp %>%
  mutate(term = fct_reorder(term, value)) %>%
  ggplot(aes(value, term)) +
  geom_point() +
  geom_errorbarh(aes(
    xmin = value - `std.error` / 2,
    xmax = value + `std.error` / 2
  ),
  height = .3) +
  labs(title = "Feature Importance",
       x = NULL, y = NULL)

```

We see that `room_type` and the geographical information will be very important for this model.

## Tuning Catboost

Now that we have some confidence that the features have predictive power, lets tune up a set of `catboost` models. 

```{r catboost_tuning_params}

catboost_params <-
  dials::parameters(min_n(), # min data in leaf
                    tree_depth(range = c(4, 15)),
                    learn_rate(range = c(-3, -0.7), 
                               trans = log10_trans())
                    )
                    
cbst_grid <- dials::grid_max_entropy(catboost_params,
                                     size = 40 
                                     )
cbst_grid
```

```{r catboost_tuning_no_eval, eval=FALSE}
cv_res_catboost <-
  workflow() %>% 
  add_recipe(basic_rec) %>% 
  add_model(catboost_spec) %>% 
  tune_grid(    
    resamples = train_folds,
    grid = cbst_grid,
    control = control_race(verbose = FALSE,
                           save_pred = TRUE, 
                           save_workflow = TRUE,
                           extract = extract_model,
                           parallel_over = "resamples"),
    metrics = mset
)
```

```{r catboost_tuning_no_include, include=FALSE}
if (file.exists(here::here("data","airbnbcatboost.rds"))) {

cv_res_catboost <- read_rds(here::here("data","airbnbcatboost.rds"))
  
} else {  
cv_res_catboost <-
  workflow() %>% 
  add_recipe(basic_rec) %>% 
  add_model(catboost_spec) %>% 
  tune_grid(    
    resamples = train_folds,
    grid = cbst_grid,
    control = control_race(verbose = FALSE,
                           save_pred = TRUE, 
                           save_workflow = TRUE,
                           extract = extract_model,
                           parallel_over = "resamples"),
    metrics = mset
)

write_rds(cv_res_catboost, here::here("data","airbnbcatboost.rds"))
}
```

```{r catboost_tuning_performance}
autoplot(cv_res_catboost)

show_best(cv_res_catboost) %>% 
  select(-.estimator)

cat_wf_best <-   
  workflow() %>% 
  add_recipe(basic_rec) %>% 
  add_model(catboost_spec) %>% 
  finalize_workflow(select_best(cv_res_catboost))

cat_fit_best <- cat_wf_best %>%
  parsnip::fit(data = train)
```

# {-}

Catboost model performance

```{r}
predict(cat_fit_best, new_data = valid) %>%
  cbind(valid) %>%
  ggplot(aes(10^price + 1, 
             10^.pred + 1, 
             color = neighbourhood_group)) +
  geom_abline(
    slope = 1,
    lty = 2,
    color = "gray50",
    alpha = 0.5
  ) +
  geom_point(alpha = 0.8, shape = 21) +
  scale_x_log10(labels = scales::dollar_format(accuracy = 1)) +
  scale_y_log10(labels = scales::dollar_format(accuracy = 1)) +
  labs(color = NULL, 
       x = "True price", 
       y = "Predicted price")

predict(cat_fit_best, new_data = valid) %>%
  cbind(valid) %>%
  rmsle(10^price - 1, 10^.pred - 1)

```

This catboost figure is somewhat better than the leaderboard RMSLE of 0.40758, so it would have been worthy of submission.

Lastly, we build the submission file 

```{r, eval=FALSE}

bind_cols(predict(cat_fit_best, test_df), test_df) %>% 
  select(id, price = .pred) %>%
  write_csv(file = path_export)

```

and make the submission to the Kaggle board

```{r, eval = FALSE}
shell(glue::glue('kaggle competitions submit -c { competition_name } -f { path_export } -m "Catboosted"'))
```



