For glmnet models, fail if no penalty value is provided.

[topepo]

library(tidymodels)
data("two_class_dat")

# These should both fail in translate()

logistic_reg(penalty = c(0.01, 0.1)) %>% 
  set_engine("glmnet") %>% 
  fit(Class ~ ., data = two_class_dat)
#> parsnip model object
#> 
#> Fit time:  12ms 
#> 
#> Call:  glmnet::glmnet(x = maybe_matrix(x), y = y, family = "binomial") 
#> 
#>    Df  %Dev   Lambda
#> 1   0  0.00 0.308100
#> 2   1  4.74 0.280700
#> 3   1  8.72 0.255800
#> 4   1 12.08 0.233100
#> 5   1 14.96 0.212400
#> 6   1 17.44 0.193500
#> 7   1 19.57 0.176300
#> 8   1 21.42 0.160600
#> 9   1 23.02 0.146400
#> 10  1 24.41 0.133400
#> 11  1 25.62 0.121500
#> 12  1 26.67 0.110700
#> 13  1 27.58 0.100900
#> 14  1 28.37 0.091930
#> 15  1 29.05 0.083760
#> 16  1 29.64 0.076320
#> 17  1 30.15 0.069540
#> 18  1 30.59 0.063360
#> 19  1 30.97 0.057730
#> 20  1 31.30 0.052610
#> 21  1 31.58 0.047930
#> 22  1 31.82 0.043670
#> 23  1 32.02 0.039790
#> 24  2 32.48 0.036260
#> 25  2 33.27 0.033040
#> 26  2 33.95 0.030100
#> 27  2 34.54 0.027430
#> 28  2 35.05 0.024990
#> 29  2 35.48 0.022770
#> 30  2 35.86 0.020750
#> 31  2 36.18 0.018910
#> 32  2 36.46 0.017230
#> 33  2 36.70 0.015700
#> 34  2 36.90 0.014300
#> 35  2 37.08 0.013030
#> 36  2 37.23 0.011870
#> 37  2 37.35 0.010820
#> 38  2 37.46 0.009857
#> 39  2 37.55 0.008982
#> 40  2 37.63 0.008184
#> 41  2 37.70 0.007457
#> 42  2 37.75 0.006794
#> 43  2 37.80 0.006191
#> 44  2 37.84 0.005641
#> 45  2 37.87 0.005140
#> 46  2 37.90 0.004683
#> 47  2 37.92 0.004267
#> 48  2 37.94 0.003888
#> 49  2 37.96 0.003543
#> 50  2 37.98 0.003228
#> 51  2 37.99 0.002941
#> 52  2 38.00 0.002680
#> 53  2 38.01 0.002442
#> 54  2 38.01 0.002225
#> 55  2 38.02 0.002027
#> 56  2 38.02 0.001847
#> 57  2 38.03 0.001683
#> 58  2 38.03 0.001533
#> 59  2 38.03 0.001397
#> 60  2 38.03 0.001273
#> 61  2 38.04 0.001160
#> 62  2 38.04 0.001057
#> 63  2 38.04 0.000963
#> 64  2 38.04 0.000878
#> 65  2 38.04 0.000800

logistic_reg() %>% 
  set_engine("glmnet") %>% 
  fit(Class ~ ., data = two_class_dat)
#> parsnip model object
#> 
#> Fit time:  9ms 
#> 
#> Call:  glmnet::glmnet(x = maybe_matrix(x), y = y, family = "binomial") 
#> 
#>    Df  %Dev   Lambda
#> 1   0  0.00 0.308100
#> 2   1  4.74 0.280700
#> 3   1  8.72 0.255800
#> 4   1 12.08 0.233100
#> 5   1 14.96 0.212400
#> 6   1 17.44 0.193500
#> 7   1 19.57 0.176300
#> 8   1 21.42 0.160600
#> 9   1 23.02 0.146400
#> 10  1 24.41 0.133400
#> 11  1 25.62 0.121500
#> 12  1 26.67 0.110700
#> 13  1 27.58 0.100900
#> 14  1 28.37 0.091930
#> 15  1 29.05 0.083760
#> 16  1 29.64 0.076320
#> 17  1 30.15 0.069540
#> 18  1 30.59 0.063360
#> 19  1 30.97 0.057730
#> 20  1 31.30 0.052610
#> 21  1 31.58 0.047930
#> 22  1 31.82 0.043670
#> 23  1 32.02 0.039790
#> 24  2 32.48 0.036260
#> 25  2 33.27 0.033040
#> 26  2 33.95 0.030100
#> 27  2 34.54 0.027430
#> 28  2 35.05 0.024990
#> 29  2 35.48 0.022770
#> 30  2 35.86 0.020750
#> 31  2 36.18 0.018910
#> 32  2 36.46 0.017230
#> 33  2 36.70 0.015700
#> 34  2 36.90 0.014300
#> 35  2 37.08 0.013030
#> 36  2 37.23 0.011870
#> 37  2 37.35 0.010820
#> 38  2 37.46 0.009857
#> 39  2 37.55 0.008982
#> 40  2 37.63 0.008184
#> 41  2 37.70 0.007457
#> 42  2 37.75 0.006794
#> 43  2 37.80 0.006191
#> 44  2 37.84 0.005641
#> 45  2 37.87 0.005140
#> 46  2 37.90 0.004683
#> 47  2 37.92 0.004267
#> 48  2 37.94 0.003888
#> 49  2 37.96 0.003543
#> 50  2 37.98 0.003228
#> 51  2 37.99 0.002941
#> 52  2 38.00 0.002680
#> 53  2 38.01 0.002442
#> 54  2 38.01 0.002225
#> 55  2 38.02 0.002027
#> 56  2 38.02 0.001847
#> 57  2 38.03 0.001683
#> 58  2 38.03 0.001533
#> 59  2 38.03 0.001397
#> 60  2 38.03 0.001273
#> 61  2 38.04 0.001160
#> 62  2 38.04 0.001057
#> 63  2 38.04 0.000963
#> 64  2 38.04 0.000878
#> 65  2 38.04 0.000800

^{Created on 2021-05-04 by the reprex package (v1.0.0.9000)}

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