# load packages
library(tidyverse)
library(tidymodels)
library(arrow)
library(textdata)
library(textrecipes)
library(here)
library(themis)
library(tictoc)
library(vip)
library(tidytext)
library(hardhat)
library(glmnet)
# use parallel processing
library(future)
plan(multisession)
# preferred theme
theme_set(theme_minimal(base_size = 12, base_family = "Atkinson Hyperlegible"))AE 13: Predicting legislative policy attention
Suggested answers
Application exercise
Answers
Load packages
Import data
leg <- read_parquet(file = "data/legislation.parquet")Split data for model fitting
# split into training and testing
set.seed(852)
# sample only 20000 observations
leg_samp <- slice_sample(.data = leg, n = 2e04)
# split into training and testing sets
leg_split <- initial_split(data = leg_samp, strata = policy_lab, prop = 0.9)
leg_train <- training(x = leg_split)
leg_test <- testing(x = leg_split)
# create 5-fold cross-validation sets
leg_folds <- vfold_cv(data = leg_train, v = 5)Null model
Demonstration: Estimate a null model to serve as a baseline for evaluating performance. How does it perform?
# estimate a null model
null_spec <- null_model() |>
set_mode("classification") |>
set_engine("parsnip")
null_rs <- workflow() |>
add_formula(policy_lab ~ id) |>
add_model(null_spec) |>
fit_resamples(
resamples = leg_folds
)
collect_metrics(null_rs)# A tibble: 3 × 6
.metric .estimator mean n std_err .config
<chr> <chr> <dbl> <int> <dbl> <chr>
1 accuracy multiclass 0.129 5 0.00306 Preprocessor1_Model1
2 brier_class multiclass 0.467 5 0.000162 Preprocessor1_Model1
3 roc_auc hand_till 0.5 5 0 Preprocessor1_Model1Add response here. Poorly. Baseline accuracy is quite low and the Brier score is high. This is due to the imbalanced nature of the policy labels and the fact that there are 20 distinct labels. This is a hard problem to solve.
Lasso regression model
Estimate a simple lasso regression model using a bag-of-words representation of the legislative descriptions.
Define recipe
Your turn: Define a feature engineering recipe that uses the description to predict policy_lab. The recipe should:
- Tokenize the
descriptioncolumn. - Remove stopwords.
- Keep tokens that appear more than 500 times in the corpus.
- Convert the tokens to a term frequency-inverse document frequency (TF-IDF) representation.
- Downsample using
policy_labto balance the classes.
Note
Step functions for text features are found in the {textrecipes} package.
glmnet_rec <- recipe(policy_lab ~ description, data = leg_train) |>
# tokenize and prep text
step_tokenize(description) |>
step_stopwords(description) |>
step_tokenfilter(description, max_tokens = 500) |>
step_tfidf(description) |>
step_downsample(policy_lab)
glmnet_rec# A tibble: 4,960 × 501
policy_lab tfidf_description_1 tfidf_description_10 tfidf_description_18
<fct> <dbl> <dbl> <dbl>
1 Macroeconomics 0 0 0
2 Macroeconomics 0 0 0
3 Macroeconomics 0 0 0
4 Macroeconomics 0 0 0
5 Macroeconomics 0 0 0
6 Macroeconomics 0 0 0
7 Macroeconomics 0 0 0
8 Macroeconomics 0 0 0
9 Macroeconomics 0 0 0
10 Macroeconomics 0 0 0
# ℹ 4,950 more rows
# ℹ 497 more variables: tfidf_description_1930 <dbl>,
# tfidf_description_1934 <dbl>, tfidf_description_1938 <dbl>,
# tfidf_description_1949 <dbl>, tfidf_description_1954 <dbl>,
# tfidf_description_1958 <dbl>, tfidf_description_1965 <dbl>,
# tfidf_description_1970 <dbl>, tfidf_description_1974 <dbl>,
# tfidf_description_1986 <dbl>, tfidf_description_2 <dbl>, …Model specification
Demonstration: Specify a lasso regression model tuned over the penalty parameter.
# penalized logistic regression, tune over penalty - keep mixture = 1 (lasso regression)
glmnet_spec <- multinom_reg(penalty = tune(), mixture = 1) |>
set_mode("classification") |>
set_engine("glmnet")Define workflow
Demonstration: Create the workflow for the model.
Sparse encoding
Recall that many of the cells in the prepared data frame contain 0s (e.g. token not used in a specific document). Regularized regression models with text features powered using set_engine("glmnet") can be more efficiently fit if we transform the data to a sparse matrix. This is done by specifying a non-default preprocessing blueprint using the {hardhat} package.
For more information, see this case study from SMLTAR.
# use sparse blueprint for more efficient model estimation
library(hardhat)
sparse_bp <- default_recipe_blueprint(composition = "dgCMatrix")
# define workflow
glmnet_wf <- workflow() |>
add_recipe(glmnet_rec, blueprint = sparse_bp) |>
add_model(glmnet_spec)Tune the model
Demonstration: Create a tuning grid for the penalty parameter and tune the model.
# define tuning grid
penalty_grid <- grid_regular(penalty(range = c(-5, 0)), levels = 30)# tune the model
set.seed(123)
tic()
glmnet_tune_rs <- tune_grid(
object = glmnet_wf,
resamples = leg_folds,
grid = penalty_grid,
control = control_grid(save_pred = TRUE, save_workflow = TRUE)
)
toc()17.853 sec elapsedYour turn: Examine the performance of the model, both overall and using the confusion matrix. Which misclassifications are most common? Why might the model have a hard time discriminating between these policy labels?
# view average metrics
autoplot(glmnet_tune_rs)
# identify best models based on assessment set
show_best(x = glmnet_tune_rs, metric = "roc_auc")# A tibble: 5 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.00259 roc_auc hand_till 0.911 5 0.00211 Preprocessor1_Model15
2 0.00386 roc_auc hand_till 0.911 5 0.00217 Preprocessor1_Model16
3 0.00174 roc_auc hand_till 0.910 5 0.00213 Preprocessor1_Model14
4 0.00574 roc_auc hand_till 0.907 5 0.00205 Preprocessor1_Model17
5 0.00117 roc_auc hand_till 0.906 5 0.00221 Preprocessor1_Model13show_best(x = glmnet_tune_rs, metric = "accuracy")# A tibble: 5 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.00386 accuracy multiclass 0.603 5 0.00529 Preprocessor1_Model16
2 0.00259 accuracy multiclass 0.601 5 0.00479 Preprocessor1_Model15
3 0.00574 accuracy multiclass 0.596 5 0.00373 Preprocessor1_Model17
4 0.00174 accuracy multiclass 0.594 5 0.00480 Preprocessor1_Model14
5 0.00853 accuracy multiclass 0.589 5 0.00311 Preprocessor1_Model18# confusion matrix for best penalty value
conf_mat_resampled(
x = glmnet_tune_rs,
parameters = select_best(glmnet_tune_rs, metric = "roc_auc"),
tidy = FALSE
) |>
autoplot(type = "heatmap")
# most frequent misclassifications (average)
conf_mat_resampled(
x = glmnet_tune_rs,
parameters = select_best(glmnet_tune_rs, metric = "roc_auc")
) |>
# filter out correct predictions and sort by frequency
filter(Prediction != Truth) |>
arrange(-Freq)# A tibble: 380 × 3
Prediction Truth Freq
<fct> <fct> <dbl>
1 Civil rights, minority issues, civil liberties Government operations 25.4
2 Macroeconomics Government operations 23.6
3 Macroeconomics Banking, finance, and d… 20.8
4 International affairs and foreign aid Government operations 19.4
5 Labor and employment Government operations 19.2
6 Defense Government operations 18.8
7 Public lands and water management Government operations 18.2
8 Environment Public lands and water … 17.8
9 Law, crime, family issues Government operations 17.8
10 International affairs and foreign aid Defense 16.6
# ℹ 370 more rowsAdd response here. Overall the model has a high ROC AUC, but still a low accuracy. This is likely due to the number of possible policy labels and the complexity of the relationships between the descriptions and the labels. Many of the common misclassifications feel like reasonable mistakes even if a human being was coding the documents. For example, International affairs and Defense probably overlap in terms of common tokens and subject-matter.
Additionally, the most common misclassifications are for bills that are actually Government operations. Since it is the most type of legislation, there are simply more opportunities to incorrectly label them.
Variable importance
Your turn: Calculate which tokens are used by the model to predict each of the policy labels. Visualize the top 10 tokens for each policy label. Do these tokens make sense as useful predictors?
# fit the best model
glmnet_best <- glmnet_tune_rs |>
fit_best()# multiclass classification - need to manually extract coefficients for each
# outcome class
lasso_vip <- coef(extract_fit_engine(glmnet_best),
s = select_best(x = glmnet_tune_rs, metric = "roc_auc")$penalty
) |>
# need first element from each sublist
map(\(x) x[, 1L]) |>
# convert to a data frame and extract the relevant pieces of information
enframe() |>
unnest_longer(value) |>
rename(
token = value_id,
importance = value,
class = name
) |>
# ignore the intercept
filter(token != "(Intercept)") |>
# clean up data to focus purely on magnitude of coefficients
mutate(
sign = ifelse(sign(importance) == 1, "POS", "NEG"),
importance = abs(importance),
token = str_remove_all(token, "tfidf_description_")
) |>
# remove anything with importance of 0
filter(importance != 0)
# visualize the top 10 tokens for each class
lasso_vip |>
# keep the top 10 coefficients for each
filter(sign == "POS") |>
slice_max(n = 10, order_by = importance, by = class) |>
# change order of token levels for plotting
mutate(token = reorder_within(token, by = importance, within = class)) |>
ggplot(mapping = aes(
x = importance,
y = token
)) +
geom_col() +
scale_y_reordered() +
facet_wrap(
facets = vars(class),
scales = "free",
ncol = 4,
labeller = labeller(class = label_wrap_gen(20))
) +
labs(
title = "Most important tokens for each policy class",
x = "Importance",
y = NULL
)
Add response here. A lot of these are common words or terms one would associate with the substantive policy area. However many are numbers or other seemingly generic terms. This is where domain experience and knowing your data is quite helpful. Many of these numbers are likely part of references to different prior legislative acts or U.S. Code. Allowing for longer \(n\)-grams might allows us to use these in a semantically valid manner.
\(n\)-grams
This model will be similar to the first one, but we will now utilize \(n\)-grams to capture more complex relationships between words.
Define recipe
Your turn: Modify your previous feature engineering recipe to include \(n\)-grams, for \(n \in 1, 2, 3, 4\).
ngram_rec <- recipe(policy_lab ~ description, data = leg_train) |>
# tokenize and prep text
step_tokenize(description) |>
step_stopwords(description) |>
step_ngram(description, num_tokens = 4L, min_num_tokens = 1L) |>
step_tokenfilter(description, max_tokens = 1000) |>
step_tfidf(description) |>
step_downsample(policy_lab)
ngram_rec# A tibble: 4,960 × 1,001
policy_lab tfidf_description_1 tfidf_description_10 tfidf_description_10…¹
<fct> <dbl> <dbl> <dbl>
1 Macroeconomi… 0 0 0
2 Macroeconomi… 0 0 0
3 Macroeconomi… 0 0 0
4 Macroeconomi… 0 0 0
5 Macroeconomi… 0 0 0
6 Macroeconomi… 0 0 0
7 Macroeconomi… 0 0 0
8 Macroeconomi… 0 0 0
9 Macroeconomi… 0 0 0
10 Macroeconomi… 0 0 0
# ℹ 4,950 more rows
# ℹ abbreviated name: ¹tfidf_description_10_united
# ℹ 997 more variables: tfidf_description_10_united_states <dbl>,
# tfidf_description_10_united_states_code <dbl>, tfidf_description_15 <dbl>,
# tfidf_description_18 <dbl>, tfidf_description_18_united <dbl>,
# tfidf_description_18_united_states <dbl>,
# tfidf_description_18_united_states_code <dbl>, …Define workflow
Demonstration: Create the workflow for the model, reusing the previous model specification and a sparse blueprint.
# define workflow
ngram_wf <- workflow() |>
add_recipe(ngram_rec, blueprint = sparse_bp) |>
add_model(glmnet_spec)Tune the model
Demonstration: Tune the model.
# tune the model
set.seed(123)
tic()
ngram_tune_rs <- tune_grid(
object = ngram_wf,
resamples = leg_folds,
grid = penalty_grid,
control = control_grid(save_pred = TRUE, save_workflow = TRUE)
)
toc()20.829 sec elapsedYour turn: Examine the performance of the model, both overall and using the confusion matrix. Which misclassifications are most common? Why might the model have a hard time discriminating between these policy labels?
# view average metrics
autoplot(ngram_tune_rs)
# identify best models based on assessment set
show_best(x = ngram_tune_rs, metric = "roc_auc")# A tibble: 5 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.00386 roc_auc hand_till 0.917 5 0.00230 Preprocessor1_Model16
2 0.00574 roc_auc hand_till 0.917 5 0.00244 Preprocessor1_Model17
3 0.00259 roc_auc hand_till 0.916 5 0.00217 Preprocessor1_Model15
4 0.00853 roc_auc hand_till 0.915 5 0.00222 Preprocessor1_Model18
5 0.00174 roc_auc hand_till 0.913 5 0.00225 Preprocessor1_Model14show_best(x = ngram_tune_rs, metric = "accuracy")# A tibble: 5 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.00386 accuracy multiclass 0.627 5 0.00498 Preprocessor1_Model16
2 0.00259 accuracy multiclass 0.626 5 0.00500 Preprocessor1_Model15
3 0.00574 accuracy multiclass 0.624 5 0.00467 Preprocessor1_Model17
4 0.00174 accuracy multiclass 0.618 5 0.00415 Preprocessor1_Model14
5 0.00853 accuracy multiclass 0.618 5 0.00424 Preprocessor1_Model18# confusion matrix for best penalty value
conf_mat_resampled(
x = ngram_tune_rs,
parameters = select_best(ngram_tune_rs, metric = "roc_auc"),
tidy = FALSE
) |>
autoplot(type = "heatmap")
# most frequent misclassifications (average)
conf_mat_resampled(
x = ngram_tune_rs,
parameters = select_best(glmnet_tune_rs, metric = "roc_auc")
) |>
# filter out correct predictions and sort by frequency
filter(Prediction != Truth) |>
arrange(-Freq)# A tibble: 380 × 3
Prediction Truth Freq
<fct> <fct> <dbl>
1 Macroeconomics Government operations 24.4
2 Macroeconomics Banking, finance, and d… 24
3 Civil rights, minority issues, civil liberties Government operations 22.8
4 Law, crime, family issues Government operations 19
5 Environment Public lands and water … 17
6 International affairs and foreign aid Defense 16.2
7 Public lands and water management Government operations 15
8 International affairs and foreign aid Government operations 14.8
9 Defense Government operations 14.6
10 Labor and employment Government operations 14.4
# ℹ 370 more rowsAdd response here. Not a significant improvement over the unigram model. It still appears to make many of the same types of mistakes.
Variable importance
Your turn: Calculate which tokens are used by the model to predict each of the policy labels. Visualize the top 10 tokens for each policy label. Do these tokens make sense as useful predictors?
# fit the best model
ngram_best <- ngram_tune_rs |>
fit_best()# multiclass classification - need to manually extract coefficients for each
# outcome class
ngram_vip <- coef(extract_fit_engine(ngram_best),
s = select_best(x = ngram_tune_rs, metric = "roc_auc")$penalty
) |>
# need first element from each sublist
map(\(x) x[, 1L]) |>
# convert to a data frame and extract the relevant pieces of information
enframe() |>
unnest_longer(value) |>
rename(
token = value_id,
importance = value,
class = name
) |>
# ignore the intercept
filter(token != "(Intercept)") |>
# clean up data to focus purely on magnitude of coefficients
mutate(
sign = ifelse(sign(importance) == 1, "POS", "NEG"),
importance = abs(importance),
token = str_remove_all(token, "tfidf_description_")
) |>
# remove anything with importance of 0
filter(importance != 0)
# visualize the top 10 tokens for each class
ngram_vip |>
# keep the top 10 coefficients for each
filter(sign == "POS") |>
slice_max(n = 10, order_by = importance, by = class) |>
# change order of token levels for plotting
mutate(token = reorder_within(token, by = importance, within = class)) |>
ggplot(mapping = aes(
x = importance,
y = token
)) +
geom_col() +
scale_y_reordered() +
facet_wrap(
facets = vars(class),
scales = "free",
ncol = 4,
labeller = labeller(class = label_wrap_gen(20))
) +
labs(
title = "Most important tokens for each policy class",
x = "Importance",
y = NULL
)
Add response here. These are improvements over the unigram model. Now we see extended phrases such as “Title 38”, “28 United States” (most likely referring to “28 United States Code”), and “labor standards”. These are more likely to be unique to the policy areas and thus more useful for prediction.
Word embeddings
Finally we will fit a lasso regression model using word embeddings extracted from the GLoVE model to represent each description.
Define recipe
Your turn: Import the GLoVE 6b embeddings for 100 dimensions. Define a recipe that uses these embeddings to predict policy_lab.
##### uncomment if you are running RStudio on your personal computer
# extract 100 dimensions from GLoVE
glove6b <- embedding_glove6b(dimensions = 100)
# ##### uncomment if you are running RStudio on the Workbench
# # hacky way to make it work on RStudio Workbench
# glove6b <- read_delim(
# file = "/rstudio-files/glove6b/glove.6B.100d.txt",
# delim = " ",
# quote = "",
# col_names = c(
# "token",
# paste0("d", seq_len(100))
# ),
# col_types = paste0(
# c(
# "c",
# rep("d", 100)
# ),
# collapse = ""
# )
# )# initialize recipe
embed_rec <- recipe(policy_lab ~ description, data = leg_train) |>
# tokenize
step_tokenize(description) |>
# convert to word embeddings
step_word_embeddings(description, embeddings = glove6b) |>
# normalize the columns
step_zv(all_predictors()) |>
step_normalize(all_predictors()) |>
# downsample to keep same number of rows for each policy focus
step_downsample(policy_lab)# A tibble: 4,960 × 101
policy_lab wordembed_description_d1 wordembed_description_d2
<fct> <dbl> <dbl>
1 Macroeconomics 0.727 -0.458
2 Macroeconomics -0.497 -0.685
3 Macroeconomics -0.409 -0.832
4 Macroeconomics -0.461 -0.325
5 Macroeconomics 0.605 1.13
6 Macroeconomics -0.486 -0.882
7 Macroeconomics -1.47 0.234
8 Macroeconomics 0.679 -0.283
9 Macroeconomics -0.213 -0.702
10 Macroeconomics -0.381 -0.185
# ℹ 4,950 more rows
# ℹ 98 more variables: wordembed_description_d3 <dbl>,
# wordembed_description_d4 <dbl>, wordembed_description_d5 <dbl>,
# wordembed_description_d6 <dbl>, wordembed_description_d7 <dbl>,
# wordembed_description_d8 <dbl>, wordembed_description_d9 <dbl>,
# wordembed_description_d10 <dbl>, wordembed_description_d11 <dbl>,
# wordembed_description_d12 <dbl>, wordembed_description_d13 <dbl>, …Tune the penalized regression model
Demonstration: Tune the model.
Note
Since this model uses word embeddings, we will not use the sparse blueprint for the recipe. The embeddings are already in a dense format.
# define workflow
embed_wf <- workflow() |>
add_recipe(embed_rec) |>
add_model(glmnet_spec)
tic()
embed_tune_rs <- tune_grid(
object = embed_wf,
resamples = leg_folds,
grid = penalty_grid,
control = control_grid(save_pred = TRUE, save_workflow = TRUE)
)
toc()29.58 sec elapsedYour turn: Examine the performance of the model, both overall and using the confusion matrix. Which misclassifications are most common? Why might the model have a hard time discriminating between these policy labels?
# view average metrics
autoplot(embed_tune_rs)
# identify best models based on assessment set
show_best(x = embed_tune_rs, metric = "roc_auc")# A tibble: 5 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.000788 roc_auc hand_till 0.943 5 0.000581 Preprocessor1_Model12
2 0.000530 roc_auc hand_till 0.943 5 0.000371 Preprocessor1_Model11
3 0.00117 roc_auc hand_till 0.942 5 0.000820 Preprocessor1_Model13
4 0.000356 roc_auc hand_till 0.942 5 0.000298 Preprocessor1_Model10
5 0.000240 roc_auc hand_till 0.941 5 0.000274 Preprocessor1_Model09show_best(x = embed_tune_rs, metric = "accuracy")# A tibble: 5 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.000530 accuracy multiclass 0.608 5 0.00314 Preprocessor1_Model11
2 0.000356 accuracy multiclass 0.608 5 0.00342 Preprocessor1_Model10
3 0.000240 accuracy multiclass 0.608 5 0.00345 Preprocessor1_Model09
4 0.000788 accuracy multiclass 0.605 5 0.00195 Preprocessor1_Model12
5 0.000161 accuracy multiclass 0.603 5 0.00308 Preprocessor1_Model08# confusion matrix for best penalty value
conf_mat_resampled(
x = embed_tune_rs,
parameters = select_best(embed_tune_rs, metric = "roc_auc"),
tidy = FALSE
) |>
autoplot(type = "heatmap")
# most frequent misclassifications (average)
conf_mat_resampled(
x = embed_tune_rs,
parameters = select_best(glmnet_tune_rs, metric = "roc_auc")
) |>
# filter out correct predictions and sort by frequency
filter(Prediction != Truth) |>
arrange(-Freq)# A tibble: 380 × 3
Prediction Truth Freq
<fct> <fct> <dbl>
1 Labor and employment Government operations 36.4
2 Civil rights, minority issues, civil liberties Government operations 36
3 Defense Government operations 29.2
4 Environment Public lands and water … 28.4
5 Law, crime, family issues Government operations 24
6 Macroeconomics Banking, finance, and d… 23.6
7 International affairs and foreign aid Defense 21.6
8 Macroeconomics Government operations 21.4
9 Banking, finance, and domestic commerce Government operations 20.8
10 Civil rights, minority issues, civil liberties Law, crime, family issu… 20.4
# ℹ 370 more rowsAdd response here. It took a bit longer to train this model even though it has fewer features, most likely because of its dense structure hence we could not take advantage of structuring the data as a sparse matrix. We see a \(0.03\) increase in ROC AUC compared to the first two models. Still similar types of mistakes compared to the earlier models.
Variable importance
Your turn: Calculate which dimensions are used by the model to predict each of the policy labels. Visualize the top 10 dimensions for each policy label. How useful is this analysis?
# fit the best model
embed_best <- embed_tune_rs |>
fit_best()# multiclass classification - need to manually extract coefficients for each
# outcome class
embed_vip <- coef(extract_fit_engine(embed_best),
s = select_best(x = embed_tune_rs, metric = "roc_auc")$penalty
) |>
# need first element from each sublist
map(\(x) x[, 1L]) |>
# convert to a data frame and extract the relevant pieces of information
enframe() |>
unnest_longer(value) |>
rename(
token = value_id,
importance = value,
class = name
) |>
# ignore the intercept
filter(token != "(Intercept)") |>
# clean up data to focus purely on magnitude of coefficients
mutate(
sign = ifelse(sign(importance) == 1, "POS", "NEG"),
importance = abs(importance),
token = str_remove_all(token, "wordembed_description_")
) |>
# remove anything with importance of 0
filter(importance != 0)
# visualize the top 10 tokens for each class
embed_vip |>
# keep the top 10 coefficients for each
filter(sign == "POS") |>
slice_max(n = 10, order_by = importance, by = class) |>
# change order of token levels for plotting
mutate(token = reorder_within(token, by = importance, within = class)) |>
ggplot(mapping = aes(
x = importance,
y = token
)) +
geom_col() +
scale_y_reordered() +
facet_wrap(
facets = vars(class),
scales = "free",
ncol = 4,
labeller = labeller(class = label_wrap_gen(20))
) +
labs(
title = "Most important dimensions for each policy class",
x = "Importance",
y = NULL
)
Add response here. Unfortunately this form of analysis is now useless. There is no intuitive interpretation of each of the word embeddings dimensions since they don’t have inherent semantic meaning, and there are far too many of them to interpret even if they did. If we want to identify the most important or useful tokens, we have to use another method.
Session information
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14)
os macOS Sonoma 14.6.1
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2024-10-18
pandoc 3.4 @ /usr/local/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
arrow * 17.0.0 2024-09-18 [1] https://apache.r-universe.dev (R 4.4.1)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.3.0)
backports 1.5.0 2024-05-23 [1] CRAN (R 4.4.0)
bit 4.0.5 2022-11-15 [1] CRAN (R 4.3.0)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.3.0)
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farver 2.1.2 2024-05-13 [1] CRAN (R 4.3.3)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
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gower 1.0.1 2022-12-22 [1] CRAN (R 4.3.0)
GPfit 1.0-8 2019-02-08 [1] CRAN (R 4.3.0)
gtable 0.3.5 2024-04-22 [1] CRAN (R 4.3.1)
hardhat * 1.4.0 2024-06-02 [1] CRAN (R 4.4.0)
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hms 1.1.3 2023-03-21 [1] CRAN (R 4.3.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.3.1)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.3.1)
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knitr 1.47 2024-05-29 [1] CRAN (R 4.4.0)
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lhs 1.1.6 2022-12-17 [1] CRAN (R 4.3.0)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.1)
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modeldata * 1.4.0 2024-06-19 [1] CRAN (R 4.4.0)
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nnet 7.3-19 2023-05-03 [1] CRAN (R 4.4.0)
parallelly 1.37.1 2024-02-29 [1] CRAN (R 4.3.1)
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pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0)
prodlim 2023.08.28 2023-08-28 [1] CRAN (R 4.3.0)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.3.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.0)
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Rcpp 1.0.13 2024-07-17 [1] CRAN (R 4.4.0)
readr * 2.1.5 2024-01-10 [1] CRAN (R 4.3.1)
recipes * 1.0.10 2024-02-18 [1] CRAN (R 4.3.1)
rlang 1.1.4 2024-06-04 [1] CRAN (R 4.3.3)
rmarkdown 2.27 2024-05-17 [1] CRAN (R 4.4.0)
ROSE 0.0-4 2021-06-14 [1] CRAN (R 4.3.0)
rpart 4.1.23 2023-12-05 [1] CRAN (R 4.4.0)
rprojroot 2.0.4 2023-11-05 [1] CRAN (R 4.3.1)
rsample * 1.2.1 2024-03-25 [1] CRAN (R 4.3.1)
rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.3.1)
scales * 1.3.0 2023-11-28 [1] CRAN (R 4.4.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.0)
shape 1.4.6.1 2024-02-23 [1] CRAN (R 4.3.1)
SnowballC 0.7.1 2023-04-25 [1] CRAN (R 4.3.0)
stopwords 2.3 2021-10-28 [1] CRAN (R 4.3.0)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.3.1)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.3.1)
survival 3.7-0 2024-06-05 [1] CRAN (R 4.4.0)
textdata * 0.4.5 2024-05-28 [1] CRAN (R 4.4.0)
textrecipes * 1.0.6 2023-11-15 [1] CRAN (R 4.3.1)
themis * 1.0.2 2023-08-14 [1] CRAN (R 4.3.0)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.3.0)
tictoc * 1.2.1 2024-03-18 [1] CRAN (R 4.4.0)
tidymodels * 1.2.0 2024-03-25 [1] CRAN (R 4.3.1)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.3.1)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.3.1)
tidytext * 0.4.2 2024-04-10 [1] CRAN (R 4.4.0)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.3.0)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.3.1)
timeDate 4032.109 2023-12-14 [1] CRAN (R 4.3.1)
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tune * 1.2.1 2024-04-18 [1] CRAN (R 4.3.1)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.3.0)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.1)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.1)
vip * 0.4.1 2023-08-21 [1] CRAN (R 4.3.0)
withr 3.0.1 2024-07-31 [1] CRAN (R 4.4.0)
workflows * 1.1.4 2024-02-19 [1] CRAN (R 4.3.1)
workflowsets * 1.1.0 2024-03-21 [1] CRAN (R 4.3.1)
xfun 0.45 2024-06-16 [1] CRAN (R 4.4.0)
yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.0)
yardstick * 1.3.1 2024-03-21 [1] CRAN (R 4.3.1)
[1] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
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