snapshot updates

Author: topepo

tests/testthat/_snaps/adds.md

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

tests/testthat/_snaps/boost_tree.md

@@ -1,3 +1,43 @@
+# updating
+
+    Code
+      update(set_engine(boost_tree(trees = 1), "C5.0", noGlobalPruning = TRUE),
+      trees = tune(), noGlobalPruning = tune())
+    Output
+      Boosted Tree Model Specification (unknown mode)
+      
+      Main Arguments:
+        trees = tune()
+      
+      Engine-Specific Arguments:
+        noGlobalPruning = tune()
+      
+      Computational engine: C5.0 
+      
+
+# bad input
+
+    Code
+      boost_tree(mode = "bogus")
+    Condition
+      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)
+
 # check_args() works
 
     Code

tests/testthat/_snaps/boost_tree_C5.0.md

@@ -0,0 +1,36 @@
+# C5.0 execution
+
+    Code
+      res <- fit(lc_basic, funded_amnt ~ term, data = lending_club, engine = "C5.0",
+      control = ctrl)
+    Condition
+      Error in `.convert_form_to_xy_fit()`:
+      ! The argument `engine` cannot be used to create the data.
+      Possible arguments are subset or weights.
+
+# submodel prediction
+
+    Code
+      multi_predict(class_fit, newdata = wa_churn[1:4, vars], trees = 4, type = "prob")
+    Condition
+      Error in `multi_predict()`:
+      ! Please use `new_data` instead of `newdata`.
+
+# argument checks for data dimensions
+
+    Code
+      f_fit <- fit(spec, 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 <- fit_xy(spec, 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

@@ -0,0 +1,102 @@
+# xgboost execution, classification
+
+    Code
+      res <- parsnip::fit(hpc_xgboost, class ~ novar, data = hpc, control = ctrl)
+    Condition
+      Error:
+      ! object 'novar' not found
+
+# submodel prediction
+
+    Code
+      multi_predict(class_fit, newdata = wa_churn[1:4, vars], trees = 5, type = "prob")
+    Condition
+      Error in `multi_predict()`:
+      ! Please use `new_data` instead of `newdata`.
+
+# validation sets
+
+    Code
+      reg_fit <- fit(set_engine(boost_tree(trees = 20, mode = "regression"),
+      "xgboost", validation = 3), mpg ~ ., data = mtcars[-(1:4), ])
+    Condition
+      Error in `parsnip::xgb_train()`:
+      ! `validation` should be on [0, 1).
+
+# early stopping
+
+    Code
+      reg_fit <- fit(set_engine(boost_tree(trees = 20, stop_iter = 30, mode = "regression"),
+      "xgboost", validation = 0.1), mpg ~ ., data = mtcars[-(1:4), ])
+    Condition
+      Warning:
+      `early_stop` was reduced to 19.
+
+---
+
+    Code
+      reg_fit <- fit(set_engine(boost_tree(trees = 20, stop_iter = 0, mode = "regression"),
+      "xgboost", validation = 0.1), mpg ~ ., data = mtcars[-(1:4), ])
+    Condition
+      Error in `parsnip::xgb_train()`:
+      ! `early_stop` should be on [2, 20).
+
+# xgboost data conversion
+
+    Code
+      from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars_y, event_level = "second")
+    Condition
+      Warning:
+      `event_level` can only be set for binary outcomes.
+
+# argument checks for data dimensions
+
+    Code
+      f_fit <- fit(spec, 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 <- fit_xy(spec, 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
+      fit(set_mode(set_engine(boost_tree(mtry = 0.9, trees = 4), "xgboost"),
+      "regression"), mpg ~ ., data = mtcars)
+    Condition
+      Error in `xgb_train()`:
+      ! 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`.
+    i See `?details_boost_tree_xgboost` for more information.
+
+---
+
+    ! Please supply elements of the `params` list argument as main arguments to `set_engine()` rather than as part of `params`.
+    i See `?details_boost_tree_xgboost` for more information.
+
+---
+
+    ! The argument `watchlist` is guarded by parsnip and will not be passed to `xgb.train()`.
+
+---
+
+    ! The arguments `watchlist` and `data` are guarded by parsnip and will not be passed to `xgb.train()`.
+
+---
+
+    ! Please supply elements of the `params` list argument as main arguments to `set_engine()` rather than as part of `params`.
+    i See `?details_boost_tree_xgboost` for more information.
+

tests/testthat/_snaps/convert_data.md

@@ -0,0 +1,68 @@
+# 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:
+      ! `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:
+      ! `weights` must be a numeric vector.
+
+---
+
+    Code
+      .convert_xy_to_form_fit(mtcars$disp, mtcars$mpg, remove_intercept = TRUE)
+    Condition
+      Error:
+      ! `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
+      parsnip::maybe_matrix(ames[, c("Year_Built", "Neighborhood")])
+    Condition
+      Error in `parsnip::maybe_matrix()`:
+      ! The column "Neighborhood" is non-numeric, so the data cannot be converted to a numeric matrix.
+
+---
+
+    Code
+      parsnip::maybe_matrix(Chicago[, c("ridership", "date")])
+    Condition
+      Error in `parsnip::maybe_matrix()`:
+      ! The column "date" is non-numeric, so the data cannot be converted to a numeric matrix.
+

tests/testthat/_snaps/decision_tree.md

@@ -0,0 +1,54 @@
+# updating
+
+    Code
+      update(set_engine(decision_tree(cost_complexity = 0.1), "rpart", model = FALSE),
+      cost_complexity = tune(), model = tune())
+    Output
+      Decision Tree Model Specification (unknown mode)
+      
+      Main Arguments:
+        cost_complexity = tune()
+      
+      Engine-Specific Arguments:
+        model = tune()
+      
+      Computational engine: rpart 
+      
+
+# bad input
+
+    "bogus" is not a known mode for model `decision_tree()`.
+
+---
+
+    Please set the mode in the model specification (`?parsnip::model_spec()`).
+
+---
+
+    Please set the mode in the model specification (`?parsnip::model_spec()`).
+
+---
+
+    Code
+      try(translate(decision_tree(), engine = NULL), silent = TRUE)
+    Message
+      Used `engine = 'rpart'` for translation.
+
+# argument checks for data dimensions
+
+    Code
+      f_fit <- fit(spec, body_mass_g ~ ., data = penguins)
+    Condition
+      Warning:
+      ! 1000 samples were requested but there were 333 rows in the data.
+      i 333 samples will be used.
+
+---
+
+    Code
+      xy_fit <- fit_xy(spec, x = penguins[, -6], y = penguins$body_mass_g)
+    Condition
+      Warning:
+      ! 1000 samples were requested but there were 333 rows in the data.
+      i 333 samples will be used.
+

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

@@ -0,0 +1,56 @@
+# extract
+
+    Code
+      extract_spec_parsnip(x_no_spec)
+    Condition
+      Error in `extract_spec_parsnip()`:
+      ! The model fit does not have a model spec.
+      i This is an internal error that was detected in the parsnip package.
+        Please report it at <https://github.com/tidymodels/parsnip/issues> with a reprex (<https://tidyverse.org/help/>) and the full backtrace.
+
+---
+
+    Code
+      extract_fit_engine(x_no_fit)
+    Condition
+      Error in `extract_fit_engine()`:
+      ! The model fit does not have an engine fit.
+      i This is an internal error that was detected in the parsnip package.
+        Please report it at <https://github.com/tidymodels/parsnip/issues> with a reprex (<https://tidyverse.org/help/>) and the full backtrace.
+
+# extract parameter set from model with no loaded implementation
+
+    Code
+      extract_parameter_set_dials(bt_mod)
+    Condition
+      Error:
+      ! parsnip could not locate an implementation for `bag_tree` regression model specifications.
+      i The parsnip extension package baguette implements support for this specification.
+      i Please install (if needed) and load to continue.
+
+---
+
+    Code
+      extract_parameter_dials(bt_mod, parameter = "min_n")
+    Condition
+      Error:
+      ! parsnip could not locate an implementation for `bag_tree` regression model specifications.
+      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
+      extract_fit_time(lm_fit)
+    Condition
+      Error in `extract_fit_time()`:
+      ! This model was fit before `extract_fit_time()` was added.
+