Package 'mlr3proba'

Description

Provides extensions for probabilistic supervised learning for 'mlr3'. This currently includes survival analysis, probabilistic regression and density estimation.

Author(s)

Maintainer: John Zobolas [email protected] (ORCID)

Authors:

• Raphael Sonabend [email protected] (ORCID)

• Franz Kiraly [email protected]

• Michel Lang [email protected] (ORCID)

• Philip Studener [email protected]

Other contributors:

• Nurul Ain Toha [email protected] [contributor]

• Andreas Bender [email protected] (ORCID) [contributor]

• Lukas Burk [email protected] (ORCID) [contributor]

• Maximilian Mücke [email protected] (ORCID) [contributor]

• Lee Xingzhuo Li [email protected] (ORCID) [contributor]

• Markus Goeswein [email protected] [contributor]

See Also

Useful links:

• https://mlr3proba.mlr-org.com

• https://github.com/mlr-org/mlr3proba

• Report bugs at https://github.com/mlr-org/mlr3proba/issues

Description

actg dataset from Hosmer et al. (2008)

Usage

actg

Format

id

Identification Code

time

Time to AIDS diagnosis or death (days).

censor

Event indicator. 1 = AIDS defining diagnosis, 0 = Otherwise.

time_d

Time to death (days)

censor_d

Event indicator for death (only). 1 = Death, 0 = Otherwise.

tx

Treatment indicator. 1 = Treatment includes IDV, 0 = Control group.

txgrp

Treatment group indicator. 1 = ZDV + 3TC. 2 = ZDV + 3TC + IDV. 3 = d4T + 3TC. 4 = d4T + 3TC + IDV.

strat2

CD4 stratum at screening. 0 = CD4 <= 50. 1 = CD4 > 50.

sexF

0 = Male. 1 = Female.

raceth

Race/Ethnicity. 1 = White Non-Hispanic. 2 = Black Non-Hispanic. 3 = Hispanic. 4 = Asian, Pacific Islander. 5 = American Indian, Alaskan Native. 6 = Other/unknown.

ivdrug

IV drug use history. 1 = Never. 2 = Currently. 3 = Previously.

hemophil

Hemophiliac. 1 = Yes. 0 = No.

karnof

Karnofsky Performance Scale. 100 = Normal; no complaint no evidence of disease. 90 = Normal activity possible; minor signs/symptoms of disease. 80 = Normal activity with effort; some signs/symptoms of disease. 70 = Cares for self; normal activity/active work not possible.

cd4

Baseline CD4 count (Cells/Milliliter).

priorzdv

Months of prior ZDV use (months).

age

Age at Enrollment (years).

Source

https://onlinelibrary.wiley.com/doi/book/10.1002/9780470258019

References

Hosmer, D.W. and Lemeshow, S. and May, S. (2008) Applied Survival Analysis: Regression Modeling of Time to Event Data: Second Edition, John Wiley and Sons Inc., New York, NY

Description

Convert object to a PredictionDens.

Usage

as_prediction_dens(x, ...)

## S3 method for class 'PredictionDens'
as_prediction_dens(x, ...)

## S3 method for class 'data.frame'
as_prediction_dens(x, ...)

Arguments

Value

PredictionDens.

Examples

library(mlr3)
task = tsk("precip")
learner = lrn("dens.hist")
learner$train(task)
p = learner$predict(task)

# convert to a data.table
tab = as.data.table(p)

# convert back to a Prediction
as_prediction_dens(tab)

Description

Convert object to a PredictionSurv.

Usage

as_prediction_surv(x, ...)

## S3 method for class 'PredictionSurv'
as_prediction_surv(x, ...)

## S3 method for class 'data.frame'
as_prediction_surv(x, ...)

Arguments

Value

PredictionSurv.

Examples

library(mlr3)
task = tsk("rats")
learner = lrn("surv.coxph")
learner$train(task)
p = learner$predict(task)

# convert to a data.table
tab = as.data.table(p)

# convert back to a Prediction
as_prediction_surv(tab)

Description

Convert object to a density task (TaskDens).

Usage

as_task_dens(x, ...)

## S3 method for class 'TaskDens'
as_task_dens(x, clone = FALSE, ...)

## S3 method for class 'data.frame'
as_task_dens(x, id = deparse(substitute(x)), ...)

## S3 method for class 'DataBackend'
as_task_dens(x, id = deparse(substitute(x)), ...)

Arguments

Description

Convert object to a survival task (TaskSurv).

Usage

as_task_surv(x, ...)

## S3 method for class 'TaskSurv'
as_task_surv(x, clone = FALSE, ...)

## S3 method for class 'data.frame'
as_task_surv(
  x,
  time = "time",
  event = "event",
  time2 = "time2",
  type = "right",
  id = deparse(substitute(x)),
  ...
)

## S3 method for class 'DataBackend'
as_task_surv(
  x,
  time = "time",
  event = "event",
  time2,
  type = "right",
  id = deparse(substitute(x)),
  ...
)

Arguments

Description

Asserts if the given input matrix is a (discrete) survival probabilities matrix using Rcpp. The following checks are performed:

1. All values are probabilities, i.e. $S(t) \in [0,1]$

2. Column names correspond to time-points and should therefore be coercable to numeric and increasing

3. Per row/observation, the survival probabilities decrease non-strictly, i.e. $S(t) \ge S(t+1)$

Usage

assert_surv_matrix(x)

Arguments

Value

if the assertion fails an error occurs, otherwise NULL is returned invisibly.

Examples

x = matrix(data = c(1,0.6,0.4,0.8,0.8,0.7), nrow = 2, ncol = 3, byrow = TRUE)
colnames(x) = c(12, 34, 42)
x

assert_surv_matrix(x)

Description

Visualizations for LearnerSurvCoxPH.

The argument type controls what kind of plot is drawn. The only possible choice right now is "ggforest" which is a Forest Plot, using ggforest. This plot displays the estimated hazard ratios (HRs) and their confidence intervals (CIs) for different variables included in the trained model.

Usage

## S3 method for class 'LearnerSurvCoxPH'
autoplot(object, type = "ggforest", ...)

Arguments

Value

ggplot2::ggplot().

Examples

library(ggplot2)

task = tsk("lung")
learner = lrn("surv.coxph")
learner$train(task)
autoplot(learner)

Description

Generates plots for PredictionSurv, depending on argument type:

• "calib" (default): Calibration plot comparing the average predicted survival distribution (Pred) to a Kaplan-Meier prediction (KM), this is not a comparison of a stratified crank or lp.

• "dcalib": Distribution calibration plot. A model is considered D-calibrated if, for any given quantile p, the proportion of observed outcomes occurring before the predicted time quantile, matches p. For example, 50% of events should occur before the predicted median survival time (i.e. the time corresponding to a predicted survival probability of 0.5). Good calibration means that the resulting line plot will lie close to the straight line $y = x$. Note that we impute NAs from the predicted quantile function with the maximum observed outcome time.

• "scalib": Smoothed calibration plot at a specific time point. For a range of probabilities of event occurrence in $[0,1]$ (x-axis), the y-axis has the smoothed observed proportions calculated using hazard regression (model is fitted using the predicted probabilities). See Austin et al. (2020) and MeasureSurvICI for more details. Good calibration means that the resulting line plot will lie close to the straight line $y = x$.

• "isd": Plot the predicted individual survival distributions (survival curves) for the test set's observations.

Usage

## S3 method for class 'PredictionSurv'
autoplot(
  object,
  type = "calib",
  times = NULL,
  row_ids = NULL,
  cuts = 11L,
  time = NULL,
  theme = theme_minimal(),
  ...
)

Arguments

Notes

1. object must have a distr prediction, as all plot types use the predicted survival distribution/matrix.

2. type = "dcalib" is drawn a bit differently from Haider et al. (2020), though its still conceptually the same.

References

Haider, Humza, Hoehn, Bret, Davis, Sarah, Greiner, Russell (2020). “Effective Ways to Build and Evaluate Individual Survival Distributions.” Journal of Machine Learning Research, 21(85), 1–63. https://jmlr.org/papers/v21/18-772.html.

Austin, C. P, Harrell, E. F, van Klaveren, David (2020). “Graphical calibration curves and the integrated calibration index (ICI) for survival models.” Statistics in Medicine, 39(21), 2714. ISSN 10970258, doi:10.1002/SIM.8570, https://pmc.ncbi.nlm.nih.gov/articles/PMC7497089/.

Examples

library(ggplot2)

learner = lrn("surv.coxph")
task = tsk("gbcs")
p = learner$train(task, row_ids = 1:600)$predict(task, row_ids = 601:686)

# calibration by comparison of average prediction to Kaplan-Meier
autoplot(p)

# same as above, use specific time points
autoplot(p, times = seq(1, 1000, 5))

# Distribution-calibration (D-Calibration)
autoplot(p, type = "dcalib")

# Smoothed Calibration (S-Calibration)
autoplot(p, type = "scalib", time = 1750)

# Predicted survival curves (all observations)
autoplot(p, type = "isd")

# Predicted survival curves (specific observations)
autoplot(p, type = "isd", row_ids = c(601, 651, 686))

Description

Generates plots for TaskDens.

Usage

## S3 method for class 'TaskDens'
autoplot(object, type = "dens", theme = theme_minimal(), ...)

Arguments

Value

ggplot2::ggplot() object.

Examples

library(ggplot2)

task = tsk("precip")
task$head()

autoplot(task, bins = 15)
autoplot(task, type = "freq", bins = 15)
autoplot(task, type = "overlay", bins = 15)
autoplot(task, type = "freqpoly", bins = 15)

Description

Generates plots for TaskSurv, depending on argument type:

• "target": Calls GGally::ggsurv() on a survival::survfit() object. This computes the Kaplan-Meier survival curve for the observations if this task.

• "duo": Passes data and additional arguments down to GGally::ggduo(). columnsX is target, columnsY is features.

• "pairs": Passes data and additional arguments down to GGally::ggpairs(). Color is set to target column.

Usage

## S3 method for class 'TaskSurv'
autoplot(
  object,
  type = "target",
  theme = theme_minimal(),
  reverse = FALSE,
  ...
)

Arguments

Value

ggplot2::ggplot() object.

Examples

library(ggplot2)

task = tsk("lung")
task$head()

autoplot(task) # KM
autoplot(task) # KM of the censoring distribution
autoplot(task, rhs = "sex")
autoplot(task, type = "duo")

Description

Helper function to compose a survival distribution (or cumulative hazard) from the relative risk predictions (linear predictors, lp) of a proportional hazards model (e.g. a Cox-type model).

Usage

breslow(times, status, lp_train, lp_test, eval_times = NULL, type = "surv")

Arguments

Details

We estimate the survival probability of individual $i$ (from the test set), at time point $t$ as follows:

$S_i(t) = e^{-H_i(t)} = e^{-\hat{H}_0(t) \times e^{lp_i}}$

where:

• $H_i(t)$ is the cumulative hazard function for individual $i$

• $\hat{H}_0(t)$ is Breslow's estimator for the cumulative baseline hazard. Estimation requires the training set's times and status as well the risk predictions (lp_train).

• $lp_i$ is the risk prediction (linear predictor) of individual $i$ on the test set.

Breslow's approach uses a non-parametric maximum likelihood estimation of the cumulative baseline hazard function:

$\hat{H}_0(t) = \sum_{i=1}^n{\frac{I(T_i \le t)\delta_i} {\sum\nolimits_{j \in R_i}e^{lp_j}}}$

where:

• $t$ is the vector of time points (unique and sorted, from the train set)

• $n$ is number of events (train set)

• $T$ is the vector of event times (train set)

• $\delta$ is the status indicator (1 = event or 0 = censored)

• $R_i$ is the risk set (number of individuals at risk just before event $i$)

• $lp_j$ is the risk prediction (linear predictor) of individual $j$ (who is part of the risk set $R_i$) on the train set.

We employ constant interpolation to estimate the cumulative baseline hazards, extending from the observed unique event times to the specified evaluation times (eval_times). Any values falling outside the range of the estimated times are assigned as follows:

$\hat{H}_0(eval\_times < min(t)) = 0$

and

$\hat{H}_0(eval\_times > max(t)) = \hat{H}_0(max(t))$

Note that in the rare event of lp predictions being Inf or -Inf, the resulting cumulative hazard values become NaN, which we substitute with Inf (and corresponding survival probabilities take the value of $0$).

For similar implementations, see gbm::basehaz.gbm(), C060::basesurv() and xgboost.surv::sgb_bhaz().

Value

a matrix (obs x times). Number of columns is equal to eval_times and number of rows is equal to the number of test observations (i.e. the length of the lp_test vector). Depending on the type argument, the matrix can have either survival probabilities (0-1) or cumulative hazard estimates (0-Inf).

References

Breslow N (1972). “Discussion of 'Regression Models and Life-Tables' by D.R. Cox.” Journal of the Royal Statistical Society: Series B, 34(2), 216-217.

Lin, Y. D (2007). “On the Breslow estimator.” Lifetime Data Analysis, 13(4), 471-480. doi:10.1007/s10985-007-9048-y.

Examples

task = tsk("rats")
part = partition(task, ratio = 0.8)

learner = lrn("surv.coxph")
learner$train(task, part$train)
p_train = learner$predict(task, part$train)
p_test = learner$predict(task, part$test)

surv = breslow(times = task$times(part$train), status = task$status(part$train),
               lp_train = p_train$lp, lp_test = p_test$lp)
head(surv)

Description

gbcs dataset from Hosmer et al. (2008)

Usage

gbcs

Format

id

Identification Code

diagdate

Date of diagnosis.

recdate

Date of recurrence free survival.

deathdate

Date of death.

age

Age at diagnosis (years).

menopause

Menopausal status. 1 = Yes, 0 = No.

hormone

Hormone therapy. 1 = Yes. 0 = No.

size

Tumor size (mm).

grade

Tumor grade (1-3).

nodes

Number of lymph nodes.

prog_recp

Number of progesterone receptors.

estrg_recp

Number of estrogen receptors.

rectime

Time to recurrence (days).

censrec

Recurrence status. 1 = Recurrence. 0 = Censored.

survtime

Time to death (days).

censdead

Censoring status. 1 = Death. 0 = Censored.

Source

https://onlinelibrary.wiley.com/doi/book/10.1002/9780470258019

References

Hosmer, D.W. and Lemeshow, S. and May, S. (2008) Applied Survival Analysis: Regression Modeling of Time to Event Data: Second Edition, John Wiley and Sons Inc., New York, NY

Description

Many methods can be used to reduce a discrete survival distribution prediction (i.e. matrix) to a relative risk / ranking prediction, see Sonabend et al. (2022).

This function calculates a relative risk score as the sum of the predicted cumulative hazard function, also called ensemble/expected mortality. This risk score can be loosely interpreted as the expected number of deaths for patients with similar characteristics, see Ishwaran et al. (2008) and has no model or survival distribution assumptions.

Usage

get_mortality(x)

Arguments

Value

a numeric vector of the mortality risk scores, one per row of the input survival matrix.

References

Sonabend, Raphael, Bender, Andreas, Vollmer, Sebastian (2022). “Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.” Bioinformatics. ISSN 1367-4803, doi:10.1093/BIOINFORMATICS/BTAC451, https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac451/6640155.

Ishwaran, Hemant, Kogalur, B U, Blackstone, H E, Lauer, S M, others (2008). “Random survival forests.” The Annals of applied statistics, 2(3), 841–860.

Examples

n = 10 # number of observations
k = 50 # time points

# Create the matrix with random values between 0 and 1
mat = matrix(runif(n * k, min = 0, max = 1), nrow = n, ncol = k)

# transform it to a survival matrix
surv_mat = t(apply(mat, 1L, function(row) sort(row, decreasing = TRUE)))
colnames(surv_mat) = 1:k # time points

# get mortality scores (the larger, the more risk)
mort = get_mortality(surv_mat)
mort

Description

grace dataset from Hosmer et al. (2008)

Usage

grace

Format

id

Identification Code

days

Follow up time.

death

Censoring indicator. 1 = Death. 0 = Censored.

revasc

Revascularization Performed. 1 = Yes. 0 = No.

revascdays

Days to revascularization after admission.

los

Length of hospital stay (days).

age

Age at admission (years).

sysbp

Systolic blood pressure on admission (mm Hg).

stchange

ST-segment deviation on index ECG. 1 = Yes. 0 = No.

Source

https://onlinelibrary.wiley.com/doi/book/10.1002/9780470258019

References

Hosmer, D.W. and Lemeshow, S. and May, S. (2008) Applied Survival Analysis: Regression Modeling of Time to Event Data: Second Edition, John Wiley and Sons Inc., New York, NY

Description

This Learner specializes Learner for density estimation problems:

• task_type is set to "dens"

• Creates Predictions of class PredictionDens.

• Possible values for predict_types are:

  • "pdf": Evaluates estimated probability density function for each value in the test set.

  • "cdf": Evaluates estimated cumulative distribution function for each value in the test set.

Super class

mlr3::Learner -> LearnerDens

Methods

Public methods

• LearnerDens$new()

• LearnerDens$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDens$new(
  id,
  param_set = ps(),
  predict_types = "cdf",
  feature_types = character(),
  properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDens$clone(deep = FALSE)

Arguments

See Also

Other Learner: LearnerSurv

Examples

library(mlr3)
# get all density learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^dens"))
names(lrns)

# get a specific learner from mlr_learners:
mlr_learners$get("dens.hist")
lrn("dens.hist")

Description

This Learner specializes Learner for survival problems:

• task_type is set to "surv"

• Creates Predictions of class PredictionSurv.

• Possible values for predict_types are:

  • "distr": Predicts a probability distribution for each observation in the test set, uses distr6.

  • "lp": Predicts a linear predictor for each observation in the test set.

  • "crank": Predicts a continuous ranking for each observation in the test set.

  • "response": Predicts a survival time for each observation in the test set.

Super class

mlr3::Learner -> LearnerSurv

Methods

Public methods

• LearnerSurv$new()

• LearnerSurv$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurv$new(
  id,
  param_set = ps(),
  predict_types = "distr",
  feature_types = character(),
  properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurv$clone(deep = FALSE)

Arguments

See Also

Other Learner: LearnerDens

Examples

library(mlr3)
# get all survival learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^surv"))
names(lrns)

# get a specific learner from mlr_learners:
mlr_learners$get("surv.coxph")
lrn("surv.coxph")

Description

This measure specializes Measure for survival problems.

• task_type is set to "dens".

• Possible values for predict_type are "pdf" and "cdf".

Predefined measures can be found in the dictionary mlr3::mlr_measures.

Super class

mlr3::Measure -> MeasureDens

Methods

Public methods

• MeasureDens$new()

• MeasureDens$clone()

Method new()

Creates a new instance of this R6 class.

Usage

MeasureDens$new(
  id,
  param_set = ps(),
  range,
  minimize = NA,
  aggregator = NULL,
  properties = character(),
  predict_type = "pdf",
  task_properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureDens$clone(deep = FALSE)

Arguments

See Also

Default density measures: dens.logloss

Other Measure: MeasureSurv

Description

This measure specializes Measure for survival problems.

• task_type is set to "surv".

• Possible values for predict_type are "distr", "lp", "crank", and "response".

Predefined measures can be found in the dictionary mlr3::mlr_measures.

Super class

mlr3::Measure -> MeasureSurv

Methods

Public methods

• MeasureSurv$new()

• MeasureSurv$clone()

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurv$new(
  id,
  param_set = ps(),
  range,
  minimize = NA,
  average = "macro",
  aggregator = NULL,
  properties = character(),
  predict_type = "distr",
  predict_sets = "test",
  task_properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_,
  trafo = NULL
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSurv$clone(deep = FALSE)

Arguments

See Also

Default survival measure: surv.cindex

Other Measure: MeasureDens

Description

This is an abstract class that should not be constructed directly.

Parameter details

• integrated (logical(1))
  If TRUE (default), returns the integrated score (eg across time points); otherwise, not integrated (eg at a single time point).

• times (numeric())
  If integrated == TRUE then a vector of time-points over which to integrate the score. If integrated == FALSE then a single time point at which to return the score.

Super classes

mlr3::Measure -> mlr3proba::MeasureSurv -> MeasureSurvAUC

Methods

Public methods

• MeasureSurvAUC$new()

• MeasureSurvAUC$clone()

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurvAUC$new(
  id,
  properties = character(),
  label = NA_character_,
  man = NA_character_,
  param_set = ps()
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSurvAUC$clone(deep = FALSE)

Arguments

Description

Wrapper around PipeOpCrankCompositor to simplify Graph creation.

Usage

pipeline_crankcompositor(
  learner,
  method = c("mort"),
  overwrite = FALSE,
  graph_learner = FALSE
)

Arguments

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("crankcompositor")
ppl("crankcompositor")

See Also

Other pipelines: mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoclassif_disctime, mlr_graphs_survtoregr_pem

Examples

## Not run: 
  library(mlr3)
  library(mlr3pipelines)

  task = tsk("lung")
  part = partition(task)

  # change the crank prediction type of a Cox's model predictions
  grlrn = ppl(
    "crankcompositor",
    learner = lrn("surv.coxph"),
    method = "mort",
    overwrite = TRUE,
    graph_learner = TRUE
  )
  grlrn$train(task, part$train)
  grlrn$predict(task, part$test)

## End(Not run)

Description

Wrapper around PipeOpDistrCompositor or PipeOpBreslow to simplify Graph creation.

Usage

pipeline_distrcompositor(
  learner,
  estimator = "kaplan",
  form = "aft",
  overwrite = FALSE,
  scale_lp = FALSE,
  graph_learner = FALSE
)

Arguments

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("distrcompositor")
ppl("distrcompositor")

See Also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoclassif_disctime, mlr_graphs_survtoregr_pem

Examples

## Not run: 
  library(mlr3pipelines)

  # let's change the distribution prediction of Cox (Breslow-based) to an AFT form:
  task = tsk("rats")
  grlrn = ppl(
    "distrcompositor",
    learner = lrn("surv.coxph"),
    estimator = "kaplan",
    form = "aft",
    overwrite = TRUE,
    graph_learner = TRUE
  )
  grlrn$train(task)
  grlrn$predict(task)

## End(Not run)

Description

Wrapper around PipeOpProbregr to simplify Graph creation.

Usage

pipeline_probregr(
  learner,
  learner_se = NULL,
  dist = "Uniform",
  graph_learner = FALSE
)

Arguments

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("probregr")
ppl("probregr")

See Also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_distrcompositor, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoclassif_disctime, mlr_graphs_survtoregr_pem

Examples

## Not run: 
  library(mlr3)
  library(mlr3pipelines)

  task = tsk("boston_housing")

  # method 1 - same learner for response and se
  pipe = ppl(
    "probregr",
    learner = lrn("regr.featureless", predict_type = "se"),
    dist = "Uniform"
  )
  pipe$train(task)
  pipe$predict(task)

  # method 2 - different learners for response and se
  pipe = ppl(
    "probregr",
    learner = lrn("regr.rpart"),
    learner_se = lrn("regr.featureless", predict_type = "se"),
    dist = "Normal"
  )
  pipe$train(task)
  pipe$predict(task)

## End(Not run)

Description

Wrapper around PipeOpResponseCompositor to simplify Graph creation.

Usage

pipeline_responsecompositor(
  learner,
  method = "rmst",
  tau = NULL,
  add_crank = FALSE,
  overwrite = FALSE,
  graph_learner = FALSE
)

Arguments

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("responsecompositor")
ppl("responsecompositor")

See Also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoclassif_disctime, mlr_graphs_survtoregr_pem

Examples

## Not run: 
  library(mlr3)
  library(mlr3pipelines)

  task = tsk("lung")
  part = partition(task)

  # add survival time prediction type to the predictions of a Cox model
  grlrn = ppl(
    "responsecompositor",
    learner = lrn("surv.coxph"),
    method = "rmst",
    overwrite = TRUE,
    graph_learner = TRUE
  )
  grlrn$train(task, part$train)
  grlrn$predict(task, part$test)

## End(Not run)

Description

Wrapper around PipeOpSurvAvg to simplify Graph creation.

Usage

pipeline_survaverager(learners, param_vals = list(), graph_learner = FALSE)

Arguments

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("survaverager")
ppl("survaverager")

See Also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survbagging, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoclassif_disctime, mlr_graphs_survtoregr_pem

Examples

## Not run: 
  library(mlr3)
  library(mlr3pipelines)

  task = tsk("rats")
  pipe = ppl(
    "survaverager",
    learners = lrns(c("surv.kaplan", "surv.coxph")),
    param_vals = list(weights = c(0.1, 0.9)),
    graph_learner = FALSE
  )
  pipe$train(task)
  pipe$predict(task)

## End(Not run)

Description

Wrapper around PipeOpSubsample and PipeOpSurvAvg to simplify Graph creation.

Usage

pipeline_survbagging(
  learner,
  iterations = 10,
  frac = 0.7,
  avg = TRUE,
  weights = 1,
  graph_learner = FALSE
)

Arguments

Details

Bagging (Bootstrap AGGregatING) is the process of bootstrapping data and aggregating the final predictions. Bootstrapping splits the data into B smaller datasets of a given size and is performed with PipeOpSubsample. Aggregation is the sample mean of deterministic predictions and a MixtureDistribution of distribution predictions. This can be further enhanced by using a weighted average by supplying weights.

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("survbagging")
ppl("survbagging")

See Also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoclassif_disctime, mlr_graphs_survtoregr_pem

Examples

## Not run: 
  library(mlr3)
  library(mlr3pipelines)

  task = tsk("rats")
  pipe = ppl(
    "survbagging",
    learner = lrn("surv.coxph"),
    iterations = 5,
    graph_learner = FALSE
  )
  pipe$train(task)
  pipe$predict(task)

## End(Not run)

Description

Wrapper around PipeOpTaskSurvClassifDiscTime and PipeOpPredClassifSurvDiscTime to simplify Graph creation.

Usage

pipeline_survtoclassif_disctime(
  learner,
  cut = NULL,
  max_time = NULL,
  graph_learner = FALSE
)

Arguments

Details

The pipeline consists of the following steps:

1. PipeOpTaskSurvClassifDiscTime Converts TaskSurv to a TaskClassif.

2. A LearnerClassif is fit and predicted on the new TaskClassif.

3. PipeOpPredClassifSurvDiscTime transforms the resulting PredictionClassif to PredictionSurv.

4. Optionally: PipeOpModelMatrix is used to transform the formula of the task before fitting the learner.

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("survtoclassif_disctime")
ppl("survtoclassif_disctime")

References

Tutz, Gerhard, Schmid, Matthias (2016). Modeling Discrete Time-to-Event Data, series Springer Series in Statistics. Springer International Publishing. ISBN 978-3-319-28156-8 978-3-319-28158-2, http://link.springer.com/10.1007/978-3-319-28158-2.

See Also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoregr_pem

Examples

## Not run: 
  library(mlr3)
  library(mlr3learners)
  library(mlr3pipelines)

  task = tsk("lung")
  part = partition(task)

  grlrn = ppl(
    "survtoclassif_disctime",
    learner = lrn("classif.log_reg"),
    cut = 4, # 4 equidistant time intervals
    graph_learner = TRUE
  )
  grlrn$train(task, row_ids = part$train)
  grlrn$predict(task, row_ids = part$test)

## End(Not run)

Description

Wrapper around PipeOpTaskSurvClassifIPCW and PipeOpPredClassifSurvIPCW to simplify Graph creation.

Usage

pipeline_survtoclassif_IPCW(
  learner,
  tau = NULL,
  eps = 0.001,
  graph_learner = FALSE
)

Arguments

Details

The pipeline consists of the following steps:

1. PipeOpTaskSurvClassifIPCW Converts TaskSurv to a TaskClassif.

2. A LearnerClassif is fit and predicted on the new TaskClassif.

3. PipeOpPredClassifSurvIPCW transforms the resulting PredictionClassif to PredictionSurv.

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

Dictionary

This Graph can be instantiated via the dictionary mlr_graphs or with the associated sugar function ppl():

mlr_graphs$get("survtoclassif_IPCW")
ppl("survtoclassif_IPCW")

Additional alias id for pipeline construction:

ppl("survtoclassif_vock")

References

Vock, M D, Wolfson, Julian, Bandyopadhyay, Sunayan, Adomavicius, Gediminas, Johnson, E P, Vazquez-Benitez, Gabriela, O'Connor, J P (2016). “Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.” Journal of Biomedical Informatics, 61, 119–131. doi:10.1016/j.jbi.2016.03.009, https://www.sciencedirect.com/science/article/pii/S1532046416000496.

See Also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoclassif_disctime, mlr_graphs_survtoregr_pem

Examples

## Not run: 
  library(mlr3)
  library(mlr3learners)
  library(mlr3pipelines)

  task = tsk("lung")
  part = partition(task)

  grlrn = ppl(
    "survtoclassif_IPCW",
    learner = lrn("classif.rpart"),
    tau = 500, # Observations after 500 days are censored
    graph_learner = TRUE
  )
  grlrn$train(task, row_ids = part$train)
  pred = grlrn$predict(task, row_ids = part$test)
  pred # crank and distr at the cutoff time point included

  # score predictions
  pred$score() # C-index
  pred$score(msr("surv.brier", times = 500, integrated = FALSE)) # Brier score at tau

## End(Not run)

Description

Wrapper around multiple PipeOps to help in creation of complex survival reduction methods.

Usage

pipeline_survtoregr_pem(
  learner,
  cut = NULL,
  max_time = NULL,
  graph_learner = FALSE
)

Arguments

Details

A brief mathematical summary of PEMs (see referenced article for more detail):

1. PED Transformation: Survival data is converted into piece-wise exponential data (PED) format. Key elements are: Continuous time is divided into $j = 1, \ldots, J$ intervals for each subject, $i = 1, \ldots, n$. A status variable in each entry indicates whether an event or censoring occurred during that interval. For any subject, data entries are created only up until the interval including the event time. An offset column is introduced and represents the logarithm of the time a subject spent in any given interval. For more details, see pammtools::as_ped().

2. Hazard Estimation with PEM: The PED transformation combined with the working assumption

   $\delta_{ij} \stackrel{\text{iid}}{\sim} Poisson \left( \mu_{ij} = \lambda_{ij} t_{ij} \right),$

   where $\delta_{ij}$ denotes the event or censoring indicator, allows framing the problem of piecewise constant hazard estimation as a poisson regression with offset. Specifically, we want to estimate

   $\lambda(t \mid \mathbf{x}_i) := exp(g(x_{i},t_{j})), \quad \forall t \in [t_{j-1}, t_{j}), \quad i = 1, \dots, n.$

   $g(x_{i},t_{j})$ is a general function of features $x$ and $t$, i.e. a learner, and may include non-linearity and complex feature interactions. Two important prerequisites of the learner are its capacity to model a poisson likelihood and accommodate the offset.

3. From Piecewise Hazards to Survival Probabilities: Lastly, the computed hazards are back transformed to survival probabilities via the following identity

   $S(t | \mathbf{x}) = \exp \left( - \int_{0}^{t} \lambda(s | \mathbf{x}) \, ds \right) = \exp \left( - \sum_{j = 1}^{J} \lambda(j | \mathbf{x}) d_j\, \right),$

   where $d_j$ specifies the duration of interval $j$.

The previous considerations are reflected in the pipeline which consists of the following steps:

1. PipeOpTaskSurvRegrPEM Converts TaskSurv to a TaskRegr.

2. A LearnerRegr is fit and predicted on the new TaskRegr.

3. PipeOpPredRegrSurvPEM transforms the resulting PredictionRegr to PredictionSurv.

Value

mlr3pipelines::Graph or mlr3pipelines::GraphLearner

References

Bender, Andreas, Groll, Andreas, Scheipl, Fabian (2018). “A generalized additive model approach to time-to-event analysis.” Statistical Modelling, 18(3-4), 299–321. https://doi.org/10.1177/1471082X17748083.

See Also

Other pipelines: mlr_graphs_crankcompositor, mlr_graphs_distrcompositor, mlr_graphs_probregr, mlr_graphs_responsecompositor, mlr_graphs_survaverager, mlr_graphs_survbagging, mlr_graphs_survtoclassif_IPCW, mlr_graphs_survtoclassif_disctime

Examples

## Not run: 
  library(mlr3)
  library(mlr3learners)
  library(mlr3extralearners)
  library(mlr3pipelines)

  task = tsk("lung")
  part = partition(task)

  # typically model formula and features types are extracted from the task
  learner = lrn("regr.gam", family = "poisson")
  grlrn = ppl(
   "survtoregr_pem",
    learner = learner,
    graph_learner = TRUE
  )
  grlrn$train(task, row_ids = part$train)
  grlrn$predict(task, row_ids = part$test)

  # In some instances special formulas can be specified in the learner
  learner = lrn("regr.gam", family = "poisson", formula = pem_status ~ s(tend) + s(age) + meal.cal)
  grlrn = ppl(
   "survtoregr_pem",
    learner = learner,
    graph_learner = TRUE
  )
  grlrn$train(task, row_ids = part$train)
  grlrn$predict(task, row_ids = part$test)

  # if necessary encode data before passing to learner with e.g. po("encode"),
  # po("modelmatrix"), etc.
  # With po("modelmatrix") feature types and formula can be adjusted at the same time
  cut = round(seq(0, max(task$data()$time), length.out = 20))
  learner = as_learner(
    po("modelmatrix", formula = ~ as.factor(tend) + .) %>>%
    lrn("regr.glmnet", family = "poisson", lambda = 0)
  )
  grlrn = ppl(
    "survtoregr_pem",
    learner = learner,
    cut = cut,
    graph_learner = TRUE
  )
  grlrn$train(task, row_ids = part$train)
  grlrn$predict(task, row_ids = part$test)

  # xgboost regression learner
  learner = as_learner(
    po("modelmatrix", formula = ~ .) %>>%
    lrn("regr.xgboost", objective = "count:poisson", nrounds = 100, eta = 0.1)
  )

  grlrn = ppl(
    "survtoregr_pem",
    learner = learner,
    graph_learner = TRUE
  )
  grlrn$train(task, row_ids = part$train)
  grlrn$predict(task, row_ids = part$test)

## End(Not run)

Description

Calls graphics::hist() and the result is coerced to a distr6::Distribution.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerDensHistogram$new()
mlr_learners$get("dens.hist")
lrn("dens.hist")

Meta Information

• Type: "dens"

• Predict Types: ⁠pdf, cdf, distr⁠

• Feature Types: ⁠integer, numeric⁠

• Properties: -

• Packages: mlr3 mlr3proba distr6

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensHistogram

Methods

Public methods

• LearnerDensHistogram$new()

• LearnerDensHistogram$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensHistogram$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensHistogram$clone(deep = FALSE)

Arguments

See Also

Other density estimators: mlr_learners_dens.kde, mlr_learners_dens.kde_ks, mlr_learners_dens.locfit, mlr_learners_dens.logspline, mlr_learners_dens.mixed, mlr_learners_dens.nonpar, mlr_learners_dens.plug, mlr_learners_dens.spline

Examples

# Define the Learner
learner = lrn("dens.hist")
print(learner)

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Description

Calls kernels implemented in distr6 and the result is coerced to a distr6::Distribution.

Details

The default bandwidth uses Silverman's rule-of-thumb for Gaussian kernels, however for non-Gaussian kernels it is recommended to use mlr3tuning to tune the bandwidth with cross-validation. Other density learners can be used for automated bandwidth selection. The default kernel is Epanechnikov (chosen to reduce dependencies).

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerDensKDE$new()
mlr_learners$get("dens.kde")
lrn("dens.kde")

Meta Information

• Type: "dens"

• Predict Types: ⁠pdf, distr⁠

• Feature Types: ⁠integer, numeric⁠

• Properties: missings

• Packages: mlr3 mlr3proba distr6

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensKDE

Methods

Public methods

• LearnerDensKDE$new()

• LearnerDensKDE$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensKDE$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensKDE$clone(deep = FALSE)

Arguments

References

Silverman, W. B (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.

See Also

Other density estimators: mlr_learners_dens.hist, mlr_learners_dens.kde_ks, mlr_learners_dens.locfit, mlr_learners_dens.logspline, mlr_learners_dens.mixed, mlr_learners_dens.nonpar, mlr_learners_dens.plug, mlr_learners_dens.spline

Examples

# Define the Learner
learner = lrn("dens.kde")
print(learner)

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Description

Calls ks::kde() and the result is coerced to a distr6::Distribution.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerDensKDEks$new()
mlr_learners$get("dens.kde_ks")
lrn("dens.kde_ks")

Meta Information

• Type: "dens"

• Predict Types: pdf

• Feature Types: ⁠integer, numeric⁠

• Properties: -

• Packages: mlr3 mlr3proba ks distr6

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensKDEks

Methods

Public methods

• LearnerDensKDEks$new()

• LearnerDensKDEks$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensKDEks$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensKDEks$clone(deep = FALSE)

Arguments

References

Gramacki, Artur, Gramacki, Jarosław (2017). “FFT-based fast computation of multivariate kernel density estimators with unconstrained bandwidth matrices.” Journal of Computational and Graphical Statistics, 26(2), 459–462.

See Also

Other density estimators: mlr_learners_dens.hist, mlr_learners_dens.kde, mlr_learners_dens.locfit, mlr_learners_dens.logspline, mlr_learners_dens.mixed, mlr_learners_dens.nonpar, mlr_learners_dens.plug, mlr_learners_dens.spline

Examples

# Define the Learner
learner = lrn("dens.kde_ks")
print(learner)

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Description

Calls locfit::density.lf() and the result is coerced to a distr6::Distribution.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerDensLocfit$new()
mlr_learners$get("dens.locfit")
lrn("dens.locfit")

Meta Information

• Type: "dens"

• Predict Types: pdf

• Feature Types: ⁠integer, numeric⁠

• Properties: -

• Packages: mlr3 mlr3proba locfit distr6

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensLocfit

Methods

Public methods

• LearnerDensLocfit$new()

• LearnerDensLocfit$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensLocfit$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensLocfit$clone(deep = FALSE)

Arguments

References

Loader, Clive (2006). Local regression and likelihood. Springer Science & Business Media.

See Also

Other density estimators: mlr_learners_dens.hist, mlr_learners_dens.kde, mlr_learners_dens.kde_ks, mlr_learners_dens.logspline, mlr_learners_dens.mixed, mlr_learners_dens.nonpar, mlr_learners_dens.plug, mlr_learners_dens.spline

Examples

# Define the Learner
learner = lrn("dens.locfit")
print(learner)

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Description

Calls logspline::logspline() and the result is coerced to a distr6::Distribution.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerDensLogspline$new()
mlr_learners$get("dens.logspline")
lrn("dens.logspline")

Meta Information

• Type: "dens"

• Predict Types: ⁠pdf, cdf⁠

• Feature Types: ⁠integer, numeric⁠

• Properties: -

• Packages: mlr3 mlr3proba logspline distr6

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensLogspline

Methods

Public methods

• LearnerDensLogspline$new()

• LearnerDensLogspline$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensLogspline$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensLogspline$clone(deep = FALSE)

Arguments

References

Kooperberg, Charles, Stone, J C (1992). “Logspline density estimation for censored data.” Journal of Computational and Graphical Statistics, 1(4), 301–328.

See Also

Other density estimators: mlr_learners_dens.hist, mlr_learners_dens.kde, mlr_learners_dens.kde_ks, mlr_learners_dens.locfit, mlr_learners_dens.mixed, mlr_learners_dens.nonpar, mlr_learners_dens.plug, mlr_learners_dens.spline

Examples

# Define the Learner
learner = lrn("dens.logspline")
print(learner)

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Description

Calls np::npudens() and the result is coerced to a distr6::Distribution.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerDensMixed$new()
mlr_learners$get("dens.mixed")
lrn("dens.mixed")

Meta Information

• Type: "dens"

• Predict Types: pdf

• Feature Types: ⁠integer, numeric⁠

• Properties: -

• Packages: mlr3 mlr3proba np distr6

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensMixed

Methods

Public methods

• LearnerDensMixed$new()

• LearnerDensMixed$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensMixed$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensMixed$clone(deep = FALSE)

Arguments

References

Li, Qi, Racine, Jeff (2003). “Nonparametric estimation of distributions with categorical and continuous data.” journal of multivariate analysis, 86(2), 266–292.

See Also

Other density estimators: mlr_learners_dens.hist, mlr_learners_dens.kde, mlr_learners_dens.kde_ks, mlr_learners_dens.locfit, mlr_learners_dens.logspline, mlr_learners_dens.nonpar, mlr_learners_dens.plug, mlr_learners_dens.spline

Examples

# Define the Learner
learner = lrn("dens.mixed")
print(learner)

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Description

Calls sm::sm.density() and the result is coerced to a distr6::Distribution.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerDensNonparametric$new()
mlr_learners$get("dens.nonpar")
lrn("dens.nonpar")

Meta Information

• Type: "dens"

• Predict Types: pdf

• Feature Types: ⁠integer, numeric⁠

• Properties: weights

• Packages: mlr3 mlr3proba sm distr6

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensNonparametric

Methods

Public methods

• LearnerDensNonparametric$new()

• LearnerDensNonparametric$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensNonparametric$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensNonparametric$clone(deep = FALSE)

Arguments

References

Bowman, A.W., Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations, series Oxford Statistical Science Series. OUP Oxford. ISBN 9780191545696, https://books.google.de/books?id=7WBMrZ9umRYC.

See Also

Other density estimators: mlr_learners_dens.hist, mlr_learners_dens.kde, mlr_learners_dens.kde_ks, mlr_learners_dens.locfit, mlr_learners_dens.logspline, mlr_learners_dens.mixed, mlr_learners_dens.plug, mlr_learners_dens.spline

Examples

# Define the Learner
learner = lrn("dens.nonpar")
print(learner)

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Description

Calls plugdensity::plugin.density() and the result is coerced to a distr6::Distribution.

Details

Kernel density estimation by "plug-in" bandwidth selection.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerDensPlugin$new()
mlr_learners$get("dens.plug")
lrn("dens.plug")

Meta Information

• Type: "dens"

• Predict Types: pdf

• Feature Types: numeric

• Properties: missings

• Packages: mlr3 mlr3proba plugdensity distr6

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensPlugin

Methods

Public methods

• LearnerDensPlugin$new()

• LearnerDensPlugin$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensPlugin$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensPlugin$clone(deep = FALSE)

Arguments

References

Engel, Joachim, Herrmann, Eva, Gasser, Theo (1994). “An iterative bandwidth selector for kernel estimation of densities and their derivatives.” Journaltitle of Nonparametric Statistics, 4(1), 21–34.

See Also

Other density estimators: mlr_learners_dens.hist, mlr_learners_dens.kde, mlr_learners_dens.kde_ks, mlr_learners_dens.locfit, mlr_learners_dens.logspline, mlr_learners_dens.mixed, mlr_learners_dens.nonpar, mlr_learners_dens.spline

Examples

# Define the Learner
learner = lrn("dens.plug")
print(learner)

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Description

Calls gss::ssden() and the result is coerced to a distr6::Distribution.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerDensSpline$new()
mlr_learners$get("dens.spline")
lrn("dens.spline")

Meta Information

• Type: "dens"

• Predict Types: ⁠pdf, cdf⁠

• Feature Types: ⁠integer, numeric⁠

• Properties: missings

• Packages: mlr3 mlr3proba gss distr6

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensSpline

Methods

Public methods

• LearnerDensSpline$new()

• LearnerDensSpline$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensSpline$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensSpline$clone(deep = FALSE)

Arguments

References

Gu, Chong, Wang, Jingyuan (2003). “Penalized likelihood density estimation: Direct cross-validation and scalable approximation.” Statistica Sinica, 811–826.

See Also

Other density estimators: mlr_learners_dens.hist, mlr_learners_dens.kde, mlr_learners_dens.kde_ks, mlr_learners_dens.locfit, mlr_learners_dens.logspline, mlr_learners_dens.mixed, mlr_learners_dens.nonpar, mlr_learners_dens.plug

Examples

# Define the Learner
learner = lrn("dens.spline")
print(learner)

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Description

Calls survival::coxph().

• lp is predicted by survival::predict.coxph()

• distr is predicted by survival::survfit.coxph()

• crank is identical to lp

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerSurvCoxPH$new()
mlr_learners$get("surv.coxph")
lrn("surv.coxph")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”, “lp”

• Feature Types: “logical”, “integer”, “numeric”, “factor”

• Required Packages: mlr3, mlr3proba, survival, distr6

Parameters

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCoxPH

Methods

Public methods

• LearnerSurvCoxPH$new()

• LearnerSurvCoxPH$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvCoxPH$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCoxPH$clone(deep = FALSE)

Arguments

References

Cox DR (1972). “Regression Models and Life-Tables.” Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. doi:10.1111/j.2517-6161.1972.tb00899.x.

See Also

Other survival learners: mlr_learners_surv.kaplan, mlr_learners_surv.rpart

Description

Calls survival::survfit().

• distr is predicted by estimating the survival function with survival::survfit()

• crank is predicted as the sum of the cumulative hazard function (expected mortality) derived from the survival distribution, distr

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerSurvKaplan$new()
mlr_learners$get("surv.kaplan")
lrn("surv.kaplan")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

• Required Packages: mlr3, mlr3proba, survival, distr6

Parameters

Empty ParamSet

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvKaplan

Methods

Public methods

• LearnerSurvKaplan$new()

• LearnerSurvKaplan$importance()

• LearnerSurvKaplan$selected_features()

• LearnerSurvKaplan$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvKaplan$new()

Method importance()

All features have a score of 0 for this learner. This method exists solely for compatibility with the mlr3 ecosystem, as this learner is used as a fallback for other survival learners that require an importance() method.

Usage

LearnerSurvKaplan$importance()

Returns

Named numeric().

Method selected_features()

Selected features are always the empty set for this learner. This method is implemented only for compatibility with the mlr3 API, as this learner does not perform feature selection.

Usage

LearnerSurvKaplan$selected_features()

Returns

character(0).

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvKaplan$clone(deep = FALSE)

Arguments

References

Kaplan EL, Meier P (1958). “Nonparametric Estimation from Incomplete Observations.” Journal of the American Statistical Association, 53(282), 457–481. doi:10.1080/01621459.1958.10501452.

See Also

Other survival learners: mlr_learners_surv.coxph, mlr_learners_surv.rpart

Description

Calls rpart::rpart().

• crank is predicted using rpart::predict.rpart()

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerSurvRpart$new()
mlr_learners$get("surv.rpart")
lrn("surv.rpart")

Meta Information

• Task type: “surv”

• Predict Types: “crank”

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

• Required Packages: mlr3, mlr3proba, rpart, distr6, survival

Parameters

Initial parameter values

• xval is set to 0 in order to save some computation time.

• model has been renamed to keep_model.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvRpart

Methods

Public methods

• LearnerSurvRpart$new()

• LearnerSurvRpart$importance()

• LearnerSurvRpart$selected_features()

• LearnerSurvRpart$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvRpart$new()

Method importance()

The importance scores are extracted from the model slot variable.importance.

Usage

LearnerSurvRpart$importance()

Returns

Named numeric().

Method selected_features()

Selected features are extracted from the model slot frame$var.

Usage

LearnerSurvRpart$selected_features()

Returns

character().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvRpart$clone(deep = FALSE)

Arguments

References

Breiman L, Friedman JH, Olshen RA, Stone CJ (1984). Classification And Regression Trees. Routledge. doi:10.1201/9781315139470.

See Also

Other survival learners: mlr_learners_surv.coxph, mlr_learners_surv.kaplan

Description

Calculates the cross-entropy, or logarithmic (log), loss.

Details

The Log Loss, in the context of probabilistic predictions, is defined as the negative log probability density function, $f$, evaluated at the observed value, $y$,

$L(f, y) = -\log(f(y))$

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

MeasureDensLogloss$new()
mlr_measures$get("dens.logloss")
msr("dens.logloss")

Parameters

Meta Information

• Type: "density"

• Range: $[0, \infty)$

• Minimize: TRUE

• Required prediction: pdf

Parameter details

• eps (numeric(1))
  Very small number to substitute near-zero values in order to prevent errors in e.g. log(0) and/or division-by-zero calculations. Default value is 1e-15.

Super classes

mlr3::Measure -> mlr3proba::MeasureDens -> MeasureDensLogloss

Methods

Public methods

• MeasureDensLogloss$new()

• MeasureDensLogloss$clone()

Method new()

Creates a new instance of this R6 class.

Usage

MeasureDensLogloss$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureDensLogloss$clone(deep = FALSE)

Arguments

Description

Calculates the cross-entropy, or logarithmic (log), loss.

Details

The Log Loss, in the context of probabilistic predictions, is defined as the negative log probability density function, $f$, evaluated at the observed value, $y$,

$L(f, y) = -\log(f(y))$

Parameters

Meta Information

• Type: "regr"

• Range: $[0, \infty)$

• Minimize: TRUE

• Required prediction: distr

Parameter details

• eps (numeric(1))
  Very small number to substitute near-zero values in order to prevent errors in e.g. log(0) and/or division-by-zero calculations. Default value is 1e-15.

Super classes

mlr3::Measure -> mlr3::MeasureRegr -> MeasureRegrLogloss

Methods

Public methods

• MeasureRegrLogloss$new()

• MeasureRegrLogloss$clone()