transition to snapshot testing

tests/testthat/_snaps/adds.md

@@ -0,0 +1,8 @@
+# adding row indicies
+
+    Code
+      as.matrix(mtcars) %>% add_rowindex()
+    Condition
+      Error in `add_rowindex()`:
+      ! `x` should be a data frame.
+

tests/testthat/_snaps/args_and_modes.md

@@ -1,3 +1,35 @@
+# pipe arguments
+
+    Code
+      rand_forest() %>% set_args()
+    Condition
+      Error in `set_args()`:
+      ! Please pass at least one named argument.
+
+# pipe engine
+
+    Code
+      rand_forest() %>% set_mode()
+    Condition
+      Error in `set_mode()`:
+      ! Available modes for model type rand_forest are: "unknown", "classification", "regression", and "censored regression".
+
+---
+
+    Code
+      rand_forest() %>% set_mode(2)
+    Condition
+      Error in `set_mode()`:
+      ! 2 is not a known mode for model `rand_forest()`.
+
+---
+
+    Code
+      rand_forest() %>% set_mode("haberdashery")
+    Condition
+      Error in `set_mode()`:
+      ! "haberdashery" is not a known mode for model `rand_forest()`.
+
 # can't set a mode that isn't allowed by the model spec
 
     Code

tests/testthat/_snaps/boost_tree.md

@@ -23,6 +23,29 @@
       Error in `boost_tree()`:
       ! "bogus" is not a known mode for model `boost_tree()`.
 
+---
+
+    Code
+      translate(boost_tree(mode = "classification"), engine = NULL)
+    Message
+      Used `engine = 'xgboost'` for translation.
+    Output
+      Boosted Tree Model Specification (classification)
+
+      Computational engine: xgboost 
+
+      Model fit template:
+      parsnip::xgb_train(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
+          nthread = 1, verbose = 0)
+
+---
+
+    Code
+      translate(boost_tree(formula = y ~ x))
+    Condition
+      Error in `boost_tree()`:
+      ! unused argument (formula = y ~ x)
+
 # check_args() works
 
     Code

tests/testthat/_snaps/boost_tree_C5.0.md

@@ -1,3 +1,12 @@
+# C5.0 execution
+
+    Code
+      res <- fit(lc_basic, funded_amnt ~ term, data = lending_club, engine = "C5.0",
+      control = ctrl)
+    Condition
+      Error in `glue()`:
+      ! Expecting '}'
+
 # submodel prediction
 
     Code
@@ -6,3 +15,21 @@
       Error in `multi_predict()`:
       ! Please use `new_data` instead of `newdata`.
 
+# argument checks for data dimensions
+
+    Code
+      f_fit <- spec %>% fit(species ~ ., data = penguins)
+    Condition
+      Warning:
+      ! 1000 samples were requested but there were 333 rows in the data.
+      i 333 will be used.
+
+---
+
+    Code
+      xy_fit <- spec %>% fit_xy(x = penguins[, -1], y = penguins$species)
+    Condition
+      Warning:
+      ! 1000 samples were requested but there were 333 rows in the data.
+      i 333 will be used.
+

tests/testthat/_snaps/boost_tree_xgboost.md

@@ -1,3 +1,20 @@
+# xgboost execution, classification
+
+    Code
+      res <- parsnip::fit(hpc_xgboost, class ~ novar, data = hpc, control = ctrl)
+    Condition
+      Error:
+      ! object 'novar' not found
+
+# xgboost execution, regression
+
+    Code
+      res <- parsnip::fit_xy(car_basic, x = mtcars[, num_pred], y = factor(mtcars$vs),
+      control = ctrl)
+    Condition
+      Error in `check_outcome()`:
+      ! For a regression model, the outcome should be `numeric`, not a `factor`.
+
 # submodel prediction
 
     Code
@@ -6,6 +23,35 @@
       Error in `multi_predict()`:
       ! Please use `new_data` instead of `newdata`.
 
+# validation sets
+
+    Code
+      reg_fit <- boost_tree(trees = 20, mode = "regression") %>% set_engine("xgboost",
+        validation = 3) %>% fit(mpg ~ ., data = mtcars[-(1:4), ])
+    Condition
+      Error in `parsnip::xgb_train()`:
+      ! `validation` should be on [0, 1).
+
+# early stopping
+
+    Code
+      reg_fit <- boost_tree(trees = 20, stop_iter = 30, mode = "regression") %>%
+        set_engine("xgboost", validation = 0.1) %>% fit(mpg ~ ., data = mtcars[-(1:4),
+      ])
+    Condition
+      Warning:
+      `early_stop` was reduced to 19.
+
+---
+
+    Code
+      reg_fit <- boost_tree(trees = 20, stop_iter = 0, mode = "regression") %>%
+        set_engine("xgboost", validation = 0.1) %>% fit(mpg ~ ., data = mtcars[-(1:4),
+      ])
+    Condition
+      Error in `parsnip::xgb_train()`:
+      ! `early_stop` should be on [2, 20).
+
 # xgboost data conversion
 
     Code
@@ -14,6 +60,34 @@
       Warning:
       `event_level` can only be set for binary outcomes.
 
+# argument checks for data dimensions
+
+    Code
+      f_fit <- spec %>% fit(species ~ ., data = penguins, control = ctrl)
+    Condition
+      Warning:
+      ! 1000 samples were requested but there were 333 rows in the data.
+      i 333 will be used.
+
+---
+
+    Code
+      xy_fit <- spec %>% fit_xy(x = penguins_dummy, y = penguins$species, control = ctrl)
+    Condition
+      Warning:
+      ! 1000 samples were requested but there were 333 rows in the data.
+      i 333 will be used.
+
+# count/proportion parameters
+
+    Code
+      boost_tree(mtry = 0.9, trees = 4) %>% set_engine("xgboost") %>% set_mode(
+        "regression") %>% fit(mpg ~ ., data = mtcars)
+    Condition
+      Error in `recalc_param()`:
+      ! The option `counts = TRUE` was used but `colsample_bynode` was given as 0.9.
+      i Please use a value >= 1 or use `counts = FALSE`.
+
 # interface to param arguments
 
     ! Please supply elements of the `params` list argument as main arguments to `set_engine()` rather than as part of `params`.

tests/testthat/_snaps/convert_data.md

@@ -1,3 +1,55 @@
+# numeric y and mixed x, fail missing data
+
+    Code
+      .convert_form_to_xy_fit(rate ~ ., data = Puromycin_miss, na.action = na.fail,
+      indicators = "traditional", remove_intercept = TRUE)
+    Condition
+      Error in `na.fail.default()`:
+      ! missing values in object
+
+# numeric x and factor y
+
+    Code
+      expected <- glm(class ~ ., data = hpc, x = TRUE, y = TRUE, family = binomial())
+    Condition
+      Warning:
+      glm.fit: fitted probabilities numerically 0 or 1 occurred
+
+# bad args
+
+    Code
+      .convert_form_to_xy_fit(mpg ~ ., data = mtcars, composition = "tibble",
+      indicators = "traditional", remove_intercept = TRUE)
+    Condition
+      Error in `.convert_form_to_xy_fit()`:
+      ! `composition` should be either "data.frame", "matrix", or "dgCMatrix".
+
+---
+
+    Code
+      .convert_form_to_xy_fit(mpg ~ ., data = mtcars, weights = letters[1:nrow(mtcars)],
+      indicators = "traditional", remove_intercept = TRUE)
+    Condition
+      Error in `.convert_form_to_xy_fit()`:
+      ! `weights` must be a numeric vector.
+
+---
+
+    Code
+      .convert_xy_to_form_fit(mtcars$disp, mtcars$mpg, remove_intercept = TRUE)
+    Condition
+      Error in `.convert_xy_to_form_fit()`:
+      ! `x` cannot be a vector.
+
+---
+
+    Code
+      .convert_xy_to_form_fit(mtcars[, 1:3], mtcars[, 2:5], remove_intercept = TRUE)
+    Condition
+      Error in `.convert_xy_to_form_fit()`:
+      ! `x` and `y` have the names "cyl" and "disp" in common.
+      i Please ensure that `x` and `y` don't share any column names.
+
 # convert to matrix
 
     Code

tests/testthat/_snaps/descriptors.md

@@ -0,0 +1,8 @@
+# can be temporarily overriden at evaluation time
+
+    Code
+      .cols()
+    Condition
+      Error in `descr_env$.cols()`:
+      ! Descriptor context not set
+

tests/testthat/_snaps/extract.md

@@ -38,6 +38,14 @@
       i The parsnip extension package baguette implements support for this specification.
       i Please install (if needed) and load to continue.
 
+# extract single parameter from model with no parameters
+
+    Code
+      extract_parameter_dials(lm_model, parameter = "none there")
+    Condition
+      Error in `extract_parameter_dials()`:
+      ! No parameter exists with id "none there".
+
 # extract_fit_time() works
 
     Code

tests/testthat/_snaps/failed_models.md

@@ -0,0 +1,51 @@
+# numeric model
+
+    Code
+      num_res <- predict(lm_mod, hpc_bad[1:11, -1])
+    Condition
+      Warning:
+      Model fit failed; cannot make predictions.
+
+---
+
+    Code
+      ci_res <- predict(lm_mod, hpc_bad[1:11, -1], type = "conf_int")
+    Condition
+      Warning:
+      Model fit failed; cannot make predictions.
+
+---
+
+    Code
+      pi_res <- predict(lm_mod, hpc_bad[1:11, -1], type = "pred_int")
+    Condition
+      Warning:
+      Model fit failed; cannot make predictions.
+
+# classification model
+
+    Code
+      cls_res <- predict(log_reg, lending_club %>% dplyr::slice(1:7) %>% dplyr::select(
+        -Class))
+    Condition
+      Warning:
+      Model fit failed; cannot make predictions.
+
+---
+
+    Code
+      prb_res <- predict(log_reg, lending_club %>% dplyr::slice(1:7) %>% dplyr::select(
+        -Class), type = "prob")
+    Condition
+      Warning:
+      Model fit failed; cannot make predictions.
+
+---
+
+    Code
+      ci_res <- predict(log_reg, lending_club %>% dplyr::slice(1:7) %>% dplyr::select(
+        -Class), type = "conf_int")
+    Condition
+      Warning:
+      Model fit failed; cannot make predictions.
+

tests/testthat/_snaps/fit_interfaces.md

@@ -1,3 +1,43 @@
+# wrong args
+
+    Code
+      tester_xy(NULL, x = sprk, y = hpc, model = rmod)
+    Condition
+      Error in `tester_xy()`:
+      ! `x` should be a <data.frame/matrix>, not an <integer> object.
+
+---
+
+    Code
+      tester(NULL, f, data = as.matrix(hpc[, 1:4]))
+    Condition
+      Error in `tester()`:
+      ! `data` should be a <data.frame/dgCMatrix/tbl_spark>, not a double matrix.
+
+# unknown modes
+
+    Code
+      fit(mars_spec, am ~ ., data = mtcars)
+    Condition
+      Error in `fit()`:
+      ! Please set the mode in the model specification.
+
+---
+
+    Code
+      fit_xy(mars_spec, x = mtcars[, -1], y = mtcars[, 1])
+    Condition
+      Error in `fit_xy()`:
+      ! Please set the mode in the model specification.
+
+---
+
+    Code
+      fit_xy(mars_spec, x = lending_club[, 1:2], y = lending_club$Class)
+    Condition
+      Error in `fit_xy()`:
+      ! Please set the mode in the model specification.
+
 # misspecified formula argument
 
     Code

tests/testthat/_snaps/gen_additive_model.md

@@ -0,0 +1,19 @@
+# regression
+
+    Code
+      xy_res <- fit_xy(reg_mod, x = mtcars[, 1:5], y = mtcars$mpg, control = ctrl)
+    Condition
+      Error in `fit_xy()`:
+      ! Please use `fit()` rather than `fit_xy()` to train generalized additive models with the "mgcv" engine.
+      i See `?model_formula()` to learn more.
+
+# classification
+
+    Code
+      xy_res <- fit_xy(cls_mod, x = two_class_dat[, 2:3], y = two_class_dat$Class,
+      control = ctrl)
+    Condition
+      Error in `fit_xy()`:
+      ! Please use `fit()` rather than `fit_xy()` to train generalized additive models with the "mgcv" engine.
+      i See `?model_formula()` to learn more.
+