Tune and interpret decision trees for wind turbines
Nguyen_LSCM
11/1/2020
Tune and interpret decision trees for wind turbines
This is the latest in my series of screencasts demonstrating how to use the tidymodels packages, from starting out with first modeling steps to tuning more complex models. Today’s screencast walks through how to tune, fit and predict from decision tree models, using this week’s dataset on Canadian wind turbines
Here is the code I used in the video, for those who prefer reading instead of or in addition to video.
Explore data
Our modeling goal is to predict the capacity of wind turbines in Canada based on other characteristics of the turbines fro mthis week Simon Couch outlined this week how to use stacks for ensembling with this dataset, but here let’s take a more straighforward approach.
Let’s start by reading in the data
library(tidyverse)## -- Attaching packages --------## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.0
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts -----------------
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()turbines <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-27/wind-turbine.csv")## Parsed with column specification:
## cols(
## objectid = col_double(),
## province_territory = col_character(),
## project_name = col_character(),
## total_project_capacity_mw = col_double(),
## turbine_identifier = col_character(),
## turbine_number_in_project = col_character(),
## turbine_rated_capacity_k_w = col_double(),
## rotor_diameter_m = col_double(),
## hub_height_m = col_double(),
## manufacturer = col_character(),
## model = col_character(),
## commissioning_date = col_character(),
## latitude = col_double(),
## longitude = col_double(),
## notes = col_character()
## )turbines## # A tibble: 6,698 x 15
## objectid province_territ~ project_name total_project_c~ turbine_identif~
## <dbl> <chr> <chr> <dbl> <chr>
## 1 1 Alberta Optimist Wi~ 0.9 OWE1
## 2 2 Alberta Castle Rive~ 44 CRW1
## 3 3 Alberta Waterton Wi~ 3.78 WWT1
## 4 4 Alberta Waterton Wi~ 3.78 WWT2
## 5 5 Alberta Waterton Wi~ 3.78 WWT3
## 6 6 Alberta Waterton Wi~ 3.78 WWT4
## 7 7 Alberta Cowley North 19.5 CON1
## 8 8 Alberta Cowley North 19.5 CON2
## 9 9 Alberta Cowley North 19.5 CON3
## 10 10 Alberta Cowley North 19.5 CON4
## # ... with 6,688 more rows, and 10 more variables:
## # turbine_number_in_project <chr>, turbine_rated_capacity_k_w <dbl>,
## # rotor_diameter_m <dbl>, hub_height_m <dbl>, manufacturer <chr>,
## # model <chr>, commissioning_date <chr>, latitude <dbl>, longitude <dbl>,
## # notes <chr>How is the capacity related to other characteristics like the year of commissioning or size of the turbines?
turbines_df <- turbines %>%
transmute(
turbine_capacity = turbine_rated_capacity_k_w,
rotor_diameter_m,
hub_height_m,
commissioning_date = parse_number(commissioning_date),
province_territory = fct_lump_n(province_territory, 10),
model = fct_lump_n(model, 10)
) %>%
filter(!is.na(turbine_capacity)) %>%
mutate_if(is.character, factor)How is the capacity related to other characteristics like the year of commissioning or size of the turbines?
theme_set(theme_light())
turbines_df %>%
select(turbine_capacity:commissioning_date) %>%
pivot_longer(rotor_diameter_m:commissioning_date)%>%
ggplot(aes(turbine_capacity, value)) +
geom_hex(bins = 15, alpha = 0.8) +
geom_smooth(method = 'lm') +
facet_wrap(~name, scales = 'free_y')+
labs(y = NULL) +
scale_fill_gradient(high = 'cyan3')## `geom_smooth()` using formula 'y ~ x'
These relationships are the kind that we want to use in modeling, whether that’s the modeling stacking Simon demonstrated or the single model we’ll use here.
Build a model
We can start by loading the tidymodels metapackage, splitting our data into training and testing sets, and creating cross-validation samples
library(tidymodels)## -- Attaching packages --------## v broom 0.7.0 v recipes 0.1.13
## v dials 0.0.8 v rsample 0.0.7
## v infer 0.5.3 v tune 0.1.1
## v modeldata 0.0.2 v workflows 0.1.2
## v parsnip 0.1.2 v yardstick 0.0.7## -- Conflicts -----------------
## x scales::discard() masks purrr::discard()
## x dplyr::filter() masks stats::filter()
## x recipes::fixed() masks stringr::fixed()
## x dplyr::lag() masks stats::lag()
## x yardstick::spec() masks readr::spec()
## x recipes::step() masks stats::step()set.seed(123)
wind_split <- initial_split(turbines_df, strata = turbine_capacity)
wind_train <- training(wind_split)
wind_test <- testing(wind_split)
set.seed(234)
wind_folds <- vfold_cv(wind_train, strata = turbine_capacity)
wind_folds## # 10-fold cross-validation using stratification
## # A tibble: 10 x 2
## splits id
## <list> <chr>
## 1 <split [4.4K/488]> Fold01
## 2 <split [4.4K/487]> Fold02
## 3 <split [4.4K/486]> Fold03
## 4 <split [4.4K/486]> Fold04
## 5 <split [4.4K/486]> Fold05
## 6 <split [4.4K/486]> Fold06
## 7 <split [4.4K/486]> Fold07
## 8 <split [4.4K/486]> Fold08
## 9 <split [4.4K/485]> Fold09
## 10 <split [4.4K/484]> Fold10Next, let’s create a tunable decision tree model specification
tree_spec <- decision_tree(
cost_complexity = tune(),
tree_depth = tune(),
min_n = tune()
) %>%
set_engine('rpart') %>%
set_mode('regression')
tree_spec## Decision Tree Model Specification (regression)
##
## Main Arguments:
## cost_complexity = tune()
## tree_depth = tune()
## min_n = tune()
##
## Computational engine: rpartWe need a set of possible parameter values to try out for the decision tree
tree_grid <- grid_regular(cost_complexity(), tree_depth(), min_n(), levels = 4)
tree_grid## # A tibble: 64 x 3
## cost_complexity tree_depth min_n
## <dbl> <int> <int>
## 1 0.0000000001 1 2
## 2 0.0000001 1 2
## 3 0.0001 1 2
## 4 0.1 1 2
## 5 0.0000000001 5 2
## 6 0.0000001 5 2
## 7 0.0001 5 2
## 8 0.1 5 2
## 9 0.0000000001 10 2
## 10 0.0000001 10 2
## # ... with 54 more rowsNow, let’s try out all the possible parameter values on all our resampled datasets. Let’s use some non-default metrics, while we;re at it
doParallel::registerDoParallel()
set.seed(345)
tree_rs <- tune_grid(
tree_spec,
turbine_capacity ~.,
resamples = wind_folds,
grid = tree_grid,
metrics = metric_set(rmse, rsq, mae, mape)
)
tree_rs## # Tuning results
## # 10-fold cross-validation using stratification
## # A tibble: 10 x 4
## splits id .metrics .notes
## <list> <chr> <list> <list>
## 1 <split [4.4K/488]> Fold01 <tibble [256 x 7]> <tibble [0 x 1]>
## 2 <split [4.4K/487]> Fold02 <tibble [256 x 7]> <tibble [0 x 1]>
## 3 <split [4.4K/486]> Fold03 <tibble [256 x 7]> <tibble [0 x 1]>
## 4 <split [4.4K/486]> Fold04 <tibble [256 x 7]> <tibble [0 x 1]>
## 5 <split [4.4K/486]> Fold05 <tibble [256 x 7]> <tibble [0 x 1]>
## 6 <split [4.4K/486]> Fold06 <tibble [256 x 7]> <tibble [0 x 1]>
## 7 <split [4.4K/486]> Fold07 <tibble [256 x 7]> <tibble [0 x 1]>
## 8 <split [4.4K/486]> Fold08 <tibble [256 x 7]> <tibble [0 x 1]>
## 9 <split [4.4K/485]> Fold09 <tibble [256 x 7]> <tibble [0 x 1]>
## 10 <split [4.4K/484]> Fold10 <tibble [256 x 7]> <tibble [0 x 1]>Notice that we aren;t tuning a workflow() here, as I have often shown how to do instead we are tuning the model specification (accompanied by a formula preprocessor); this is so we can use the bare model in some model evaluation activities.
Evaluate model
Now let’s check out how we did. We can collect or visualize the metrics
collect_metrics(tree_rs)## # A tibble: 256 x 9
## cost_complexity tree_depth min_n .metric .estimator mean n std_err
## <dbl> <int> <int> <chr> <chr> <dbl> <int> <dbl>
## 1 0.0000000001 1 2 mae standard 386. 10 1.50
## 2 0.0000000001 1 2 mape standard 27.7 10 1.30
## 3 0.0000000001 1 2 rmse standard 508. 10 1.44
## 4 0.0000000001 1 2 rsq standard 0.303 10 0.0134
## 5 0.0000001 1 2 mae standard 386. 10 1.50
## 6 0.0000001 1 2 mape standard 27.7 10 1.30
## 7 0.0000001 1 2 rmse standard 508. 10 1.44
## 8 0.0000001 1 2 rsq standard 0.303 10 0.0134
## 9 0.0001 1 2 mae standard 386. 10 1.50
## 10 0.0001 1 2 mape standard 27.7 10 1.30
## # ... with 246 more rows, and 1 more variable: .config <chr>autoplot(tree_rs) + theme_light(base_family = 'IBMPlexSans')
Looks like this data need a fairly complex tree
We can examine or select the best sets of parameter options, chosen by whichever metric we want
show_best(tree_rs, 'mape')## # A tibble: 5 x 9
## cost_complexity tree_depth min_n .metric .estimator mean n std_err
## <dbl> <int> <int> <chr> <chr> <dbl> <int> <dbl>
## 1 0.0000000001 15 2 mape standard 0.564 10 0.0592
## 2 0.0000001 15 2 mape standard 0.564 10 0.0591
## 3 0.0000000001 15 14 mape standard 0.823 10 0.0547
## 4 0.0000001 15 14 mape standard 0.823 10 0.0547
## 5 0.0001 15 2 mape standard 0.885 10 0.0705
## # ... with 1 more variable: .config <chr>show_best(tree_rs, 'rmse')## # A tibble: 5 x 9
## cost_complexity tree_depth min_n .metric .estimator mean n std_err
## <dbl> <int> <int> <chr> <chr> <dbl> <int> <dbl>
## 1 0.0000000001 15 2 rmse standard 52.4 10 5.08
## 2 0.0000001 15 2 rmse standard 52.4 10 5.09
## 3 0.0001 15 2 rmse standard 55.6 10 4.64
## 4 0.0000000001 15 14 rmse standard 64.7 10 4.59
## 5 0.0000001 15 14 rmse standard 64.7 10 4.59
## # ... with 1 more variable: .config <chr>Next, let’s use one of these ‘best’ sets of parameters to update and finalize our model.
final_tree <- finalize_model(tree_spec, select_best(tree_rs, 'rmse'))
final_tree## Decision Tree Model Specification (regression)
##
## Main Arguments:
## cost_complexity = 1e-10
## tree_depth = 15
## min_n = 2
##
## Computational engine: rpartThis model final_tree is updated and finalized (no longer tunable) but it is not fit. It has all its hyperparameters set but it has not been fit to any data. We have a couple of options for how to fit this model. We ca neither fit final_tree to training data using fit() or to the testing/ training splot using last_fit(), which will give us some other results along with the fitted output.
final_fit <- fit(final_tree, turbine_capacity ~., wind_train)
final_rs <- last_fit(final_tree, turbine_capacity~., wind_split)We can predict from either one of these objects
predict(final_fit, wind_train[144, ])## # A tibble: 1 x 1
## .pred
## <dbl>
## 1 1800What are the most important variables in this decision tree for predicting turbine capacity?
library(vip)##
## Attaching package: 'vip'## The following object is masked from 'package:utils':
##
## vifinal_fit %>%
vip(geom = 'col', aesthetics = list(fill = 'midnightblue', alpha = 0.8)) +
scale_y_continuous(expand = c(0, 0))
I really like the parttree package for visualization decision tree results. It only works for models with one or two predictors, so we’ll have to fit an example model that isn’t quite the same as our full model. It can still help us understand how this decision tree is working, but keep in mind that is it not the same as our full model with more predictors
library(parttree)
ex_fit <- fit(
final_tree,
turbine_capacity ~ rotor_diameter_m + commissioning_date, wind_train)
wind_train %>%
ggplot(aes(rotor_diameter_m, commissioning_date)) +
geom_parttree(data = ex_fit, aes(fill = turbine_capacity), alpha = 0.3)+
geom_jitter(alpha = 0.7, width =1, height = 0.5, aes(color = turbine_capacity)) +
scale_colour_viridis_c(aesthetics = c('color', 'fill'))
Finally, let’s turn to the testing data! These results are stored in final_rs, along with the fitted output there. We can see both metrics on the testing data and predictions.
collect_metrics(final_rs)## # A tibble: 2 x 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 rmse standard 73.6
## 2 rsq standard 0.985final_rs %>%
collect_predictions() %>%
ggplot(aes(turbine_capacity, .pred)) +
geom_abline(slope = 1, lty = 2, color = 'gray50', alpha = 0.5) +
geom_point(alpha = 0.6, color = 'midnightblue') +
coord_fixed()