Package 'randomPlantedForest'

Extract predicted components from a Random Planted Forest

Description

Prediction components are a functional decomposition of the model prediction. The sum of all components equals the overall predicted value for an observation.

Usage

predict_components(object, new_data, max_interaction = NULL, predictors = NULL)

Arguments

object	A fit object of class rpf.
new_data	Data for new observations to predict.
max_interaction	integer or NULL: Maximum degree of interactions to consider. Default will use the max_interaction parameter from the rpf object. Must be between 1 (main effects only) and the max_interaction of the rpf object.
predictors	character or NULL: Vector of one or more column names of predictor variables in new_data to extract components for. If NULL, all variables and their interactions are returned.

Details

Extracts all possible components up to max_interaction degrees, up to the value set when calling rpf(). The intercept is always included. Optionally predictors can be specified to only include components including the given variables. If max_interaction is greater than length(predictors), the max_interaction will be lowered accordingly.

Value

A list with elements:

• m (data.table): Components for each main effect and interaction term, representing the functional decomposition of the prediction. All components together with the intercept sum up to the prediction. For multiclass classification, the number of output columns is multiplied by the number of levels in the outcome.

• intercept (numeric(1)): Expected value of the prediction.

• x (data.table): Copy of new_data containing predictors selected by predictors.

• target_levels (character): For multiclass classification only: Vector of target levels which can be used to disassemble m, as names include both term and target level.

Note

Depending on the number of predictors and max_interaction, the number of components will increase drastically to sum(choose(ncol(new_data), seq_len(max_interaction))).

Examples

# Regression task, only some predictors
train <-  mtcars[1:20, 1:4]
test <-  mtcars[21:32, 1:4]

set.seed(23)
rpfit <- rpf(mpg ~ ., data = train, max_interaction = 3, ntrees = 30)

# Extract all components, including main effects and interaction terms up to `max_interaction`
(components <- predict_components(rpfit, test))

# sums to prediction
cbind(
  m_sum = rowSums(components$m) + components$intercept,
  prediction = predict(rpfit, test)
)

# Only get components with interactions of a lower degree, ignoring 3-way interactions
predict_components(rpfit, test, max_interaction = 2)

# Only retrieve main effects
(main_effects <- predict_components(rpfit, test, max_interaction = 1))

# The difference is the combined contribution of interaction effects
cbind(
  m_sum = rowSums(main_effects$m) + main_effects$intercept,
  prediction = predict(rpfit, test)
)

---

Random Planted Forest Predictions

Description

Random Planted Forest Predictions

Usage

## S3 method for class 'rpf'
predict(
  object,
  new_data,
  type = ifelse(object$mode == "regression", "numeric", "prob"),
  ...
)

Arguments

object	A fit object of class rpf.
new_data	Data for new observations to predict.
type	"numeric" for regression outcomes, "class" for class predictions or "prob" for probability predictions. For classification and loss = "L1" or "L2", "numeric" yields raw predictions which are not guaranteed to be valid probabilities in ⁠[0, 1]⁠. For type = "prob", these are truncated to ensure this property. If loss is "logit" or "exponential", type = "link" is an alias for type = "numeric", as in this case the raw predictions have the additional interpretation similar to the linear predictor in a glm.
...	Unused.

Value

For regression: A tbl with column .pred with the same number of rows as new_data.

For classification: A tbl with one column for each level in y containing class probabilities if type = "prob". For type = "class", one column .pred with class predictions is returned. For type = "numeric" or "link", one column .pred with raw predictions.

Examples

# Regression with L2 loss
rpfit <- rpf(y = mtcars$mpg, x = mtcars[, c("cyl", "wt")])
predict(rpfit, mtcars[, c("cyl", "wt")])

---

Print an rpf fit

Description

Print an rpf fit

Usage

## S3 method for class 'rpf'
print(x, ...)

Arguments

x	And object of class rpf.
...	Further arguments passed to or from other methods.

Value

Invisibly: x.

See Also

rpf.

Examples

rpf(mpg ~ cyl + wt + drat, data = mtcars, max_interaction = 2, ntrees = 10)

---

Compact printing of forest structures

Description

These methods are provided to avoid flooding the console with long nested lists containing tree structures. Note

Usage

## S3 method for class 'rpf_forest'
print(x, ...)

## S3 method for class 'rpf_forest'
str(object, ...)

Arguments

x	Object of class rpf_forest
...	Further arguments passed to or from other methods.
object	Object of class rpf_forest

See Also

rpf

Examples

rpfit <- rpf(mpg ~ cyl + wt, data = mtcars, ntrees = 10)
print(rpfit$forest)
str(rpfit$forest)

---

Purify a Random Planted Forest

Description

TODO: Explain what this does

Usage

purify(x, ...)

## Default S3 method:
purify(x, ...)

## S3 method for class 'rpf'
purify(x, ...)

is_purified(x)

Arguments

x	And object of class rpf.
...	(Unused)

Details

Unless rpf() is called with purify = TRUE, the forest has to be purified after fit to ensure the components extracted by predict_components() are valid. predict_components() will automatically purify a forest if is_purified() reports FALSE.

Value

Invisibly: The rpf object.

Examples

rpfit <- rpf(mpg ~., data = mtcars, max_interaction = 2, ntrees = 10)
purify(rpfit)

---

Random Planted Forest

Description

Random Planted Forest

Usage

rpf(x, ...)

## S3 method for class 'data.frame'
rpf(
  x,
  y,
  max_interaction = 1,
  ntrees = 50,
  splits = 30,
  split_try = 10,
  t_try = 0.4,
  deterministic = FALSE,
  nthreads = 1,
  purify = FALSE,
  cv = FALSE,
  loss = "L2",
  delta = 0,
  epsilon = 0.1,
  ...
)

## S3 method for class 'matrix'
rpf(
  x,
  y,
  max_interaction = 1,
  ntrees = 50,
  splits = 30,
  split_try = 10,
  t_try = 0.4,
  deterministic = FALSE,
  nthreads = 1,
  purify = FALSE,
  cv = FALSE,
  loss = "L2",
  delta = 0,
  epsilon = 0.1,
  ...
)

## S3 method for class 'formula'
rpf(
  formula,
  data,
  max_interaction = 1,
  ntrees = 50,
  splits = 30,
  split_try = 10,
  t_try = 0.4,
  deterministic = FALSE,
  nthreads = 1,
  purify = FALSE,
  cv = FALSE,
  loss = "L2",
  delta = 0,
  epsilon = 0.1,
  ...
)

## S3 method for class 'recipe'
rpf(
  x,
  data,
  max_interaction = 1,
  ntrees = 50,
  splits = 30,
  split_try = 10,
  t_try = 0.4,
  deterministic = FALSE,
  nthreads = 1,
  purify = FALSE,
  cv = FALSE,
  loss = "L2",
  delta = 0,
  epsilon = 0.1,
  ...
)

Arguments

x, data	Feature matrix, or data.frame, or recipe.
...	(Unused).
y	Target vector for use with x. The class of y (either numeric or factor) determines if regression or classification will be performed.
max_interaction	⁠[1]⁠: Maximum level of interaction determining maximum number of split dimensions for a tree. The default 1 corresponds to main effects only. If 0, the number fo columns in x is used, i.e. for 10 predictors, this is equivalent to setting max_interaction = 10.
ntrees	⁠[50]⁠: Number of trees generated per family.
splits	⁠[30]⁠: Number of splits performed for each tree family.
split_try	⁠[10]⁠: Number of split points to be considered when choosing a split candidate.
t_try	⁠[0.4]⁠: A value in (0,1] specifying the proportion of viable split-candidates in each round.
deterministic	⁠[FALSE]⁠: Choose whether approach deterministic or random.
nthreads	⁠[1L]⁠: Number of threads used for computation, defaulting to serial execution.
purify	⁠[FALSE]⁠: Whether the forest should be purified. Set to TRUE to enable components extract with predict_components() are valid. Can be achieved after fitting with purify().
cv	⁠[FALSE]⁠: Determines if cross validation is performed.
loss	⁠["L2"]⁠: For regression, only "L2" is supported. For classification, "L1", "logit" and "exponential" are also available. "exponential" yields similar results as "logit" while being significantly faster.
delta	⁠[0]⁠: Only used if loss is "logit" or "exponential". Proportion of class membership is truncated to be smaller 1-delta when calculating the loss to determine the optimal split.
epsilon	⁠[0.1]⁠: Only used if loss = "logit" or "exponential". Proportion of class membership is truncated to be smaller 1-epsilon when calculating the fit in a leaf.
formula	Formula specification, e.g. y ~ x1 + x2.

Value

Object of class "rpf" with model object contained in ⁠$fit⁠.

Examples

# Regression with x and y
rpfit <- rpf(x = mtcars[, c("cyl", "wt")], y = mtcars$mpg)

# Regression with formula
rpfit <- rpf(mpg ~ cyl + wt, data = mtcars)

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