diff --git a/.travis.yml b/.travis.yml index 0466db939..7fed3b0b6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,9 +10,6 @@ cran: https://cran.rstudio.com warnings_are_errors: false r: - - 3.2 - - 3.3 - - 3.4 - 3.5 - 3.6 - devel @@ -24,34 +21,6 @@ matrix: - r: 3.2 # partykit install failure (libcoin needs >= 3.4.0) - r: 3.4 # mvtnorm requires >= 3.5.0 -r_binary_packages: - - RCurl - - dplyr - - glue - - magrittr - - stringi - - stringr - - munsell - - rlang - - reshape2 - - scales - - tibble - - ggplot2 - - Rcpp - - RcppEigen - - BH - - glmnet - - earth - - sparklyr - - flexsurv - - ranger - - randomforest - - xgboost - - C50 - - reticulate - - scales - - dials - cache: packages: true diff --git a/DESCRIPTION b/DESCRIPTION index 2ff8ed495..d9c46830b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: parsnip -Version: 0.0.3 +Version: 0.0.3.9000 Title: A Common API to Modeling and Analysis Functions Description: A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', etc). Authors@R: c( diff --git a/NEWS.md b/NEWS.md index a505a745f..024f4f38d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,5 @@ +# parsnip 0.0.3.9000 + # parsnip 0.0.3 Unplanned release based on CRAN requirements for Solaris. diff --git a/docs/articles/articles/Classification.html b/docs/articles/articles/Classification.html index 2ec6a523f..2dc96fcc0 100644 --- a/docs/articles/articles/Classification.html +++ b/docs/articles/articles/Classification.html @@ -105,25 +105,24 @@

Classification Example

To demonstrate parsnip for classification models, the credit data will be used.

library(tidymodels)
-#> ── Attaching packages ─────────────────────────────────────── tidymodels 0.0.2 ──
-#> ✔ broom     0.5.1          ✔ purrr     0.3.2     
-#> ✔ dials     0.0.2.9000     ✔ recipes   0.1.6     
-#> ✔ dplyr     0.8.3          ✔ rsample   0.0.5     
-#> ✔ infer     0.4.0          ✔ yardstick 0.0.3.9000
-#> ── Conflicts ────────────────────────────────────────── tidymodels_conflicts() ──
+#> ── Attaching packages ───────────────────────────────────────────── tidymodels 0.0.2 ──
+#> ✔ broom     0.5.2       ✔ purrr     0.3.2  
+#> ✔ dials     0.0.2       ✔ recipes   0.1.6  
+#> ✔ dplyr     0.8.3       ✔ rsample   0.0.5  
+#> ✔ infer     0.4.0.1     ✔ yardstick 0.0.3
+#> ── Conflicts ──────────────────────────────────────────────── tidymodels_conflicts() ──
 #> ✖ purrr::discard() masks scales::discard()
 #> ✖ dplyr::filter()  masks stats::filter()
 #> ✖ dplyr::lag()     masks stats::lag()
-#> ✖ dials::margin()  masks ggplot2::margin()
-#> ✖ recipes::step()  masks stats::step()
-
-data(credit_data)
-
-set.seed(7075)
-data_split <- initial_split(credit_data, strata = "Status", p = 0.75)
-
-credit_train <- training(data_split)
-credit_test  <- testing(data_split)
+#> ✖ recipes::step() masks stats::step() + +data(credit_data) + +set.seed(7075) +data_split <- initial_split(credit_data, strata = "Status", p = 0.75) + +credit_train <- training(data_split) +credit_test <- testing(data_split)

A single hidden layer neural network will be used to predict a person’s credit status. To do so, the columns of the predictor matrix should be numeric and on a common scale. recipes will be used to do so.

credit_rec <- 
   recipe(Status ~ ., data = credit_train) %>%
@@ -185,12 +184,12 @@ 
Classification Example

 #> # A tibble: 1 x 3
 #>   .metric  .estimator .estimate
 #>   <chr>    <chr>          <dbl>
-#> 1 accuracy binary         0.804
+#> 1 accuracy binary         0.803
 test_results %>% conf_mat(truth = Status, nnet_class)
 #>           Truth
 #> Prediction bad good
-#>       bad  191   96
-#>       good 122  704
+#> bad 183 89 +#> good 130 711 @@ -99,6 +100,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -115,6 +119,7 @@ +
    @@ -130,6 +135,7 @@

    Page not found (404)

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -141,11 +147,14 @@

    Page not found (404)

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/articles/articles/Classification.html b/docs/dev/articles/articles/Classification.html index 9f5ef1ac1..65addb71d 100644 --- a/docs/dev/articles/articles/Classification.html +++ b/docs/dev/articles/articles/Classification.html @@ -41,7 +41,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -69,6 +69,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -87,6 +90,7 @@ +
    @@ -102,27 +106,24 @@

    Classification Example

    To demonstrate parsnip for classification models, the credit data will be used.

    library(tidymodels)
-#> Registered S3 method overwritten by 'xts':
-#>   method     from
-#>   as.zoo.xts zoo
-#> ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────── tidymodels 0.0.2 ──
-#> ✔ broom     0.5.1       ✔ purrr     0.3.2  
-#> ✔ dials     0.0.2       ✔ recipes   0.1.5  
-#> ✔ dplyr     0.8.0.1     ✔ rsample   0.0.4  
-#> ✔ infer     0.4.0       ✔ yardstick 0.0.2
-#> ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
-#> ✖ purrr::discard() masks scales::discard()
-#> ✖ dplyr::filter()  masks stats::filter()
-#> ✖ dplyr::lag()     masks stats::lag()
-#> ✖ recipes::step()  masks stats::step()
-
-data(credit_data)
+#> ── Attaching packages ───────────────────────────────────────────── tidymodels 0.0.2 ──
+#> ✔ broom     0.5.2       ✔ purrr     0.3.2  
+#> ✔ dials     0.0.2       ✔ recipes   0.1.6  
+#> ✔ dplyr     0.8.3       ✔ rsample   0.0.5  
+#> ✔ infer     0.4.0.1     ✔ yardstick 0.0.3
+#> ── Conflicts ──────────────────────────────────────────────── tidymodels_conflicts() ──
+#> ✖ purrr::discard() masks scales::discard()
+#> ✖ dplyr::filter()  masks stats::filter()
+#> ✖ dplyr::lag()     masks stats::lag()
+#> ✖ recipes::step()  masks stats::step()
+
+data(credit_data)
+
+set.seed(7075)
+data_split <- initial_split(credit_data, strata = "Status", p = 0.75)
 
-set.seed(7075)
-data_split <- initial_split(credit_data, strata = "Status", p = 0.75)
-
-credit_train <- training(data_split)
-credit_test  <- testing(data_split)
    +credit_train <- training(data_split) +credit_test <- testing(data_split)

    A single hidden layer neural network will be used to predict a person’s credit status. To do so, the columns of the predictor matrix should be numeric and on a common scale. recipes will be used to do so.

    +#> bad 188 95 +#> good 125 705
    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -208,10 +210,12 @@

    Classification Example

    Site built by pkgdown.

    +
    + diff --git a/docs/dev/articles/articles/Models.html b/docs/dev/articles/articles/Models.html index c6995e6cc..dcec9dcdb 100644 --- a/docs/dev/articles/articles/Models.html +++ b/docs/dev/articles/articles/Models.html @@ -41,7 +41,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -69,6 +69,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -87,6 +90,7 @@ +
    @@ -103,7 +107,7 @@

    List of Models

    parsnip contains wrappers for a number of models. For example, the parsnip function rand_forest() can be used to create a random forest model. The mode of a model is related to its goal. Examples would be regression and classification.

    The list of models accessible via parsnip is:

    classification: boost_tree(), decision_tree(), logistic_reg(), mars(), mlp(), multinom_reg(), nearest_neighbor(), null_model(), rand_forest(), svm_poly(), svm_rbf()

    -

    regression: boost_tree(), decision_tree(), linear_reg(), logistic_reg(), mars(), mlp(), multinom_reg(), nearest_neighbor(), null_model(), rand_forest(), surv_reg(), svm_poly(), svm_rbf()

    +

    regression: boost_tree(), decision_tree(), linear_reg(), mars(), mlp(), nearest_neighbor(), null_model(), rand_forest(), surv_reg(), svm_poly(), svm_rbf()

    How the model is created is related to the engine. In many cases, this is an R modeling package. In others, it may be a connection to an external system (such as Spark or Tensorflow). This table lists the engines for each model type along with the type of prediction that it can make (see predict.model_fit()).

    @@ -1509,6 +1513,7 @@

    List of Models

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -1519,10 +1524,12 @@

    List of Models

    Site built by pkgdown.

    +
    + diff --git a/docs/dev/articles/articles/Regression.html b/docs/dev/articles/articles/Regression.html index 36381fc35..43dcb4090 100644 --- a/docs/dev/articles/articles/Regression.html +++ b/docs/dev/articles/articles/Regression.html @@ -41,7 +41,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -69,6 +69,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -87,6 +90,7 @@ +
    @@ -105,28 +109,25 @@

    Regression Example

    ames <- make_ames() library(tidymodels) -#> Registered S3 method overwritten by 'xts': -#> method from -#> as.zoo.xts zoo -#> ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────── tidymodels 0.0.2 ── -#> ✔ broom 0.5.1 ✔ recipes 0.1.5 -#> ✔ dials 0.0.2 ✔ rsample 0.0.4 -#> ✔ dplyr 0.8.0.1 ✔ tibble 2.1.1 -#> ✔ infer 0.4.0 ✔ yardstick 0.0.2 -#> ✔ purrr 0.3.2 -#> ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ── -#> ✖ dplyr::combine() masks randomForest::combine() -#> ✖ purrr::discard() masks scales::discard() -#> ✖ dplyr::filter() masks stats::filter() -#> ✖ dplyr::lag() masks stats::lag() -#> ✖ dials::margin() masks ggplot2::margin(), randomForest::margin() -#> ✖ recipes::step() masks stats::step() +#> ── Attaching packages ───────────────────────────────────────────── tidymodels 0.0.2 ── +#> ✔ broom 0.5.2 ✔ recipes 0.1.6 +#> ✔ dials 0.0.2 ✔ rsample 0.0.5 +#> ✔ dplyr 0.8.3 ✔ tibble 2.1.3 +#> ✔ infer 0.4.0.1 ✔ yardstick 0.0.3 +#> ✔ purrr 0.3.2 +#> ── Conflicts ──────────────────────────────────────────────── tidymodels_conflicts() ── +#> ✖ dplyr::combine() masks randomForest::combine() +#> ✖ purrr::discard() masks scales::discard() +#> ✖ dplyr::filter() masks stats::filter() +#> ✖ dplyr::lag() masks stats::lag() +#> ✖ dials::margin() masks ggplot2::margin(), randomForest::margin() +#> ✖ recipes::step() masks stats::step() + +set.seed(4595) +data_split <- initial_split(ames, strata = "Sale_Price", p = 0.75) -set.seed(4595) -data_split <- initial_split(ames, strata = "Sale_Price", p = 0.75) - -ames_train <- training(data_split) -ames_test <- testing(data_split)
    +ames_train <- training(data_split) +ames_test <- testing(data_split)
    +#> OOB prediction error (MSE): 0.00843 +#> R squared (OOB): 0.736

    The non-formula interface doesn’t do anything to the predictors before giving it to the underlying model function. This particular model does not require indicator variables to be create prior to the model (note that the output shows “Number of independent variables: 5”).

    For regression models, the basic predict method can be used and returns a tibble with a column named .pred:

    +#> 1 rmse standard 0.0916 +#> 2 rsq standard 0.715 +#> 3 mae standard 0.0663

    Note that:

  • @@ -87,6 +90,7 @@ +
    @@ -129,7 +133,7 @@

    The General Process

    -

    parsnip stores information about the models in an internal environment object. The environment can be accessed via the function get_model_env(). The package includes a variety of functions that can get or set the different aspects of the models.

    +

    parsnip stores information about the models in an internal environment object. The environment can be accessed via the function get_model_env(). The package includes a variety of functions that can get or set the different aspects of the models.

    If you are adding a new model form your own package, you can use these functions to add new entries into the model environment.

    @@ -137,15 +141,15 @@

    Step 1. Register the Model, Modes, and Arguments.

    We will add the MDA model using the model type mixture_da. Since this is a classification method, we only have to register a single mode:

    library(parsnip)
-set_new_model("mixture_da")
-set_model_mode(model = "mixture_da", mode = "classification")
-set_model_engine(
+set_new_model("mixture_da")
+set_model_mode(model = "mixture_da", mode = "classification")
+set_model_engine(
   "mixture_da", 
   mode = "classification", 
   eng = "mda"
 )

    These functions should silently finish. There is also a function that can be used to show what aspects of the model have been added to parsnip:

    -
    show_model_info("mixture_da")
+
    show_model_info("mixture_da")
 #> Information for `mixture_da`
 #>  modes: unknown, classification 
 #> 
@@ -165,7 +169,7 @@ 
    
 
  • A logical value for wether the argument can be used to generate mutiple predictions for a single R object. For example, for boosted trees, if a model is fit with 10 boosting iterations, many modeling packages allow the model object to make predictions for any iterations less than the one used to fit the model. In general this is not the case so one would use has_submodels = FALSE.
    • 
 
 
    For mda::mda(), the main tuning parameter is subclasses which we will rewrite as sub_classes.
    
-
    set_model_arg(
+
    set_model_arg(
   model = "mixture_da",
   eng = "mda",
   parsnip = "sub_classes",
@@ -173,7 +177,7 @@ 
    
   func = list(pkg = "foo", fun = "bar"),
   has_submodel = FALSE
 )
-show_model_info("mixture_da")
+show_model_info("mixture_da")
 #> Information for `mixture_da`
 #>  modes: unknown, classification 
 #> 
@@ -228,7 +232,7 @@ 
    
 
  • defaults is an optional list of arguments to the fit function that the user can change, but whose defaults can be set here. This isn’t needed in this case, but is describe later in this document.
    • 
 
 
    For the first engine:
    
-
    set_fit(
+
 
    A similar call can be used to define the class probability module (if they can be computed). The format is identical to the class module but the output is expected to be a tibble with columns for each factor level.
    
 
    As an example of the post function, the data frame created by mda:::predict.mda will be converted to a tibble. The arguments are x (the raw results coming from the predict method) and object (the parsnip model fit object). The latter has a sub-object called lvl which is a character string of the outcome’s factor levels (if any).
    
-
    We register the probability module:
    
+
    We register the probability module. There is a template function that makes this slightly easier to format the objects:
    
 
+#>  arguments: 
+#>    mda: 
+#>       sub_classes --> subclasses
+#> 
+#>  fit modules:
+#>  engine           mode
+#>     mda classification
+#> 
+#>  prediction modules:
+#>              mode engine     methods
+#>    classification    mda class, prob
    
 
    If this model could be used for regression situations, we could also add a “numeric” module. The convention used here is very similar to the two that are detailed in the next section. For pred, the model requires an unnamed numeric vector output (usually).
    
 
    Examples are here and here.
    
 
    
@@ -419,6 +420,62 @@ 
    
 #> 6 virginica   virginica 
 #> 7 virginica   virginica
    
 
    
+
    
+
    
+Adding an engine to an existing model
    
+
    The process here is almost the same but more simple. You would only need to add the engine-specific aspects of the model. For example, if we wanted to fit a linear regression model using M-estimation, we could only add a new engine. The code for the rlm function in MASS is pretty similar to lm, so we can copy that code and change the package/function names:
    
+
+
    
 
    
 
    
 Pro-tips, what-ifs, exceptions, FAQ, and minutiae
    
@@ -432,23 +489,25 @@ 
    
 
 
    There are some models (e.g. glmnet, plsr, Cubust, etc.) that can make predictions for different models from the same fitted model object. We want to facilitate that here so that, for these cases, the current convention is to return a tibble with the prediction in a column called values and have extra columns for any parameters that define the different sub-models.
    
 
    For example, if I fit a linear regression model via glmnet and get four values of the regularization parameter (lambda):
    
-
    linear_reg(others = list(nlambda = 4)) %>%
-  fit(mpg ~ ., data = mtcars, engine = "glmnet") %>%
-  predict(newdata = mtcars[1:3, -1])
    
+
    linear_reg() %>%
+  set_engine("glmnet", nlambda = 4) %>% 
+  fit(mpg ~ ., data = mtcars) %>%
+  multi_predict(new_data = mtcars[1:3, -1])
    
 
    However, the api is still being developed. Currently, there is not an interface in the prediction functions to pass in the values of the parameters to make predictions with (lambda, in this case).
    
 
    
 
    
 
    
 What is defaults slot and why do I need them?
    
 
    You might want to set defaults that can be overridden by the user. For example, for logistic regression with glm, it make sense to default family = binomial. However, if someone wants to use a different link function, they should be able to do that. For that model/engine definition, it has
    
-
    defaults = list(family = expr(binomial))
    
+
    defaults = list(family = expr(binomial))
    
 
    so that is the default:
    
-
    logistic_reg() %>% translate(engine = "glm")
-
-# but you can change it:
-
-logistic_reg(others = list(family = expr(binomial(link = "probit")))) %>% 
-  translate(engine = "glm")
    
+
    logistic_reg() %>% translate(engine = "glm")
+
+# but you can change it:
+
+logistic_reg() %>%
+  set_engine("glm", family = expr(binomial(link = "probit"))) %>% 
+  translate()
    
 
    That’s what defaults are for.
    
 
    Note that I wrapped binomial inside of expr. If I didn’t, it would substitute the results of executing binomial inside of the expression (and that’s a mess).
    
 
    
@@ -457,20 +516,20 @@ 
    
 What if I want more complex defaults?
    
 
    The translate function can be used to check values or set defaults once the model’s mode is known. To do this, you can create a model-specific S3 method that first calls the general method (translate.model_spec) and then makes modifications or conducts error traps.
    
 
    For example, the ranger and randomForest package functions have arguments for calculating importance. One is a logical and the other is a string. Since this is likely to lead to a bunch of frustration and GH issues, we can put in a check:
    
-
+
 
    As another example, nnet::nnet has an option for the final layer to be linear (called linout). If mode = "regression", that should probably be set to TRUE. You couldn’t do this with the args (described above) since you need the function translated first.
    @@ -512,6 +571,7 @@

  • Step 3. Add a Fit Module
  • Step 3. Add Modules for Prediction
  • Does it Work?
    • +
  • Adding an engine to an existing model
  • Pro-tips, what-ifs, exceptions, FAQ, and minutiae

    • @@ -520,6 +580,7 @@

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -530,10 +591,12 @@

    Site built by pkgdown.

    +
    + diff --git a/docs/dev/articles/articles/Submodels.html b/docs/dev/articles/articles/Submodels.html new file mode 100644 index 000000000..f865944fa --- /dev/null +++ b/docs/dev/articles/articles/Submodels.html @@ -0,0 +1,252 @@ + + + + + + + +Evaluating Submodels with the Same Model Object • parsnip + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    Evaluating Submodels with the Same Model Object

    + + + + + +
    + + + +

    Some R packages can create predictions from models that are different than the one that was fit. For example, if a boosted tree is fit with 10 iterations of boosting, the model can usually make predictions on submodels that have less than 10 trees (all other parameters being static). This is helpful for model tuning since you can cheap evaluate tuning parameter combinations and can often results in a large speed-up in the computations.

    +

    In parsnip, there is a method called multi_predict() that can do this. It’s current methods are:

    +
    library(parsnip)
+methods("multi_predict")
    +
    ## [1] multi_predict._C5.0*        multi_predict._earth*      
+## [3] multi_predict._elnet*       multi_predict._lognet*     
+## [5] multi_predict._multnet*     multi_predict._train.kknn* 
+## [7] multi_predict._xgb.Booster* multi_predict.default*     
+## see '?methods' for accessing help and source code
    +

    We’ll use the attrition data in rsample to illustrate:

    +
    library(tidymodels)
    +
    ## ── Attaching packages ───────────────────────────────────────────── tidymodels 0.0.2 ──
    +
    ## ✔ broom     0.5.2       ✔ recipes   0.1.6  
+## ✔ dials     0.0.2       ✔ rsample   0.0.5  
+## ✔ dplyr     0.8.3       ✔ tibble    2.1.3  
+## ✔ infer     0.4.0.1     ✔ yardstick 0.0.3  
+## ✔ purrr     0.3.2
    +
    ## ── Conflicts ──────────────────────────────────────────────── tidymodels_conflicts() ──
+## ✖ purrr::discard() masks scales::discard()
+## ✖ dplyr::filter()  masks stats::filter()
+## ✖ dplyr::lag()     masks stats::lag()
+## ✖ recipes::step()  masks stats::step()
    + +

    A boosted classification tree is one of the most low-maintenance approaches that we could take to these data:

    +
    # requires the xgboost package
+attrition_boost <- 
+  boost_tree(mode = "classification", trees = 100) %>% 
+  set_engine("C5.0")
    +

    Suppose that 10-fold cross-validation was being used to tune the model over the number of trees:

    + +

    The process would fit a model on 90% of the data and predict on the remaining 10%. Using rsample:

    + +

    For multi_predict(), the same semantics of predict() are used but, for this model, there is an extra argument called trees. Candidate submodel values can be passed in with trees:

    + +
    ## # A tibble: 111 x 1
+##    .pred             
+##    <list>            
+##  1 <tibble [100 × 3]>
+##  2 <tibble [100 × 3]>
+##  3 <tibble [100 × 3]>
+##  4 <tibble [100 × 3]>
+##  5 <tibble [100 × 3]>
+##  6 <tibble [100 × 3]>
+##  7 <tibble [100 × 3]>
+##  8 <tibble [100 × 3]>
+##  9 <tibble [100 × 3]>
+## 10 <tibble [100 × 3]>
+## # … with 101 more rows
    +

    The results is a tibble that has as many rows as the data being predicted (n = 111). The .pred column contains a list of tibbles and each has the predictions across the different number of trees:

    + +
    ## # A tibble: 100 x 3
+##    trees .pred_No .pred_Yes
+##    <int>    <dbl>     <dbl>
+##  1     1    0.691     0.309
+##  2     2    1         0    
+##  3     3    1         0    
+##  4     4    1         0    
+##  5     5    1         0    
+##  6     6    0.862     0.138
+##  7     7    0.882     0.118
+##  8     8    0.770     0.230
+##  9     9    0.689     0.311
+## 10    10    0.724     0.276
+## # … with 90 more rows
    +

    To get this into a format that is more usable, we can use tidyr::unnest() but we first add row numbers so that we can track the predictions by test sample as well as the actual classes:

    + +
    ## # A tibble: 11,100 x 5
+##    Attrition  .row trees .pred_No .pred_Yes
+##    <fct>     <int> <int>    <dbl>     <dbl>
+##  1 Yes           1     1    0.691     0.309
+##  2 Yes           1     2    1         0    
+##  3 Yes           1     3    1         0    
+##  4 Yes           1     4    1         0    
+##  5 Yes           1     5    1         0    
+##  6 Yes           1     6    0.862     0.138
+##  7 Yes           1     7    0.882     0.118
+##  8 Yes           1     8    0.770     0.230
+##  9 Yes           1     9    0.689     0.311
+## 10 Yes           1    10    0.724     0.276
+## # … with 11,090 more rows
    +

    For two samples, what do these look like over trees?

    + +

    +

    What does performance look like over trees (using the area under the ROC curve)?

    + +

    +
    + + + +
    + + + +
    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    +
    + +
    +

    + Developed by Max Kuhn, Davis Vaughan. + Site built by pkgdown. +

    +
    + +
    +
    + + + + + + diff --git a/docs/dev/articles/articles/Submodels_files/figure-html/auc-plot-1.png b/docs/dev/articles/articles/Submodels_files/figure-html/auc-plot-1.png new file mode 100644 index 000000000..6f91a5b27 Binary files /dev/null and b/docs/dev/articles/articles/Submodels_files/figure-html/auc-plot-1.png differ diff --git a/docs/dev/articles/articles/Submodels_files/figure-html/prob-plot-1.png b/docs/dev/articles/articles/Submodels_files/figure-html/prob-plot-1.png new file mode 100644 index 000000000..ac42253d0 Binary files /dev/null and b/docs/dev/articles/articles/Submodels_files/figure-html/prob-plot-1.png differ diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index 30876d606..6f5c790cc 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -52,6 +52,7 @@ gtag('config', 'UA-115082821-1'); + @@ -71,7 +72,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -99,6 +100,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -115,6 +119,7 @@ +
    @@ -132,12 +137,14 @@

    All vignettes

  • List of Models
  • Regression Example
  • Making a parsnip model from scratch
    • +
  • Evaluating Submodels with the Same Model Object
  • parsnip Basics
    • +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -149,11 +156,14 @@

    All vignettes

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/articles/parsnip_Intro.html b/docs/dev/articles/parsnip_Intro.html index 72c0dca0e..571c0a8c1 100644 --- a/docs/dev/articles/parsnip_Intro.html +++ b/docs/dev/articles/parsnip_Intro.html @@ -41,7 +41,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -69,6 +69,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -87,6 +90,7 @@ +
    @@ -190,7 +194,7 @@

    #> Ranger result #> #> Call: -#> ranger::ranger(formula = formula, data = data, mtry = ~4, num.trees = ~2000, seed = ~63233, num.threads = 1, verbose = FALSE) +#> ranger::ranger(formula = formula, data = data, mtry = ~4, num.trees = ~2000, seed = sample.int(10^5, 1), num.threads = 1, verbose = FALSE) #> #> Type: Regression #> Number of trees: 2000 @@ -212,15 +216,15 @@

    #> #> #> Call: -#> randomForest(x = as.data.frame(x), y = y, ntree = ~2000, mtry = ~4, seed = ~63233) +#> randomForest(x = as.data.frame(x), y = y, ntree = ~2000, mtry = ~4) #> Type of random forest: regression #> Number of trees: 2000 #> No. of variables tried at each split: 4 #> #> Mean of squared residuals: 5.52 #> % Var explained: 84.3 -

    Note that the call objects show num.trees = ~2000. The tilde is the consequence of parsnip using quosures to process the model specification’s arguments.

    -

    Normally, when a function is executed, the function’s arguments are immediately evaluated. In the case of parsnip, the model specification’s arguments are not; the expression is captured along with the environment where it should be evaluated. That is what a quosure does.

    +

    Note that the call objects show num.trees = ~2000. The tilde is the consequence of parsnip using quosures to process the model specification’s arguments.

    +

    Normally, when a function is executed, the function’s arguments are immediately evaluated. In the case of parsnip, the model specification’s arguments are not; the expression is captured along with the environment where it should be evaluated. That is what a quosure does.

    parsnip uses these expressions to make a model fit call that is evaluated. The tilde in the call above reflects that the argument was captured using a quosure.

    @@ -231,6 +235,7 @@

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -241,10 +246,12 @@

    Site built by pkgdown.

    +

    + diff --git a/docs/dev/authors.html b/docs/dev/authors.html index 3d0f985b4..554381c26 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -52,6 +52,7 @@ gtag('config', 'UA-115082821-1'); + @@ -71,7 +72,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -99,6 +100,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -115,6 +119,7 @@ +
    @@ -143,6 +148,7 @@

    Authors

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -154,11 +160,14 @@

    Authors

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/index.html b/docs/dev/index.html index 589e8c333..fdc6fb865 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -41,7 +41,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -69,6 +69,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -87,6 +90,7 @@ +
    @@ -170,14 +174,15 @@

    Dev status

    • Build Status
    • Coverage status
      • -
    • CRAN_Status_Badge
      • -
    • Downloads
      • +
    • CRAN_Status_Badge
      • +
    • Downloads
    • lifecycle
    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -188,10 +193,12 @@

    Dev status

    Site built by pkgdown.

    +
    + diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index b03e9b28d..d8c883a28 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -52,6 +52,7 @@ gtag('config', 'UA-115082821-1'); + @@ -71,7 +72,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -99,6 +100,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -115,6 +119,7 @@ +
    @@ -124,17 +129,24 @@

    Changelog

    -
    +

    -parsnip 0.0.2.9000 Unreleased +parsnip 0.0.3.9000 Unreleased

    +
    +
    +

    +parsnip 0.0.3 Unreleased +

    +

    Unplanned release based on CRAN requirements for Solaris.

    Breaking Changes

      -
    • The method that parsnip stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models. The methods are detailed in ?get_model_env() and the package vignette for adding models.
      • -
    • The mode need to be declared for models that can be used for more than one mode prior to fitting and/or translation).
      • -
    • For surv_reg(), the engine that uses the survival package is now called survival instead of survreg.
      • +

    • The method that parsnip stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models. The methods are detailed in ?get_model_env and the package vignette for adding models.

      • +

    • The mode needs to be declared for models that can be used for more than one mode prior to fitting and/or translation.

      • +

    • For surv_reg(), the engine that uses the survival package is now called survival instead of survreg.

      • +

    • For glmnet models, the full regularization path is always fit regardless of the value given to penalty. Previously, the model was fit with passing penalty to glmnet’s lambda argument and the model could only make predictions at those specific values. (#195)

    @@ -143,6 +155,9 @@

    • add_rowindex() can create a column called .row to a data frame.

    • If a computational engine is not explicitly set, a default will be used. Each default is documented on the corresponding model page. A warning is issued at fit time unless verbosity is zero.

      • +

    • nearest_neighbor() gained a multi_predict method. The multi_predict() documentation is a little better organized.

      • +

    • A suite of internal functions were added to help with upcoming model tuning features.

      • +

    • A parsnip object always saved the name(s) of the outcome variable(s) for proper naming of the predicted values.

    @@ -215,7 +230,7 @@

    • -regularization was changed to penalty in a few models to be consistent with this change.
      • +regularization was changed to penalty in a few models to be consistent with this change.
    • If a mode is not chosen in the model specification, it is assigned at the time of fit. 51
    • The underlying modeling packages now are loaded by namespace. There will be some exceptions noted in the documentation for each model. For example, in some predict methods, the earth package will need to be attached to be fully operational.
      • @@ -256,7 +271,8 @@

      Contents

      +

      parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

      @@ -282,11 +299,14 @@

      Contents

      Site built by pkgdown.

      +

    + + diff --git a/docs/dev/pkgdown.css b/docs/dev/pkgdown.css index 8af66efa2..88181704b 100644 --- a/docs/dev/pkgdown.css +++ b/docs/dev/pkgdown.css @@ -125,6 +125,9 @@ a.anchor { .orcid { height: 16px; + /* margins are required by official ORCID trademark and display guidelines */ + margin-left:4px; + margin-right:4px; vertical-align: middle; } diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 0c596657d..7aea716a6 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -1,10 +1,11 @@ pandoc: 2.3.1 pkgdown: 1.3.0.9100 -pkgdown_sha: 2b8150dacfcdad662773c77eb3866901695fa8e0 +pkgdown_sha: 71070c3aa5cbff0bffd5bdc599f0fffbd5065142 articles: Classification: articles/Classification.html Models: articles/Models.html Regression: articles/Regression.html Scratch: articles/Scratch.html + Submodels: articles/Submodels.html parsnip_Intro: parsnip_Intro.html diff --git a/docs/dev/reference/C5.0_train.html b/docs/dev/reference/C5.0_train.html index 1c7561997..bd86897e5 100644 --- a/docs/dev/reference/C5.0_train.html +++ b/docs/dev/reference/C5.0_train.html @@ -57,6 +57,7 @@ gtag('config', 'UA-115082821-1'); + @@ -76,7 +77,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -104,6 +105,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -120,6 +124,7 @@ +
    @@ -202,6 +207,7 @@

    Contents

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -213,11 +219,14 @@

    Contents

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/reference/add_rowindex.html b/docs/dev/reference/add_rowindex.html index 8d73c1d07..ef777b77b 100644 --- a/docs/dev/reference/add_rowindex.html +++ b/docs/dev/reference/add_rowindex.html @@ -55,6 +55,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +75,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +103,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,6 +122,7 @@ +
    @@ -198,6 +203,7 @@

    Contents

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -209,11 +215,14 @@

    Contents

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/reference/boost_tree.html b/docs/dev/reference/boost_tree.html index cf07a7ff1..470dcf8d1 100644 --- a/docs/dev/reference/boost_tree.html +++ b/docs/dev/reference/boost_tree.html @@ -76,6 +76,7 @@ gtag('config', 'UA-115082821-1'); + @@ -95,7 +96,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -123,6 +124,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -139,6 +143,7 @@ +
    @@ -360,6 +365,7 @@

    Contents

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -371,11 +377,14 @@

    Contents

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/reference/check_empty_ellipse.html b/docs/dev/reference/check_empty_ellipse.html index b17472924..86efd12ab 100644 --- a/docs/dev/reference/check_empty_ellipse.html +++ b/docs/dev/reference/check_empty_ellipse.html @@ -55,6 +55,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +75,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +103,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,6 +122,7 @@ +
    @@ -162,6 +167,7 @@

    Contents

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -173,11 +179,14 @@

    Contents

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/reference/check_times.html b/docs/dev/reference/check_times.html index 0b6c131d5..e200846d1 100644 --- a/docs/dev/reference/check_times.html +++ b/docs/dev/reference/check_times.html @@ -59,6 +59,7 @@ gtag('config', 'UA-115082821-1'); + @@ -78,7 +79,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -106,6 +107,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -122,6 +126,7 @@ +
    @@ -239,6 +244,7 @@

    Contents

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -250,11 +256,14 @@

    Contents

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/reference/decision_tree.html b/docs/dev/reference/decision_tree.html index 22825643e..d655f071e 100644 --- a/docs/dev/reference/decision_tree.html +++ b/docs/dev/reference/decision_tree.html @@ -70,6 +70,7 @@ gtag('config', 'UA-115082821-1'); + @@ -89,7 +90,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -117,6 +118,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -133,6 +137,7 @@ +
    @@ -259,12 +264,12 @@

    spark classification

     sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(), 
-    seed = sample.int(10^5, 1), type = "classification")
+    seed = sample.int(10^5, 1))

    spark regression

     sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(), 
-    seed = sample.int(10^5, 1), type = "regression")
+    seed = sample.int(10^5, 1))

    See also

    @@ -318,6 +323,7 @@

    Contents

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -329,11 +335,14 @@

    Contents

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/reference/descriptors.html b/docs/dev/reference/descriptors.html index 22694b7e7..4228f9d92 100644 --- a/docs/dev/reference/descriptors.html +++ b/docs/dev/reference/descriptors.html @@ -59,6 +59,7 @@ gtag('config', 'UA-115082821-1'); + @@ -78,7 +79,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -106,6 +107,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -122,6 +126,7 @@ +
    @@ -221,6 +226,7 @@

    Contents

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -232,11 +238,14 @@

    Contents

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/reference/fit.html b/docs/dev/reference/fit.html index 8bd1bbf94..6070ac901 100644 --- a/docs/dev/reference/fit.html +++ b/docs/dev/reference/fit.html @@ -57,6 +57,7 @@ gtag('config', 'UA-115082821-1'); + @@ -76,7 +77,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -104,6 +105,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -120,6 +124,7 @@ +
    @@ -297,6 +302,7 @@

    Contents

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -308,11 +314,14 @@

    Contents

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/reference/fit_control.html b/docs/dev/reference/fit_control.html index d93ee2181..30bdcd79d 100644 --- a/docs/dev/reference/fit_control.html +++ b/docs/dev/reference/fit_control.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -178,6 +183,7 @@

    Contents

    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    @@ -189,11 +195,14 @@

    Contents

    Site built by pkgdown.

    +
    + + diff --git a/docs/dev/reference/get_model_env.html b/docs/dev/reference/get_model_env.html index 0c2b8d9bb..a932bbb99 100644 --- a/docs/dev/reference/get_model_env.html +++ b/docs/dev/reference/get_model_env.html @@ -6,7 +6,7 @@ -Tools to Register Models — get_model_env • parsnip +Working with the parsnip model environment — get_model_env • parsnip @@ -31,9 +31,10 @@ - + - + @@ -55,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,58 +123,118 @@ +
    -

    Tools to Register Models

    +

    Working with the parsnip model environment

    -

    Tools to Register Models

    +

    These functions read and write to the environment where the package stores +information about model specifications.

    get_model_env()
 
-set_new_model(model)
-
-set_model_mode(model, mode)
-
-set_model_engine(model, mode, eng)
-
-set_model_arg(model, eng, parsnip, original, func, has_submodel)
-
-set_dependency(model, eng, pkg)
-
-get_dependency(model)
-
-set_fit(model, mode, eng, value)
-
-get_fit(model)
+get_from_env(items)
 
-set_pred(model, mode, eng, type, value)
+set_in_env(...)
 
-get_pred_type(model, type)
-
-show_model_info(model)
+set_env_val(name, value)
    + +

    Arguments

    +
    • + + + + + + + + + + + + + + + + + +
    items

    A character string of objects in the model environment.

    ...

    Named values that will be assigned to the model environment.

    name

    A single character value for a new symbol in the model environment.

    value

    A single value for a new value in the model environment.

    + +

    References

    -get_from_env(items) - +

    "Making a parsnip model from scratch" +https://tidymodels.github.io/parsnip/articles/articles/Scratch.html

    + +

    Examples

    + 
    # Access the model data:
+current_code <- get_model_env()
+ls(envir = current_code)
    #>  [1] "boost_tree"               "boost_tree_args"         
+#>  [3] "boost_tree_fit"           "boost_tree_modes"        
+#>  [5] "boost_tree_pkgs"          "boost_tree_predict"      
+#>  [7] "decision_tree"            "decision_tree_args"      
+#>  [9] "decision_tree_fit"        "decision_tree_modes"     
+#> [11] "decision_tree_pkgs"       "decision_tree_predict"   
+#> [13] "linear_reg"               "linear_reg_args"         
+#> [15] "linear_reg_fit"           "linear_reg_modes"        
+#> [17] "linear_reg_pkgs"          "linear_reg_predict"      
+#> [19] "logistic_reg"             "logistic_reg_args"       
+#> [21] "logistic_reg_fit"         "logistic_reg_modes"      
+#> [23] "logistic_reg_pkgs"        "logistic_reg_predict"    
+#> [25] "mars"                     "mars_args"               
+#> [27] "mars_fit"                 "mars_modes"              
+#> [29] "mars_pkgs"                "mars_predict"            
+#> [31] "mlp"                      "mlp_args"                
+#> [33] "mlp_fit"                  "mlp_modes"               
+#> [35] "mlp_pkgs"                 "mlp_predict"             
+#> [37] "models"                   "modes"                   
+#> [39] "multinom_reg"             "multinom_reg_args"       
+#> [41] "multinom_reg_fit"         "multinom_reg_modes"      
+#> [43] "multinom_reg_pkgs"        "multinom_reg_predict"    
+#> [45] "nearest_neighbor"         "nearest_neighbor_args"   
+#> [47] "nearest_neighbor_fit"     "nearest_neighbor_modes"  
+#> [49] "nearest_neighbor_pkgs"    "nearest_neighbor_predict"
+#> [51] "null_model"               "null_model_args"         
+#> [53] "null_model_fit"           "null_model_modes"        
+#> [55] "null_model_pkgs"          "null_model_predict"      
+#> [57] "rand_forest"              "rand_forest_args"        
+#> [59] "rand_forest_fit"          "rand_forest_modes"       
+#> [61] "rand_forest_pkgs"         "rand_forest_predict"     
+#> [63] "surv_reg"                 "surv_reg_args"           
+#> [65] "surv_reg_fit"             "surv_reg_modes"          
+#> [67] "surv_reg_pkgs"            "surv_reg_predict"        
+#> [69] "svm_poly"                 "svm_poly_args"           
+#> [71] "svm_poly_fit"             "svm_poly_modes"          
+#> [73] "svm_poly_pkgs"            "svm_poly_predict"        
+#> [75] "svm_rbf"                  "svm_rbf_args"            
+#> [77] "svm_rbf_fit"              "svm_rbf_modes"           
+#> [79] "svm_rbf_pkgs"             "svm_rbf_predict"         
    
+
  • Arguments
    • + +
  • References
    • + +
  • Examples
    • + + + + diff --git a/docs/dev/reference/has_multi_predict.html b/docs/dev/reference/has_multi_predict.html new file mode 100644 index 000000000..f6c585b68 --- /dev/null +++ b/docs/dev/reference/has_multi_predict.html @@ -0,0 +1,248 @@ + + + + + + + + +Tools for models that predict on sub-models — has_multi_predict • parsnip + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Tools for models that predict on sub-models

    + + +
    + +
    + +

    has_multi_predict() tests to see if an object can make multiple +predictions on submodels from the same object. multi_predict_args() +returns the names of the argments to multi_predict() for this model +(if any).

    + +
    + + 
    has_multi_predict(object, ...)
+
+# S3 method for default
+has_multi_predict(object, ...)
+
+# S3 method for model_fit
+has_multi_predict(object, ...)
+
+# S3 method for workflow
+has_multi_predict(object, ...)
+
+multi_predict_args(object, ...)
+
+# S3 method for default
+multi_predict_args(object, ...)
+
+# S3 method for model_fit
+multi_predict_args(object, ...)
+
+# S3 method for workflow
+multi_predict_args(object, ...)
    + +

    Arguments

    + + + + + + + + + + +
    object

    An object to test.

    ...

    Not currently used.

    + +

    Value

    + +

    has_multi_predict() returns single logical value while +multi_predict() returns a character vector of argument names (or NA +if none exist).

    + + +

    Examples

    + 
    lm_model_idea <- linear_reg() %>% set_engine("lm")
+has_multi_predict(lm_model_idea)
    #> [1] FALSE
    lm_model_fit <- fit(lm_model_idea, mpg ~ ., data = mtcars)
+has_multi_predict(lm_model_fit)
    #> [1] FALSE
    
+multi_predict_args(lm_model_fit)
    #> [1] NA
    
+library(kknn)
+
+knn_fit <-
+  nearest_neighbor(mode = "regression", neighbors = 5) %>%
+  set_engine("kknn") %>%
+  fit(mpg ~ ., mtcars)
+
+multi_predict_args(knn_fit)
    #> [1] "neighbors"
    
+multi_predict(knn_fit, mtcars[1, -1], neighbors = 1:4)$.pred
    #> [[1]]
+#> # A tibble: 4 x 2
+#>   neighbors .pred
+#>       <int> <dbl>
+#> 1         1  21  
+#> 2         2  21  
+#> 3         3  20.9
+#> 4         4  21.0
+#>
    +
    + +
    + + +
    +
    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    +
    + +
    +

    + Developed by Max Kuhn, Davis Vaughan. + Site built by pkgdown. +

    +
    + +
    +
    + + + + + + + + diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index 0540feb14..bf6b5992e 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -52,6 +52,7 @@ gtag('config', 'UA-115082821-1'); + @@ -71,7 +72,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -99,6 +100,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -115,6 +119,7 @@ +
    @@ -275,7 +280,7 @@

    multi_predict()

    +

    multi_predict

    Model predictions across many sub-models

    @@ -345,7 +350,7 @@

    pred_types check_mod_val() get_model_env() check_mode_val() check_engine_val() check_arg_val() check_submodels_val() check_func_val() check_fit_info() check_pred_info() check_pkg_val() set_new_model() set_model_mode() set_model_engine() set_model_arg() set_dependency() get_dependency() set_fit() get_fit() set_pred() get_pred_type() show_model_info() get_from_env()

    +

    set_new_model() set_model_mode() set_model_engine() set_model_arg() set_dependency() get_dependency() set_fit() get_fit() set_pred() get_pred_type() show_model_info() pred_value_template()

    Tools to Register Models

    @@ -364,6 +369,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/keras_mlp.html b/docs/dev/reference/keras_mlp.html index ed96c15ed..251c32769 100644 --- a/docs/dev/reference/keras_mlp.html +++ b/docs/dev/reference/keras_mlp.html @@ -57,6 +57,7 @@ gtag('config', 'UA-115082821-1'); + @@ -76,7 +77,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -104,6 +105,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -120,6 +124,7 @@ +
    @@ -203,6 +208,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/lending_club.html b/docs/dev/reference/lending_club.html index bb62cc077..14b17f066 100644 --- a/docs/dev/reference/lending_club.html +++ b/docs/dev/reference/lending_club.html @@ -55,6 +55,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +75,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +103,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,6 +122,7 @@ +
    @@ -200,6 +205,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/linear_reg.html b/docs/dev/reference/linear_reg.html index 31dfc2fbf..76c7a21b4 100644 --- a/docs/dev/reference/linear_reg.html +++ b/docs/dev/reference/linear_reg.html @@ -68,6 +68,7 @@ gtag('config', 'UA-115082821-1'); + @@ -87,7 +88,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -115,6 +116,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -131,6 +135,7 @@ +
    @@ -262,9 +267,9 @@

    -

    When using glmnet models, there is the option to pass -multiple values (or no values) to the penalty argument. This -can have an effect on the model object results. When using the +

    For glmnet models, the full regularization path is always fit regardless +of the value given to penalty. Also, there is the option to pass +multiple values (or no values) to the penalty argument. When using the predict() method in these cases, the return value depends on the value of penalty. When using predict(), only a single value of the penalty can be used. When predicting on multiple @@ -326,6 +331,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/logistic_reg.html b/docs/dev/reference/logistic_reg.html index ca4332916..996296a74 100644 --- a/docs/dev/reference/logistic_reg.html +++ b/docs/dev/reference/logistic_reg.html @@ -68,6 +68,7 @@ gtag('config', 'UA-115082821-1'); + @@ -87,7 +88,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -115,6 +116,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -131,6 +135,7 @@ +
    @@ -261,9 +266,9 @@

    -

    When using glmnet models, there is the option to pass -multiple values (or no values) to the penalty argument. This -can have an effect on the model object results. When using the +

    For glmnet models, the full regularization path is always fit regardless +of the value given to penalty. Also, there is the option to pass +multiple values (or no values) to the penalty argument. When using the predict() method in these cases, the return value depends on the value of penalty. When using predict(), only a single value of the penalty can be used. When predicting on multiple @@ -326,6 +331,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/make_classes.html b/docs/dev/reference/make_classes.html index 7a43980cb..5cbbc05b9 100644 --- a/docs/dev/reference/make_classes.html +++ b/docs/dev/reference/make_classes.html @@ -55,6 +55,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +75,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +103,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,6 +122,7 @@ +
    @@ -162,6 +167,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/mars.html b/docs/dev/reference/mars.html index fafee1c3a..026bdcc56 100644 --- a/docs/dev/reference/mars.html +++ b/docs/dev/reference/mars.html @@ -71,6 +71,7 @@ gtag('config', 'UA-115082821-1'); + @@ -90,7 +91,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -118,6 +119,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -134,6 +138,7 @@ +
    @@ -281,6 +286,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/min_grid.html b/docs/dev/reference/min_grid.html new file mode 100644 index 000000000..f84bf9682 --- /dev/null +++ b/docs/dev/reference/min_grid.html @@ -0,0 +1,223 @@ + + + + + + + + +Determine the minimum set of model fits — min_grid • parsnip + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Determine the minimum set of model fits

    + + +
    + +
    + +

    min_grid determines exactly what models should be fit in order to +evaluate the entire set of tuning parameter combinations. This is for +internal use only and the API may change in the near future.

    + +
    + + 
    min_grid(x, grid, ...)
+
+# S3 method for boost_tree
+min_grid(x, grid, ...)
+
+# S3 method for linear_reg
+min_grid(x, grid, ...)
+
+# S3 method for logistic_reg
+min_grid(x, grid, ...)
+
+# S3 method for mars
+min_grid(x, grid, ...)
+
+# S3 method for multinom_reg
+min_grid(x, grid, ...)
+
+# S3 method for nearest_neighbor
+min_grid(x, grid, ...)
    + +

    Arguments

    + + + + + + + + + + + + + + +
    x

    A model specification.

    grid

    A tibble with tuning parameter combinations.

    ...

    Not currently used.

    + +

    Value

    + +

    A tibble with the minimum tuning parameters to fit and an additional +list column with the parameter combinations used for prediction.

    + + +
    + +
    + + +
    +
    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    +
    + +
    +

    + Developed by Max Kuhn, Davis Vaughan. + Site built by pkgdown. +

    +
    + +
    +
    + + + + + + + + diff --git a/docs/dev/reference/mlp.html b/docs/dev/reference/mlp.html index e9b746b82..9d3767027 100644 --- a/docs/dev/reference/mlp.html +++ b/docs/dev/reference/mlp.html @@ -71,6 +71,7 @@ gtag('config', 'UA-115082821-1'); + @@ -90,7 +91,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -118,6 +119,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -134,6 +138,7 @@ +
    @@ -318,6 +323,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/model_fit.html b/docs/dev/reference/model_fit.html index a800662ad..357121f69 100644 --- a/docs/dev/reference/model_fit.html +++ b/docs/dev/reference/model_fit.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -204,6 +209,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/model_printer.html b/docs/dev/reference/model_printer.html index 3165048e4..8cd752ae7 100644 --- a/docs/dev/reference/model_printer.html +++ b/docs/dev/reference/model_printer.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -162,6 +167,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/model_spec.html b/docs/dev/reference/model_spec.html index a37f6356c..4cc2fa42f 100644 --- a/docs/dev/reference/model_spec.html +++ b/docs/dev/reference/model_spec.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -256,6 +261,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/multi_predict.html b/docs/dev/reference/multi_predict.html index cbef60645..4c38f401e 100644 --- a/docs/dev/reference/multi_predict.html +++ b/docs/dev/reference/multi_predict.html @@ -55,6 +55,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +75,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +103,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,6 +122,7 @@ +
    @@ -137,7 +142,35 @@

    Model predictions across many sub-models

    multi_predict(object, ...)
 
 # S3 method for default
-multi_predict(object, ...)
    +multi_predict(object, ...) + +# S3 method for _xgb.Booster +multi_predict(object, new_data, type = NULL, + trees = NULL, ...) + +# S3 method for _C5.0 +multi_predict(object, new_data, type = NULL, + trees = NULL, ...) + +# S3 method for _elnet +multi_predict(object, new_data, type = NULL, + penalty = NULL, ...) + +# S3 method for _lognet +multi_predict(object, new_data, type = NULL, + penalty = NULL, ...) + +# S3 method for _earth +multi_predict(object, new_data, type = NULL, + num_terms = NULL, ...) + +# S3 method for _multnet +multi_predict(object, new_data, type = NULL, + penalty = NULL, ...) + +# S3 method for _train.kknn +multi_predict(object, new_data, type = NULL, + neighbors = NULL, ...)

    Arguments

    @@ -151,6 +184,33 @@

    Arg

    + + + + + + + + + + + + + + + + + + + + + + + +

    Optional arguments to pass to predict.model_fit(type = "raw") such as type.
    new_data

    A rectangular data object, such as a data frame.

    type

    A single character value or NULL. Possible values +are "numeric", "class", "prob", "conf_int", "pred_int", "quantile", +or "raw". When NULL, predict() will choose an appropriate value +based on the model's mode.

    trees

    An integer vector for the number of trees in the ensemble.

    penalty

    An numeric vector of penalty values.

    num_terms

    An integer vector for the number of MARS terms to retain.

    neighbors

    An integer vector for the number of nearest neighbors.

    Value

    @@ -172,6 +232,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/multinom_reg.html b/docs/dev/reference/multinom_reg.html index 7454d2238..466b4632c 100644 --- a/docs/dev/reference/multinom_reg.html +++ b/docs/dev/reference/multinom_reg.html @@ -68,6 +68,7 @@ gtag('config', 'UA-115082821-1'); + @@ -87,7 +88,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -115,6 +116,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -131,6 +135,7 @@ +
    @@ -250,9 +255,9 @@

    -

    When using glmnet models, there is the option to pass -multiple values (or no values) to the penalty argument. This -can have an effect on the model object results. When using the +

    For glmnet models, the full regularization path is always fit regardless +of the value given to penalty. Also, there is the option to pass +multiple values (or no values) to the penalty argument. When using the predict() method in these cases, the return value depends on the value of penalty. When using predict(), only a single value of the penalty can be used. When predicting on multiple @@ -308,6 +313,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/nearest_neighbor.html b/docs/dev/reference/nearest_neighbor.html index d22c15948..cc5e17664 100644 --- a/docs/dev/reference/nearest_neighbor.html +++ b/docs/dev/reference/nearest_neighbor.html @@ -71,6 +71,7 @@ gtag('config', 'UA-115082821-1'); + @@ -90,7 +91,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -118,6 +119,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -134,6 +138,7 @@ +
    @@ -257,6 +262,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/null_model.html b/docs/dev/reference/null_model.html index 53eb934a3..d4f00f0f3 100644 --- a/docs/dev/reference/null_model.html +++ b/docs/dev/reference/null_model.html @@ -57,6 +57,7 @@ gtag('config', 'UA-115082821-1'); + @@ -76,7 +77,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -104,6 +105,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -120,6 +124,7 @@ +
    @@ -199,6 +204,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/nullmodel.html b/docs/dev/reference/nullmodel.html index ed7909a78..72173402d 100644 --- a/docs/dev/reference/nullmodel.html +++ b/docs/dev/reference/nullmodel.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -232,6 +237,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/predict.model_fit.html b/docs/dev/reference/predict.model_fit.html index 4c6df5f30..ec5978cff 100644 --- a/docs/dev/reference/predict.model_fit.html +++ b/docs/dev/reference/predict.model_fit.html @@ -57,6 +57,7 @@ gtag('config', 'UA-115082821-1'); + @@ -76,7 +77,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -104,6 +105,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -120,6 +124,7 @@ +
    @@ -318,6 +323,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/rand_forest.html b/docs/dev/reference/rand_forest.html index 1dea25281..6dfcf5750 100644 --- a/docs/dev/reference/rand_forest.html +++ b/docs/dev/reference/rand_forest.html @@ -69,6 +69,7 @@ gtag('config', 'UA-115082821-1'); + @@ -88,7 +89,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -116,6 +117,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -132,6 +136,7 @@ +
    @@ -323,6 +328,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html index 3f8af8092..5fba8a0ad 100644 --- a/docs/dev/reference/reexports.html +++ b/docs/dev/reference/reexports.html @@ -61,6 +61,7 @@ gtag('config', 'UA-115082821-1'); + @@ -80,7 +81,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -108,6 +109,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -124,6 +128,7 @@ +
    @@ -157,6 +162,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/rpart_train.html b/docs/dev/reference/rpart_train.html index 79087d510..28bbd0e27 100644 --- a/docs/dev/reference/rpart_train.html +++ b/docs/dev/reference/rpart_train.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -202,6 +207,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/set_args.html b/docs/dev/reference/set_args.html index 5a0540165..182780a8d 100644 --- a/docs/dev/reference/set_args.html +++ b/docs/dev/reference/set_args.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -197,6 +202,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/set_engine.html b/docs/dev/reference/set_engine.html index 539fc613d..f7b67d3b5 100644 --- a/docs/dev/reference/set_engine.html +++ b/docs/dev/reference/set_engine.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -196,6 +201,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/set_new_model.html b/docs/dev/reference/set_new_model.html new file mode 100644 index 000000000..239fec458 --- /dev/null +++ b/docs/dev/reference/set_new_model.html @@ -0,0 +1,356 @@ + + + + + + + + +Tools to Register Models — set_new_model • parsnip + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    +
    +

    Tools to Register Models

    + + +
    + +
    + +

    These functions are similar to constructors and can be used to validate +that there are no conflicts with the underlying model structures used by the +package.

    + +
    + + 
    set_new_model(model)
+
+set_model_mode(model, mode)
+
+set_model_engine(model, mode, eng)
+
+set_model_arg(model, eng, parsnip, original, func, has_submodel)
+
+set_dependency(model, eng, pkg)
+
+get_dependency(model)
+
+set_fit(model, mode, eng, value)
+
+get_fit(model)
+
+set_pred(model, mode, eng, type, value)
+
+get_pred_type(model, type)
+
+show_model_info(model)
+
+pred_value_template(pre = NULL, post = NULL, func, ...)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    model

    A single character string for the model type (e.g. +"rand_forest", etc).

    mode

    A single character string for the model mode (e.g. "regression").

    eng

    A single character string for the model engine.

    parsnip

    A single character string for the "harmonized" argument name +that parsnip exposes.

    original

    A single character string for the argument name that +underlying model function uses.

    func

    A named character vector that describes how to call +a function. func should have elements pkg and fun. The +former is optional but is recommended and the latter is +required. For example, c(pkg = "stats", fun = "lm") would be +used to invoke the usual linear regression function. In some +cases, it is helpful to use c(fun = "predict") when using a +package's predict method.

    has_submodel

    A single logical for whether the argument +can make predictions on mutiple submodels at once.

    pkg

    An options character string for a package name.

    value

    A list that conforms to the fit_obj or pred_obj description +above, depending on context.

    type

    A single character value for the type of prediction. Possible +values are: class, conf_int, numeric, pred_int, prob, quantile, +and raw.

    pre, post

    Optional functions for pre- and post-processing of prediction +results.

    ...

    Optional arguments that should be passed into the args slot for +prediction objects.

    arg

    A single character string for the model argument name.

    fit_obj

    A list with elements interface, protect, +func and defaults. See the package vignette "Making a +parsnip model from scratch".

    pred_obj

    A list with elements pre, post, func, and args. +See the package vignette "Making a parsnip model from scratch".

    + +

    Details

    + +

    These functions are available for users to add their +own models or engines (in package or otherwise) so that they can +be accessed using parsnip. This are more thoroughly documented +on the package web site (see references below).

    +

    In short, parsnip stores an environment object that contains +all of the information and code about how models are used (e.g. +fitting, predicting, etc). These functions can be used to add +models to that environment as well as helper functions that can +be used to makes sure that the model data is in the right +format.

    +

    check_model_exists() checks the model value and ensures that the model has +already been registered. check_model_doesnt_exist() checks the model value +and also checks to see if it is novel in the environment.

    + +

    References

    + +

    "Making a parsnip model from scratch" +https://tidymodels.github.io/parsnip/articles/articles/Scratch.html

    + + +

    Examples

    + 
    # set_new_model("shallow_learning_model")
+
+# Show the information about a model:
+show_model_info("rand_forest")
    #> Information for `rand_forest`
+#>  modes: unknown, classification, regression 
+#> 
+#>  engines: 
+#>    classification: randomForest, ranger, spark
+#>    regression:     randomForest, ranger, spark
+#> 
+#>  arguments: 
+#>    ranger:       
+#>       mtry  --> mtry
+#>       trees --> num.trees
+#>       min_n --> min.node.size
+#>    randomForest: 
+#>       mtry  --> mtry
+#>       trees --> ntree
+#>       min_n --> nodesize
+#>    spark:        
+#>       mtry  --> feature_subset_strategy
+#>       trees --> num_trees
+#>       min_n --> min_instances_per_node
+#> 
+#>  fit modules:
+#>          engine           mode
+#>          ranger classification
+#>          ranger     regression
+#>    randomForest classification
+#>    randomForest     regression
+#>           spark classification
+#>           spark     regression
+#> 
+#>  prediction modules:
+#>              mode       engine                    methods
+#>    classification randomForest           class, prob, raw
+#>    classification       ranger class, conf_int, prob, raw
+#>    classification        spark                class, prob
+#>        regression randomForest               numeric, raw
+#>        regression       ranger     conf_int, numeric, raw
+#>        regression        spark                    numeric
+#>
    +
    + +
    + + +
    +
    +

    parsnip is a part of the tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.

    +
    + +
    +

    + Developed by Max Kuhn, Davis Vaughan. + Site built by pkgdown. +

    +
    + +
    +
    + + + + + + + + diff --git a/docs/dev/reference/show_call.html b/docs/dev/reference/show_call.html index c694e9adc..788ebd542 100644 --- a/docs/dev/reference/show_call.html +++ b/docs/dev/reference/show_call.html @@ -55,6 +55,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +75,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +103,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,6 +122,7 @@ +
    @@ -162,6 +167,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/surv_reg.html b/docs/dev/reference/surv_reg.html index 31b3d2a9a..bf3ced38d 100644 --- a/docs/dev/reference/surv_reg.html +++ b/docs/dev/reference/surv_reg.html @@ -66,6 +66,7 @@ gtag('config', 'UA-115082821-1'); + @@ -85,7 +86,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -113,6 +114,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -129,6 +133,7 @@ +
    @@ -274,6 +279,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/svm_poly.html b/docs/dev/reference/svm_poly.html index 3d59e9275..1c53bbfff 100644 --- a/docs/dev/reference/svm_poly.html +++ b/docs/dev/reference/svm_poly.html @@ -70,6 +70,7 @@ gtag('config', 'UA-115082821-1'); + @@ -89,7 +90,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -117,6 +118,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -133,6 +137,7 @@ +
    @@ -285,6 +290,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/svm_rbf.html b/docs/dev/reference/svm_rbf.html index 7b4e682a4..3398d87d9 100644 --- a/docs/dev/reference/svm_rbf.html +++ b/docs/dev/reference/svm_rbf.html @@ -70,6 +70,7 @@ gtag('config', 'UA-115082821-1'); + @@ -89,7 +90,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -117,6 +118,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -133,6 +137,7 @@ +
    @@ -281,6 +286,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/tidy.model_fit.html b/docs/dev/reference/tidy.model_fit.html index 3f2a45856..456457987 100644 --- a/docs/dev/reference/tidy.model_fit.html +++ b/docs/dev/reference/tidy.model_fit.html @@ -55,6 +55,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +75,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +103,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,6 +122,7 @@ +
    @@ -166,6 +171,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/translate.html b/docs/dev/reference/translate.html index 0b00a588c..3c09631e1 100644 --- a/docs/dev/reference/translate.html +++ b/docs/dev/reference/translate.html @@ -57,6 +57,7 @@ gtag('config', 'UA-115082821-1'); + @@ -76,7 +77,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -104,6 +105,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -120,6 +124,7 @@ +
    @@ -183,7 +188,7 @@

    Examp #> #> Model fit template: #> glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), -#> lambda = 0.01, family = "gaussian")

    +#> family = "gaussian")
    # `penalty` not applicable for this model. translate(lm_spec, engine = "lm")
    #> Linear Regression Model Specification (regression) #> @@ -231,6 +236,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/type_sum.model_spec.html b/docs/dev/reference/type_sum.model_spec.html index 1eaf4d4fd..7f2fffdfa 100644 --- a/docs/dev/reference/type_sum.model_spec.html +++ b/docs/dev/reference/type_sum.model_spec.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -179,6 +184,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/varying.html b/docs/dev/reference/varying.html index 1c4bfc250..8016ed088 100644 --- a/docs/dev/reference/varying.html +++ b/docs/dev/reference/varying.html @@ -55,6 +55,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +75,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +103,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,6 +122,7 @@ +
    @@ -146,6 +151,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/varying_args.model_spec.html b/docs/dev/reference/varying_args.model_spec.html index 526546ac9..c83f8de38 100644 --- a/docs/dev/reference/varying_args.model_spec.html +++ b/docs/dev/reference/varying_args.model_spec.html @@ -57,6 +57,7 @@ gtag('config', 'UA-115082821-1'); + @@ -76,7 +77,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -104,6 +105,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -120,6 +124,7 @@ +
    @@ -243,6 +248,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/wa_churn.html b/docs/dev/reference/wa_churn.html index d14010e43..fc7942f17 100644 --- a/docs/dev/reference/wa_churn.html +++ b/docs/dev/reference/wa_churn.html @@ -55,6 +55,7 @@ gtag('config', 'UA-115082821-1'); + @@ -74,7 +75,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -102,6 +103,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -118,6 +122,7 @@ +
    @@ -197,6 +202,7 @@

    Contents

    + + + diff --git a/docs/dev/reference/xgb_train.html b/docs/dev/reference/xgb_train.html index ce1535165..898e5c26c 100644 --- a/docs/dev/reference/xgb_train.html +++ b/docs/dev/reference/xgb_train.html @@ -56,6 +56,7 @@ gtag('config', 'UA-115082821-1'); + @@ -75,7 +76,7 @@ parsnip
    part of tidymodels - 0.0.2.9000 + 0.0.3.9000
    @@ -103,6 +104,9 @@
  • Making a parsnip model from scratch
    • +
  • + Evaluating submodels with the same model object +
  • @@ -119,6 +123,7 @@ +
    @@ -204,6 +209,7 @@

    Contents

    + + + diff --git a/docs/index.html b/docs/index.html index af96dbb82..3ede6f71d 100644 --- a/docs/index.html +++ b/docs/index.html @@ -173,8 +173,8 @@

    Dev status

    diff --git a/docs/news/index.html b/docs/news/index.html index 712020170..c6d19e821 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -138,8 +138,8 @@

    Breaking Changes

    @@ -150,8 +150,9 @@

    @@ -224,7 +225,7 @@