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Nov 19, 2021
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Use task_type instead of learner_class #141

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PhilippBach Nov 16, 2021
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PhilippBach Nov 16, 2021
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PhilippBach Nov 16, 2021
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PhilippBach Nov 17, 2021
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PhilippBach Nov 17, 2021
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regression tests with `mlr3pipelines` based learner (class `GraphLear…
PhilippBach Nov 17, 2021
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PhilippBach Nov 18, 2021
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3 changes: 2 additions & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,8 @@ Suggests:
sandwich,
AER,
rpart,
bbotk
bbotk,
mlr3pipelines
VignetteBuilder: knitr
Collate:
'double_ml.R'
Expand Down
25 changes: 14 additions & 11 deletions R/double_ml.R
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Original file line number Diff line number Diff line change
Expand Up @@ -1150,7 +1150,7 @@ DoubleML = R6Class("DoubleML",
i_treat = NA_integer_,
fold_specific_params = NULL,
summary_table = NULL,
learner_class = list(),
task_type = list(),
is_cluster_data = FALSE,
n_folds_per_cluster = NA_integer_,
smpls_cluster_ = NULL,
Expand Down Expand Up @@ -1250,28 +1250,31 @@ DoubleML = R6Class("DoubleML",
check_character(learner, max.len = 1),
check_class(learner, "Learner"))

if (test_class(learner, "AutoTuner")) {
stop(paste0(
"Learners of class 'AutoTuner' are not supported."
))
}
if (is.character(learner)) {
# warning("Learner provision by character() will be deprecated in the
# future.")
learner = lrn(learner)
}

if (Regr & test_class(learner, "LearnerRegr")) {
private$learner_class[learner_name] = "LearnerRegr"
}
if (Classif & test_class(learner, "LearnerClassif")) {
private$learner_class[learner_name] = "LearnerClassif"
if ((Regr & learner$task_type == "regr") |
(Classif & learner$task_type == "classif")) {
private$task_type[learner_name] = learner$task_type
}

if ((Regr & !Classif & !test_class(learner, "LearnerRegr"))) {
if ((Regr & !Classif & !learner$task_type == "regr")) {
stop(paste0(
"Invalid learner provided for ", learner_name,
": must be of class 'LearnerRegr'"))
": 'learner$task_type' must be 'regr'"))
}
if ((Classif & !Regr & !test_class(learner, "LearnerClassif"))) {
if ((Classif & !Regr & !learner$task_type == "classif")) {
stop(paste0(
"Invalid learner provided for ", learner_name,
": must be of class 'LearnerClassif'"))
": 'learner$task_type must be 'classif'"))
}
invisible(learner)
},
Expand Down Expand Up @@ -1333,7 +1336,7 @@ DoubleML = R6Class("DoubleML",
this_learner = names(tune_settings$measure)[i_msr]
tune_settings$measure[[this_learner]] = set_default_measure(
tune_settings$measure[[this_learner]],
private$learner_class[[this_learner]])
private$task_type[[this_learner]])
}
}

Expand Down
87 changes: 44 additions & 43 deletions R/double_ml_iivm.R
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Original file line number Diff line number Diff line change
Expand Up @@ -138,42 +138,43 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
#' The `DoubleMLData` object providing the data and specifying the variables
#' of the causal model.
#'
#' @param ml_g ([`LearnerRegr`][mlr3::LearnerRegr], `character(1)`) \cr
#' An object of the class [mlr3 regression learner][mlr3::LearnerRegr] to
#' pass a learner, possibly with specified parameters, for example
#' `lrn("regr.cv_glmnet", s = "lambda.min")`.
#' Alternatively, a `character(1)` specifying the name of a
#' [mlr3 regression learner][mlr3::LearnerRegr] that is available in
#' [mlr3](https://mlr3.mlr-org.com/index.html) or its extension packages
#' [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/),
#' for example `"regr.cv_glmnet"`. \cr
#' @param ml_g ([`LearnerRegr`][mlr3::LearnerRegr],
#' [`Learner`][mlr3::Learner], `character(1)`) \cr
#' A learner of the class [`LearnerRegr`][mlr3::LearnerRegr], which is
#' available from [mlr3](https://mlr3.mlr-org.com/index.html) or its
#' extension packages [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/).
#' Alternatively, a [`Learner`][mlr3::Learner] object with public field
#' `task_type = "regr"` can be passed, for example of class
#' [`GraphLearner`][mlr3pipelines::GraphLearner]. The learner can possibly
#' be passed with specified parameters, for example
#' `lrn("regr.cv_glmnet", s = "lambda.min")`. \cr
#' `ml_g` refers to the nuisance function \eqn{g_0(Z,X) = E[Y|X,Z]}.
#'
#' @param ml_m ([`LearnerClassif`][mlr3::LearnerClassif], `character(1)`) \cr
#' An object of the class
#' [mlr3 classification learner][mlr3::LearnerClassif] to pass a learner,
#' possibly with specified parameters, for example
#' `lrn("classif.cv_glmnet", s = "lambda.min")`.
#' Alternatively, a `character(1)` specifying the name of
#' a [mlr3 classification learner][mlr3::LearnerClassif] that is available
#' in [mlr3](https://mlr3.mlr-org.com/index.html) or its extension packages
#' [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/),
#' for example `"classif.cv_glmnet"`. \cr
#' @param ml_m ([`LearnerClassif`][mlr3::LearnerClassif],
#' [`Learner`][mlr3::Learner], `character(1)`) \cr
#' A learner of the class [`LearnerClassif`][mlr3::LearnerClassif], which is
#' available from [mlr3](https://mlr3.mlr-org.com/index.html) or its
#' extension packages [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/).
#' Alternatively, a [`Learner`][mlr3::Learner] object with public field
#' `task_type = "classif"` can be passed, for example of class
#' [`GraphLearner`][mlr3pipelines::GraphLearner]. The learner can possibly
#' be passed with specified parameters, for example
#' `lrn("classif.cv_glmnet", s = "lambda.min")`. \cr
#' `ml_m` refers to the nuisance function \eqn{m_0(X) = E[Z|X]}.
#'
#' @param ml_r ([`LearnerClassif`][mlr3::LearnerClassif], `character(1)`) \cr
#' An object of the class
#' [mlr3 classification learner][mlr3::LearnerClassif] to pass a learner,
#' possibly with specified parameters, for example
#' `lrn("classif.cv_glmnet", s = "lambda.min")`.
#' Alternatively, a `character(1)` specifying the name of a
#' [mlr3 classification learner][mlr3::LearnerClassif] that is available in
#' [mlr3](https://mlr3.mlr-org.com/index.html) or its extension packages
#' [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/),
#' for example `"classif.cv_glmnet"`. \cr
#' @param ml_r ([`LearnerClassif`][mlr3::LearnerClassif],
#' [`Learner`][mlr3::Learner], `character(1)`) \cr
#' A learner of the class [`LearnerClassif`][mlr3::LearnerClassif], which is
#' available from [mlr3](https://mlr3.mlr-org.com/index.html) or its
#' extension packages [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/).
#' Alternatively, a [`Learner`][mlr3::Learner] object with public field
#' `task_type = "classif"` can be passed, for example of class
#' [`GraphLearner`][mlr3pipelines::GraphLearner]. The learner can possibly
#' be passed with specified parameters, for example
#' `lrn("classif.cv_glmnet", s = "lambda.min")`. \cr
#' `ml_r` refers to the nuisance function \eqn{r_0(Z,X) = E[D|X,Z]}.
#'
#' @param n_folds (`integer(1)`)\cr
Expand Down Expand Up @@ -241,7 +242,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",

private$check_data(self$data)
private$check_score(self$score)
private$learner_class = list(
private$task_type = list(
"ml_g" = NULL,
"ml_m" = NULL,
"ml_r" = NULL)
Expand Down Expand Up @@ -295,7 +296,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
smpls = smpls,
est_params = self$get_params("ml_m"),
return_train_preds = FALSE,
learner_class = private$learner_class$ml_m,
task_type = private$task_type$ml_m,
fold_specific_params = private$fold_specific_params)

g0_hat = dml_cv_predict(self$learner$ml_g,
Expand All @@ -306,7 +307,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
smpls = cond_smpls$smpls_0,
est_params = self$get_params("ml_g0"),
return_train_preds = FALSE,
learner_class = private$learner_class$ml_g,
task_type = private$task_type$ml_g,
fold_specific_params = private$fold_specific_params)

g1_hat = dml_cv_predict(self$learner$ml_g,
Expand All @@ -317,7 +318,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
smpls = cond_smpls$smpls_1,
est_params = self$get_params("ml_g1"),
return_train_preds = FALSE,
learner_class = private$learner_class$ml_g,
task_type = private$task_type$ml_g,
fold_specific_params = private$fold_specific_params)

if (self$subgroups$always_takers == FALSE) {
Expand All @@ -331,7 +332,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
smpls = cond_smpls$smpls_0,
est_params = self$get_params("ml_r0"),
return_train_preds = FALSE,
learner_class = private$learner_class$ml_r,
task_type = private$task_type$ml_r,
fold_specific_params = private$fold_specific_params)
}

Expand All @@ -346,7 +347,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
smpls = cond_smpls$smpls_1,
est_params = self$get_params("ml_r1"),
return_train_preds = FALSE,
learner_class = private$learner_class$ml_r,
task_type = private$task_type$ml_r,
fold_specific_params = private$fold_specific_params)
}

Expand Down Expand Up @@ -421,7 +422,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
nuisance_id = "nuis_m",
param_set$ml_m, tune_settings,
tune_settings$measure$ml_m,
private$learner_class$ml_m)
private$task_type$ml_m)

tuning_result_g0 = dml_tune(self$learner$ml_g,
c(self$data$x_cols, self$data$other_treat_cols),
Expand All @@ -430,7 +431,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
nuisance_id = "nuis_g0",
param_set$ml_g, tune_settings,
tune_settings$measure$ml_g,
private$learner_class$ml_g)
private$task_type$ml_g)

tuning_result_g1 = dml_tune(self$learner$ml_g,
c(self$data$x_cols, self$data$other_treat_cols),
Expand All @@ -439,7 +440,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
nuisance_id = "nuis_g1",
param_set$ml_g, tune_settings,
tune_settings$measure$ml_g,
private$learner_class$ml_g)
private$task_type$ml_g)

if (self$subgroups$always_takers == TRUE) {
tuning_result_r0 = dml_tune(self$learner$ml_r,
Expand All @@ -449,7 +450,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
nuisance_id = "nuis_r0",
param_set$ml_r, tune_settings,
tune_settings$measure$ml_r,
private$learner_class$ml_r)
private$task_type$ml_r)
} else {
tuning_result_r0 = list(list(), "params" = list(list()))
}
Expand All @@ -462,7 +463,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
nuisance_id = "nuis_r1",
param_set$ml_r, tune_settings,
tune_settings$measure$ml_r,
private$learner_class$ml_r)
private$task_type$ml_r)
} else {
tuning_result_r1 = list(list(), "params" = list(list()))
}
Expand Down
57 changes: 29 additions & 28 deletions R/double_ml_irm.R
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -107,29 +107,30 @@ DoubleMLIRM = R6Class("DoubleMLIRM",
#' The `DoubleMLData` object providing the data and specifying the variables
#' of the causal model.
#'
#' @param ml_g ([`LearnerRegr`][mlr3::LearnerRegr], `character(1)`) \cr
#' An object of the class [mlr3 regression learner][mlr3::LearnerRegr] to
#' pass a learner, possibly with specified parameters, for example
#' `lrn("regr.cv_glmnet", s = "lambda.min")`.
#' Alternatively, a `character(1)` specifying the name of a
#' [mlr3 regression learner][mlr3::LearnerRegr] that is available in
#' [mlr3](https://mlr3.mlr-org.com/index.html) or its extension packages
#' [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/), for example
#' `"regr.cv_glmnet"`. \cr
#' @param ml_g ([`LearnerRegr`][mlr3::LearnerRegr],
#' [`Learner`][mlr3::Learner], `character(1)`) \cr
#' A learner of the class [`LearnerRegr`][mlr3::LearnerRegr], which is
#' available from [mlr3](https://mlr3.mlr-org.com/index.html) or its
#' extension packages [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/).
#' Alternatively, a [`Learner`][mlr3::Learner] object with public field
#' `task_type = "regr"` can be passed, for example of class
#' [`GraphLearner`][mlr3pipelines::GraphLearner]. The learner can possibly
#' be passed with specified parameters, for example
#' `lrn("regr.cv_glmnet", s = "lambda.min")`. \cr
#' `ml_g` refers to the nuisance function \eqn{g_0(X) = E[Y|X,D]}.
#'
#' @param ml_m ([`LearnerClassif`][mlr3::LearnerClassif], `character(1)`) \cr
#' An object of the class
#' [mlr3 classification learner][mlr3::LearnerClassif] to pass a learner,
#' possibly with specified parameters, for example
#' `lrn("classif.cv_glmnet", s = "lambda.min")`.
#' Alternatively, a `character(1)` specifying the name of a
#' [mlr3 classification learner][mlr3::LearnerClassif] that is available
#' in [mlr3](https://mlr3.mlr-org.com/index.html) or its extension packages
#' [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/),
#' for example `"classif.cv_glmnet"`. \cr
#' @param ml_m ([`LearnerClassif`][mlr3::LearnerClassif],
#' [`Learner`][mlr3::Learner], `character(1)`) \cr
#' A learner of the class [`LearnerClassif`][mlr3::LearnerClassif], which is
#' available from [mlr3](https://mlr3.mlr-org.com/index.html) or its
#' extension packages [mlr3learners](https://mlr3learners.mlr-org.com/) or
#' [mlr3extralearners](https://mlr3extralearners.mlr-org.com/).
#' Alternatively, a [`Learner`][mlr3::Learner] object with public field
#' `task_type = "classif"` can be passed, for example of class
#' [`GraphLearner`][mlr3pipelines::GraphLearner]. The learner can possibly
#' be passed with specified parameters, for example
#' `lrn("classif.cv_glmnet", s = "lambda.min")`. \cr
#' `ml_m` refers to the nuisance function \eqn{m_0(X) = E[D|X]}.
#'
#' @param n_folds (`integer(1)`)\cr
Expand Down Expand Up @@ -185,7 +186,7 @@ DoubleMLIRM = R6Class("DoubleMLIRM",

private$check_data(self$data)
private$check_score(self$score)
private$learner_class = list(
private$task_type = list(
"ml_g" = NULL,
"ml_m" = NULL)
ml_g = private$assert_learner(ml_g, "ml_g", Regr = TRUE, Classif = FALSE)
Expand Down Expand Up @@ -227,7 +228,7 @@ DoubleMLIRM = R6Class("DoubleMLIRM",
smpls = smpls,
est_params = self$get_params("ml_m"),
return_train_preds = FALSE,
learner_class = private$learner_class$ml_m,
task_type = private$task_type$ml_m,
fold_specific_params = private$fold_specific_params)

g0_hat = dml_cv_predict(self$learner$ml_g,
Expand All @@ -238,7 +239,7 @@ DoubleMLIRM = R6Class("DoubleMLIRM",
smpls = cond_smpls$smpls_0,
est_params = self$get_params("ml_g0"),
return_train_preds = FALSE,
learner_class = private$learner_class$ml_g,
task_type = private$task_type$ml_g,
fold_specific_params = private$fold_specific_params)

g1_hat = NULL
Expand All @@ -251,7 +252,7 @@ DoubleMLIRM = R6Class("DoubleMLIRM",
smpls = cond_smpls$smpls_1,
est_params = self$get_params("ml_g1"),
return_train_preds = FALSE,
learner_class = private$learner_class$ml_g,
task_type = private$task_type$ml_g,
fold_specific_params = private$fold_specific_params)
}

Expand Down Expand Up @@ -330,7 +331,7 @@ DoubleMLIRM = R6Class("DoubleMLIRM",
nuisance_id = "nuis_m",
param_set$ml_m, tune_settings,
tune_settings$measure$ml_m,
private$learner_class$ml_m)
private$task_type$ml_m)

tuning_result_g0 = dml_tune(self$learner$ml_g,
c(self$data$x_cols, self$data$other_treat_cols),
Expand All @@ -339,7 +340,7 @@ DoubleMLIRM = R6Class("DoubleMLIRM",
nuisance_id = "nuis_g0",
param_set$ml_g, tune_settings,
tune_settings$measure$ml_g,
private$learner_class$ml_g)
private$task_type$ml_g)

if ((is.character(self$score) && self$score == "ATE") || is.function(self$score)) {
tuning_result_g1 = dml_tune(self$learner$ml_g,
Expand All @@ -349,7 +350,7 @@ DoubleMLIRM = R6Class("DoubleMLIRM",
nuisance_id = "nuis_g1",
param_set$ml_g, tune_settings,
tune_settings$measure$ml_g,
private$learner_class$ml_g)
private$task_type$ml_g)
} else {
tuning_result_g1 = list(list(), "params" = list(list()))
}
Expand Down
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