# Load packages
pacman::p_load(tidyverse, tidymodels, janitor, skimr, magrittr, here)
# Set random seed
set.seed(42)
# Load the data
train_df = read_csv(here("data", "train.csv")) %>% clean_names()
# Rename first and second floor square footage
train_df %<>%
rename(first_floor_sqft = x1st_flr_sf,
second_floor_sqft = x2nd_flr_sf)
# Create a variable for total square footage
train_df %<>%
mutate(total_sqft = first_floor_sqft + second_floor_sqft)EC524W25: Lab 003
Practice with tidymodels
Introduction
In this document, we are going to keep predicting housing prices. I have set up a tidymodels workflow, including:
- a recipe
- a linear regression model
- a cross validation strategy
- a workflow to fit the model and to make predictions on newdata
Task: Take this document and change it and make it better. Instead of using basic lm, switch to a penalized regression model using glmnet. Use either Ridge, Lasso, or Elastic Net. Change the model specification to include more variables, but make sure to start simple. Tune the model using a grid search. Make your CPU work.
Everything should be working out of the gate. Before getting started, make sure compiling the document works.
Resources:
Everything in this document is pulled from the Kaggle notebook from the previous lab. Use it for help.
However, missing from this notebook is how to use glmnet. Use the internet to find out how to use glmnet with tidymodels. Tuning the model works the same as the knn example in the kaggle notebook.
Lastly, ask me for help if you need it. Feel free to work together!
tidymodels workflow
Setup
First, setup the document, loading in packages, setting a random seed, loading the data, and adjusting column names
Recipe
Let’s define the recipe for pre-processing.
# Define the recipe
price_recipe = recipe(
sale_price ~ total_sqft + condition1 + garage_area + full_bath,
data = train_df) %>%
# Omit all missing values
step_naomit(all_predictors()) %>%
# Create dummy variables for all nominal variables
step_dummy(all_nominal(), -all_outcomes())One important note is preprocessing using recipes helps keep our analysis unadulterated when using a resampling step. This is because the preprocessing steps are applied to each fold of the data separately, ensuring that the model is getting additional information from data in the validation sets.
Model
The model is where we define the type of model we want to use for the task at hand.
# Define our linear regression model (with 'lm' engine)
model_lm =
linear_reg() %>%
set_mode("regression") %>%
set_engine("lm")
# Check the result
model_lmLinear Regression Model Specification (regression)
Computational engine: lm Workflow
The model and the recipe fit together in a workflow. This is where we define the steps to fit the model to the data. These workflows make our prediction tasks easier.
workflow() %>%
add_model(model_lm) %>%
add_recipe(price_recipe)══ Workflow ════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: linear_reg()
── Preprocessor ────────────────────────────────────────────────────────────────
2 Recipe Steps
• step_naomit()
• step_dummy()
── Model ───────────────────────────────────────────────────────────────────────
Linear Regression Model Specification (regression)
Computational engine: lm Let’s define our workflow as price_workflow
# Define our workflow
price_workflow = workflow() %>%
add_model(model_lm) %>%
add_recipe(price_recipe)Fit the model
Simple fit
As an example of how workflows fit a model with a recipe, let’s fit the model to the data using a workflow, but without the cross validation strategy.
# Fit the model without cross-validation
price_workflow %>% fit(train_df)══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: linear_reg()
── Preprocessor ────────────────────────────────────────────────────────────────
2 Recipe Steps
• step_naomit()
• step_dummy()
── Model ───────────────────────────────────────────────────────────────────────
Call:
stats::lm(formula = ..y ~ ., data = data)
Coefficients:
(Intercept) total_sqft garage_area full_bath
-48487.99 75.93 125.89 13714.88
condition1_Feedr condition1_Norm condition1_PosA condition1_PosN
6193.30 37616.85 19999.37 11192.21
condition1_RRAe condition1_RRAn condition1_RRNe condition1_RRNn
4866.58 26756.16 20059.47 49823.00 Adding cross-validation strategy
Now let’s add a cross-validation strategy to our workflow. First we have to create a cross-validation strategy object. Feel free to tinker with the number of folds.
# Create a 5-fold cross validation strategy
rsmp_cv = train_df %>% vfold_cv(v = 5)To look at the resampling splits across the data
rsmp_cv %>% tidy()Fit the model with cross-validation
Now let’s fit the model to the data using a workflow and a cross-validation strategy.
fit_cv = price_workflow %>%
fit_resamples(resamples = rsmp_cv)
Tip
One can specify which metrics to fit within the fit_resamples function with:
... %>%
fit_resamples(resamples = rsmp_cv, metrics = metric_set(r2))Now it is a good idea to assess model performance. How else do you know which model is best? The following code will summarize the results of the cross-validation. Play around until you get some good results.
# Collect the rmse metric
fit_cv %>% collect_metrics()# A tibble: 2 × 6
.metric .estimator mean n std_err .config
<chr> <chr> <dbl> <int> <dbl> <chr>
1 rmse standard 48137. 5 2603. Preprocessor1_Model1
2 rsq standard 0.637 5 0.0300 Preprocessor1_Model1Once you have a good model, we will want to predict on new data.
Before we can predict on new data, we need to finalize our workflow. This will fit the model to the entire dataset. The following code block will finalize the workflow and fit the model to the entire dataset.
# Finalize workflow
best_model = price_workflow %>%
finalize_workflow(select_best(fit_cv, metric = "rmse"))
# Check the result
best_model══ Workflow ════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: linear_reg()
── Preprocessor ────────────────────────────────────────────────────────────────
2 Recipe Steps
• step_naomit()
• step_dummy()
── Model ───────────────────────────────────────────────────────────────────────
Linear Regression Model Specification (regression)
Computational engine: lm Finally, now that we have a finalized workflow that has been tuned to the best model, we can fit the model to the entire dataset. We always want to use all of our training data to fit the model before making predictions on new data.
# Fit the final workflow
final_fit = best_model %>% fit(train_df)
# Examine the final fit
final_fit══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: linear_reg()
── Preprocessor ────────────────────────────────────────────────────────────────
2 Recipe Steps
• step_naomit()
• step_dummy()
── Model ───────────────────────────────────────────────────────────────────────
Call:
stats::lm(formula = ..y ~ ., data = data)
Coefficients:
(Intercept) total_sqft garage_area full_bath
-48487.99 75.93 125.89 13714.88
condition1_Feedr condition1_Norm condition1_PosA condition1_PosN
6193.30 37616.85 19999.37 11192.21
condition1_RRAe condition1_RRAn condition1_RRNe condition1_RRNn
4866.58 26756.16 20059.47 49823.00 Prediction
First let’s load in the testing data to make our predictions on
# Generate predictions for the test set
test_df = read_csv(here("data", "test.csv")) %>% clean_names()
# Rename first and second floor square footage
test_df %<>%
rename(first_floor_sqft = x1st_flr_sf,
second_floor_sqft = x2nd_flr_sf)
# Create a variable for total square footage
test_df %<>%
mutate(total_sqft = first_floor_sqft + second_floor_sqft)Now we can make predictions on the test data. It is pretty simple.
# Predict the sale price
predictions = predict(final_fit, new_data = test_df)