03_test.Rmd

---
title: "Example Analysis of Ames Housing Data"
author: "Axel R"
date: "2024-01-20"
output: html_document
---

# Details
These objects are the results of an analysis of the Ames housing data. A K-nearest neighbors model was used with a small predictor set that included natural spline transformations of the Longitude and Latitude predictors. The code used to generate these examples was:

```{r}
library(tidymodels)
library(tune)
library(AmesHousing)
library(kknn)

# ------------------------------------------------------------------------------

ames <- make_ames()

set.seed(4595)
data_split <- initial_split(ames, strata = "Sale_Price")

ames_train <- training(data_split)

set.seed(2453)
rs_splits <- vfold_cv(ames_train, strata = "Sale_Price")

# ------------------------------------------------------------------------------

ames_rec <-
  recipe(Sale_Price ~ ., data = ames_train) %>%
  step_log(Sale_Price, base = 10) %>%
  step_YeoJohnson(Lot_Area, Gr_Liv_Area) %>%
  step_other(Neighborhood, threshold = .1)  %>%
  step_dummy(all_nominal()) %>%
  step_zv(all_predictors()) %>%
  step_ns(Longitude, deg_free = tune("lon")) %>%
  step_ns(Latitude, deg_free = tune("lat"))

knn_model <-
  nearest_neighbor(
    mode = "regression",
    neighbors = tune("K"),
    weight_func = tune(),
    dist_power = tune()
  ) %>%
  set_engine("kknn")

ames_wflow <-
  workflow() %>%
  add_recipe(ames_rec) %>%
  add_model(knn_model)

ames_set <-
  extract_parameter_set_dials(ames_wflow) %>%
  update(K = neighbors(c(1, 50)))

set.seed(7014)
ames_grid <-
  ames_set %>%
  grid_max_entropy(size = 10)

ames_grid_search <-
  tune_grid(
    ames_wflow,
    resamples = rs_splits,
    grid = ames_grid
  )

set.seed(2082)
ames_iter_search <-
  tune_bayes(
    ames_wflow,
    resamples = rs_splits,
    param_info = ames_set,
    initial = ames_grid_search,
    iter = 15
  )
```

important note: Since the rsample split columns contain a reference to the same data, saving them to disk can results in large object sizes when the object is later used. In essence, R replaces all of those references with the actual data. For this reason, we saved zero-row tibbles in their place. This doesn't affect how we use these objects in examples but be advised that using some rsample functions on them will cause issues.

# Examples

```{r}
library(tune)

ames_grid_search
ames_iter_search

```