Consistent behavior for fit classification when the response isn't a factor #115
Description
For whatever reason you might accidentally specify your response as something other then a factor. This gives rather informative error messages.
library(tidymodels)
#> ── Attaching packages ────────────────────────────────────────────────────────────────────── tidymodels 0.0.2 ──
#> ✔ broom 0.5.1 ✔ purrr 0.2.5
#> ✔ dials 0.0.2 ✔ recipes 0.1.4
#> ✔ dplyr 0.7.8 ✔ rsample 0.0.3
#> ✔ ggplot2 3.1.0 ✔ tibble 1.4.2
#> ✔ infer 0.4.0 ✔ yardstick 0.0.2
#> ✔ parsnip 0.0.1
#> ── Conflicts ───────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ rsample::fill() masks tidyr::fill()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ recipes::step() masks stats::step()
### factor
model_logistic <- logistic_reg(mode = "classification") %>%
set_engine("glm") %>%
fit(Species ~ ., data = iris)
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
predict_class(model_logistic, iris)
#> [1] setosa setosa setosa setosa setosa setosa
#> [7] setosa setosa setosa setosa setosa setosa
#> [13] setosa setosa setosa setosa setosa setosa
#> [19] setosa setosa setosa setosa setosa setosa
#> [25] setosa setosa setosa setosa setosa setosa
#> [31] setosa setosa setosa setosa setosa setosa
#> [37] setosa setosa setosa setosa setosa setosa
#> [43] setosa setosa setosa setosa setosa setosa
#> [49] setosa setosa versicolor versicolor versicolor versicolor
#> [55] versicolor versicolor versicolor versicolor versicolor versicolor
#> [61] versicolor versicolor versicolor versicolor versicolor versicolor
#> [67] versicolor versicolor versicolor versicolor versicolor versicolor
#> [73] versicolor versicolor versicolor versicolor versicolor versicolor
#> [79] versicolor versicolor versicolor versicolor versicolor versicolor
#> [85] versicolor versicolor versicolor versicolor versicolor versicolor
#> [91] versicolor versicolor versicolor versicolor versicolor versicolor
#> [97] versicolor versicolor versicolor versicolor versicolor versicolor
#> [103] versicolor versicolor versicolor versicolor versicolor versicolor
#> [109] versicolor versicolor versicolor versicolor versicolor versicolor
#> [115] versicolor versicolor versicolor versicolor versicolor versicolor
#> [121] versicolor versicolor versicolor versicolor versicolor versicolor
#> [127] versicolor versicolor versicolor versicolor versicolor versicolor
#> [133] versicolor versicolor versicolor versicolor versicolor versicolor
#> [139] versicolor versicolor versicolor versicolor versicolor versicolor
#> [145] versicolor versicolor versicolor versicolor versicolor versicolor
#> Levels: setosa versicolor virginica
### logical
model_logistic <- logistic_reg(mode = "classification") %>%
set_engine("glm") %>%
fit(Species ~ ., data = iris %>% mutate(Species = Species == "setosa"))
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
predict_class(model_logistic, iris)
#> Warning in rep(yes, length.out = length(ans)): 'x' is NULL so the result
#> will be NULL
#> Error in ans[test & ok] <- rep(yes, length.out = length(ans))[test & ok]: replacement has length zero
### numeric
model_logistic <- logistic_reg(mode = "classification") %>%
set_engine("glm") %>%
fit(Species ~ ., data = iris %>% mutate(Species = as.numeric(Species)))
#> Error in eval(family$initialize): y values must be 0 <= y <= 1
### character
model_logistic <- logistic_reg(mode = "classification") %>%
set_engine("glm") %>%
fit(Species ~ ., data = iris %>% mutate(Species = as.character(Species)))
#> Error in eval(family$initialize): y values must be 0 <= y <= 1Created on 2018-12-13 by the reprex package (v0.2.1)
When response is logical we manage to fit, but predict_class fails. And when the response is numeric and character it fails to fit in both.
I propose we check inside fit.model_spec that the response is of type factor when doing classification.