Apply2
Maddie Westbrook
2024-02-18
Goal: to predict the average rating of horror movies. Click here for the data
Import Data
horror_movies <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-11-01/horror_movies.csv')## Rows: 32540 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): original_title, title, original_language, overview, tagline, post...
## dbl (8): id, popularity, vote_count, vote_average, budget, revenue, runtim...
## lgl (1): adult
## date (1): release_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.skimr::skim(horror_movies)| Name | horror_movies |
| Number of rows | 32540 |
| Number of columns | 20 |
| _______________________ | |
| Column type frequency: | |
| character | 10 |
| Date | 1 |
| logical | 1 |
| numeric | 8 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| original_title | 0 | 1.00 | 1 | 191 | 0 | 30296 | 0 |
| title | 0 | 1.00 | 1 | 191 | 0 | 29563 | 0 |
| original_language | 0 | 1.00 | 2 | 2 | 0 | 97 | 0 |
| overview | 1286 | 0.96 | 1 | 1000 | 0 | 31020 | 0 |
| tagline | 19835 | 0.39 | 1 | 237 | 0 | 12513 | 0 |
| poster_path | 4474 | 0.86 | 30 | 32 | 0 | 28048 | 0 |
| status | 0 | 1.00 | 7 | 15 | 0 | 4 | 0 |
| backdrop_path | 18995 | 0.42 | 29 | 32 | 0 | 13536 | 0 |
| genre_names | 0 | 1.00 | 6 | 144 | 0 | 772 | 0 |
| collection_name | 30234 | 0.07 | 4 | 56 | 0 | 815 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| release_date | 0 | 1 | 1950-01-01 | 2022-12-31 | 2012-12-09 | 10999 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| adult | 0 | 1 | 0 | FAL: 32540 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1.00 | 445910.83 | 305744.67 | 17 | 146494.8 | 426521.00 | 707534.00 | 1033095.00 | ▇▆▆▅▅ |
| popularity | 0 | 1.00 | 4.01 | 37.51 | 0 | 0.6 | 0.84 | 2.24 | 5088.58 | ▇▁▁▁▁ |
| vote_count | 0 | 1.00 | 62.69 | 420.89 | 0 | 0.0 | 2.00 | 11.00 | 16900.00 | ▇▁▁▁▁ |
| vote_average | 0 | 1.00 | 3.34 | 2.88 | 0 | 0.0 | 4.00 | 5.70 | 10.00 | ▇▂▆▃▁ |
| budget | 0 | 1.00 | 543126.59 | 4542667.81 | 0 | 0.0 | 0.00 | 0.00 | 200000000.00 | ▇▁▁▁▁ |
| revenue | 0 | 1.00 | 1349746.73 | 14430479.15 | 0 | 0.0 | 0.00 | 0.00 | 701842551.00 | ▇▁▁▁▁ |
| runtime | 0 | 1.00 | 62.14 | 41.00 | 0 | 14.0 | 80.00 | 91.00 | 683.00 | ▇▁▁▁▁ |
| collection | 30234 | 0.07 | 481534.88 | 324498.16 | 656 | 155421.0 | 471259.00 | 759067.25 | 1033032.00 | ▇▅▅▅▅ |
data2 <- horror_movies %>%
# Treat Missing Values
select(-tagline, -collection_name, -collection, -backdrop_path) %>%
filter(budget != 0) %>%
filter(revenue != 0) %>%
na.omit() %>%
# Log Transform Variables with Pos-Skewed Distribution
mutate(vote_average = log(vote_average)) %>%
mutate(popularity = log(popularity)) %>%
mutate(vote_count = log(vote_count)) %>%
mutate(budget = log(budget)) %>%
mutate(revenue = log(revenue))
skimr::skim(data2)## Warning: There was 1 warning in `dplyr::summarize()`.
## ℹ In argument: `dplyr::across(variable_names, mangled_skimmers$funs)`.
## ℹ In group 0: .
## Caused by warning:
## ! There were 2 warnings in `dplyr::summarize()`.
## The first warning was:
## ℹ In argument: `dplyr::across(variable_names, mangled_skimmers$funs)`.
## Caused by warning in `inline_hist()`:
## ! Variable contains Inf or -Inf value(s) that were converted to NA.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.| Name | data2 |
| Number of rows | 1065 |
| Number of columns | 16 |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| Date | 1 |
| logical | 1 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| original_title | 0 | 1 | 1 | 50 | 0 | 1031 | 0 |
| title | 0 | 1 | 1 | 50 | 0 | 1025 | 0 |
| original_language | 0 | 1 | 2 | 2 | 0 | 29 | 0 |
| overview | 0 | 1 | 17 | 991 | 0 | 1063 | 0 |
| poster_path | 0 | 1 | 30 | 32 | 0 | 1065 | 0 |
| status | 0 | 1 | 8 | 8 | 0 | 1 | 0 |
| genre_names | 0 | 1 | 6 | 84 | 0 | 133 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| release_date | 0 | 1 | 1953-04-16 | 2022-10-05 | 2007-01-25 | 986 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| adult | 0 | 1 | 0 | FAL: 1065 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1 | 173676.13 | 251477.46 | 83.00 | 10676.00 | 29056.00 | 292106.00 | 1032494.00 | ▇▁▁▁▁ |
| popularity | 0 | 1 | 2.60 | 1.36 | -0.51 | 2.09 | 2.78 | 3.47 | 7.53 | ▂▆▇▁▁ |
| vote_count | 0 | 1 | -Inf | NaN | -Inf | 4.91 | 6.33 | 7.38 | 9.74 | ▁▁▃▇▃ |
| vote_average | 0 | 1 | -Inf | NaN | -Inf | 1.69 | 1.79 | 1.89 | 2.30 | ▁▁▂▇▁ |
| budget | 0 | 1 | 14.60 | 3.44 | 0.00 | 14.00 | 15.61 | 16.52 | 19.11 | ▁▁▁▅▇ |
| revenue | 0 | 1 | 15.13 | 3.86 | 0.00 | 13.83 | 16.30 | 17.64 | 20.37 | ▁▁▂▅▇ |
| runtime | 0 | 1 | 93.81 | 23.94 | 0.00 | 88.00 | 96.00 | 104.00 | 179.00 | ▁▁▇▂▁ |
Explore Data
Identify good predictors
Revenue
data2 %>%
ggplot(aes(vote_average, revenue)) +
geom_point()
Popularity
data2 %>%
ggplot(aes(vote_average, popularity)) +
geom_point()
Budget
data2 %>%
ggplot(aes(vote_average, budget)) +
geom_point()
EDA Shortcut
# Step 1: Prepare data
data_binarized_tbl2 <- data2 %>%
select(-id, -original_title, -title, -overview, -poster_path, -genre_names, -release_date) %>%
binarize()
data_binarized_tbl2 %>% glimpse()## Rows: 1,065
## Columns: 31
## $ original_language__en <dbl> 1, 1, 1, 1, 1, 1, 1, 1…
## $ original_language__es <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ original_language__fr <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ original_language__hi <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ original_language__ja <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ original_language__ko <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ `original_language__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ `popularity__-Inf_2.08902051646016` <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ popularity__2.08902051646016_2.77589574799728 <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ popularity__2.77589574799728_3.47196645255036 <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ popularity__3.47196645255036_Inf <dbl> 1, 1, 1, 1, 1, 1, 1, 1…
## $ `vote_count__-Inf_4.91265488573605` <dbl> 1, 0, 1, 0, 0, 0, 0, 0…
## $ vote_count__4.91265488573605_6.32793678372919 <dbl> 0, 0, 0, 0, 0, 1, 0, 0…
## $ vote_count__6.32793678372919_7.38274644973891 <dbl> 0, 0, 0, 0, 1, 0, 0, 0…
## $ vote_count__7.38274644973891_Inf <dbl> 0, 1, 0, 1, 0, 0, 1, 1…
## $ `vote_average__-Inf_1.68639895357023` <dbl> 0, 0, 0, 0, 0, 1, 0, 0…
## $ vote_average__1.68639895357023_1.79175946922805 <dbl> 0, 0, 1, 0, 0, 0, 0, 0…
## $ vote_average__1.79175946922805_1.88706964903238 <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ vote_average__1.88706964903238_Inf <dbl> 1, 1, 0, 1, 1, 0, 1, 1…
## $ `budget__-Inf_13.9978321147582` <dbl> 0, 0, 0, 0, 0, 1, 0, 0…
## $ budget__13.9978321147582_15.6072700271923 <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ budget__15.6072700271923_16.5235607590665 <dbl> 0, 0, 0, 0, 1, 0, 0, 0…
## $ budget__16.5235607590665_Inf <dbl> 1, 1, 1, 1, 0, 0, 1, 1…
## $ `revenue__-Inf_13.8327618942679` <dbl> 0, 0, 0, 0, 0, 1, 0, 0…
## $ revenue__13.8327618942679_16.3018635779211 <dbl> 0, 0, 1, 0, 0, 0, 0, 0…
## $ revenue__16.3018635779211_17.6419756750308 <dbl> 1, 0, 0, 0, 1, 0, 0, 0…
## $ revenue__17.6419756750308_Inf <dbl> 0, 1, 0, 1, 0, 0, 1, 1…
## $ `runtime__-Inf_88` <dbl> 0, 0, 1, 0, 0, 0, 0, 0…
## $ runtime__88_96 <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ runtime__96_104 <dbl> 0, 1, 0, 0, 0, 1, 0, 0…
## $ runtime__104_Inf <dbl> 1, 0, 0, 1, 1, 0, 1, 1…# Step 2: Correlate
data_corr_tbl2 <- data_binarized_tbl2 %>%
correlate(vote_average__1.88706964903238_Inf)
data_corr_tbl2## # A tibble: 31 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 vote_average 1.88706964903238_Inf 1
## 2 vote_average -Inf_1.68639895357023 -0.341
## 3 vote_average 1.79175946922805_1.88706964903238 -0.319
## 4 vote_average 1.68639895357023_1.79175946922805 -0.311
## 5 runtime 104_Inf 0.253
## 6 vote_count 7.38274644973891_Inf 0.219
## 7 vote_count 4.91265488573605_6.32793678372919 -0.155
## 8 runtime 88_96 -0.134
## 9 original_language ja 0.125
## 10 original_language en -0.118
## # ℹ 21 more rows# Step 3: Plot
data_corr_tbl2 %>%
plot_correlation_funnel()## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Build Models
Split Data
set.seed(1234)
data2 <- sample_n(data2, 100)
# Split into train and testing data set
data_split2 <- rsample::initial_split(data2)
data_train2 <- training(data_split2)
data_test2 <- testing(data_split2)
# Further split training data set for cross-validation
set.seed(2345)
data_cv2 <- rsample::vfold_cv(data_train2)
data_cv2## # 10-fold cross-validation
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [67/8]> Fold01
## 2 <split [67/8]> Fold02
## 3 <split [67/8]> Fold03
## 4 <split [67/8]> Fold04
## 5 <split [67/8]> Fold05
## 6 <split [68/7]> Fold06
## 7 <split [68/7]> Fold07
## 8 <split [68/7]> Fold08
## 9 <split [68/7]> Fold09
## 10 <split [68/7]> Fold10library(usemodels)## Warning: package 'usemodels' was built under R version 4.2.3usemodels::use_xgboost(vote_average ~ ., data = data_train2)## xgboost_recipe <-
## recipe(formula = vote_average ~ ., data = data_train2) %>%
## step_zv(all_predictors())
##
## xgboost_spec <-
## boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
## loss_reduction = tune(), sample_size = tune()) %>%
## set_mode("classification") %>%
## set_engine("xgboost")
##
## xgboost_workflow <-
## workflow() %>%
## add_recipe(xgboost_recipe) %>%
## add_model(xgboost_spec)
##
## set.seed(6804)
## xgboost_tune <-
## tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))# Specify Recipe
xgboost_recipe <-
recipe(formula = vote_average ~ ., data = data_train2) %>%
update_role(id, new_role = "id variable") %>%
step_tokenize(overview, poster_path, genre_names) %>%
step_tokenfilter(overview, poster_path, genre_names, max_tokens = 100) %>%
step_tfidf(overview, poster_path, genre_names) %>%
step_other(original_title, title, original_language, status) %>%
step_dummy(original_title, title, original_language, one_hot = TRUE) %>%
step_YeoJohnson(popularity, vote_count, budget, revenue, runtime, vote_average) %>%
step_rm(release_date, status, adult)
xgboost_recipe %>% prep() %>% juice() %>% glimpse()## Warning: max_tokens was set to '100', but only 76 was available and selected.## Warning: max_tokens was set to '100', but only 13 was available and selected.## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
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## Columns: 202
## $ id <dbl> 452343, 8852, 173897…
## $ popularity <dbl> -0.4931408, 2.816377…
## $ vote_count <dbl> -Inf, 6.630683, 7.87…
## $ budget <dbl> 899.52634, 4182.5174…
## $ revenue <dbl> 197.68245, 845.73094…
## $ runtime <dbl> 3948.0788, 5854.0842…
## $ vote_average <dbl> -Inf, 1.916923, 1.80…
## $ tfidf_overview_a <dbl> 0.07433369, 0.202728…
## $ tfidf_overview_about <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_after <dbl> 0.07994279, 0.000000…
## $ tfidf_overview_again <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_against <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_an <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_and <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_are <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_as <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_at <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_baba <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_overview_be <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_been <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_begins <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_being <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_overview_body <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_overview_but <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_by <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_charlie <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_overview_city <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_creature <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_dark <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_overview_daughter <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_day <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_overview_dead <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_overview_deadly <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_death <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_escape <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_events <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_evil <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_family <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_find <dbl> 0.10700331, 0.000000…
## $ tfidf_overview_finds <dbl> 0.00000000, 0.236608…
## $ tfidf_overview_first <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_for <dbl> 0.08211139, 0.000000…
## $ tfidf_overview_four <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_friends <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_from <dbl> 0.07790723, 0.000000…
## $ tfidf_overview_group <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_has <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_have <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_he <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_her <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_him <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_his <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_house <dbl> 0.0000000, 0.0000000…
## $ tfidf_overview_in <dbl> 0.04464717, 0.081176…
## $ tfidf_overview_into <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_is <dbl> 0.06035465, 0.000000…
## $ tfidf_overview_it <dbl> 0.00000000, 0.203053…
## $ tfidf_overview_its <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_killed <dbl> 0.00000000, 0.000000…
## $ tfidf_overview_killer <dbl> 0, 0, 0, 0, 0, 0, 0,…
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## $ tfidf_poster_path_ysipmxlvyyvtuaowesljf7nef4c.jpg <dbl> 0.000000, 0.000000, …
## $ tfidf_poster_path_yslrbb6sdqpeydttbjostaagf0z.jpg <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_poster_path_z3bibks7fksubvr2yj1vajhx7bf.jpg <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_genre_names_action <dbl> 0.0000000, 0.0000000…
## $ tfidf_genre_names_adventure <dbl> 0.0000000, 0.0000000…
## $ tfidf_genre_names_comedy <dbl> 0.0000000, 0.0000000…
## $ tfidf_genre_names_crime <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_genre_names_drama <dbl> 1.028226, 0.000000, …
## $ tfidf_genre_names_fantasy <dbl> 0.0000000, 0.0000000…
## $ tfidf_genre_names_fiction <dbl> 0.0000000, 0.0000000…
## $ tfidf_genre_names_horror <dbl> 0.3465736, 0.6931472…
## $ tfidf_genre_names_music <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_genre_names_mystery <dbl> 0.0000000, 0.0000000…
## $ tfidf_genre_names_romance <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_genre_names_science <dbl> 0.0000000, 0.0000000…
## $ tfidf_genre_names_thriller <dbl> 0.0000000, 0.0000000…
## $ original_title_X.REC. <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ original_title_other <dbl> 1, 1, 1, 1, 1, 1, 1,…
## $ title_X.REC. <dbl> 0, 0, 0, 0, 0, 0, 0,…
## $ title_other <dbl> 1, 1, 1, 1, 1, 1, 1,…
## $ original_language_en <dbl> 0, 1, 1, 1, 1, 1, 1,…
## $ original_language_other <dbl> 1, 0, 0, 0, 0, 0, 0,…# Specify model
xgboost_spec <-
boost_tree(trees = tune(), min_n = tune(), mtry = tune(), learn_rate = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")
# Combine recipe and model using workflow
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_recipe) %>%
add_model(xgboost_spec)
# Tune hyperparameters
set.seed(6804)
xgboost_tune <-
tune_grid(xgboost_workflow, resamples = data_cv2, grid = 5)## i Creating pre-processing data to finalize unknown parameter: mtry## Warning: max_tokens was set to '100', but only 76 was available and selected.## Warning: max_tokens was set to '100', but only 13 was available and selected.## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value
## Warning in optimize(yj_obj, interval = limits, maximum = TRUE, dat = dat, :
## NA/Inf replaced by maximum positive value## → A | warning: max_tokens was set to '100', but only 68 was available and selected., max_tokens was set to '100', but only 13 was available and selected., NA/Inf replaced by maximum positive value##
There were issues with some computations A: x1
→ B | error: [11:26:18] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x1
There were issues with some computations A: x1 B: x1
→ C | error: [11:26:19] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x1 B: x1
There were issues with some computations A: x1 B: x1 C: x1
There were issues with some computations A: x1 B: x1 C: x2
→ D | error: [11:26:20] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x1 B: x1 C: x2
There were issues with some computations A: x1 B: x1 C: x2 D: x1
→ E | error: [11:26:21] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x1 B: x1 C: x2 D: x1
There were issues with some computations A: x1 B: x1 C: x2 D: x1 E: x1
There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x1
→ F | error: [11:26:24] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x1
There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x…
→ G | error: [11:26:25] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x…
→ H | error: [11:26:26] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x…
→ I | error: [11:26:27] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x2 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ J | error: [11:26:30] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ K | error: [11:26:31] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ L | error: [11:26:32] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ M | error: [11:26:33] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ N | error: [11:26:34] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ O | warning: max_tokens was set to '100', but only 68 was available and selected., max_tokens was set to '100', but only 12 was available and selected., NA/Inf replaced by maximum positive value
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ P | error: [11:26:37] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ Q | error: [11:26:38] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ R | error: [11:26:39] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ S | error: [11:26:40] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
→ T | error: [11:26:41] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x3 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ U | error: [11:26:44] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ V | error: [11:26:45] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ W | error: [11:26:46] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ X | error: [11:26:47] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ Y | error: [11:26:48] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ Z | warning: max_tokens was set to '100', but only 69 was available and selected., max_tokens was set to '100', but only 13 was available and selected., NA/Inf replaced by maximum positive value
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ a | error: [11:26:51] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ b | error: [11:26:52] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ c | error: [11:26:53] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ d | error: [11:26:54] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ e | error: [11:26:55] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ f | error: [11:26:58] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ g | error: [11:26:59] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ h | error: [11:27:00] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ i | error: [11:27:01] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ j | error: [11:27:02] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ k | error: [11:27:05] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ l | error: [11:27:06] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ m | error: [11:27:07] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ n | error: [11:27:08] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ o | error: [11:27:09] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ p | error: [11:27:13] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ q | error: [11:27:14] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ r | error: [11:27:15] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ s | error: [11:27:16] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ t | error: [11:27:17] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ u | error: [11:27:20] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ v | error: [11:27:21] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ w | error: [11:27:22] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ x | error: [11:27:23] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
→ y | error: [11:27:24] src/data/data.cc:1104: Check failed: valid: Input data contains `inf` or `nan`
## There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…
There were issues with some computations A: x4 B: x1 C: x2 D: x1 E: x…## Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
## information.