API Reference

Abstract type encompassing all tree-based learners.

Abstract type encompassing all tree-based learners with classification leaves.

Abstract type encompassing all tree-based learners with regression leaves.

Abstract type encompassing all tree-based learners with survival leaves.

Abstract type encompassing all tree-based learners with prescription leaves.

Abstract type encompassing all tree-based learners with policy leaves.

Abstract type encompassing all multi-task tree-based learners.

Abstract type encompassing all multi-task tree-based learners with classification leaves.

Abstract type encompassing all multi-task tree-based learners with regression leaves.

get_num_nodes(lnr::TreeLearner)

Return the number of nodes in the trained lnr.

Example

IAI.get_num_nodes(lnr)

7

is_leaf(lnr::TreeLearner, node_index::Int)

Return true if node node_index in the trained lnr is a leaf.

Example

IAI.is_leaf(lnr, 1)

false

get_depth(lnr::TreeLearner, node_index::Int)

Return the depth of node node_index in the trained lnr.

Example

IAI.get_depth(lnr, 6)

2

get_num_samples(lnr::TreeLearner, node_index::Int)

Return the number of training points contained in node node_index in the trained lnr.

Example

IAI.get_num_samples(lnr, 6)

78

get_parent(lnr::TreeLearner, node_index::Int)

Return the index of the parent of node node_index in the trained lnr.

Example

IAI.get_parent(lnr, 2)

1

get_lower_child(lnr::TreeLearner, node_index::Int)

Return the index of the lower child of node node_index in the trained lnr.

Example

IAI.get_lower_child(lnr, 1)

2

get_upper_child(lnr::TreeLearner, node_index::Int)

Return the index of the upper child of node node_index in the trained lnr.

Example

IAI.get_upper_child(lnr, 1)

5

is_parallel_split(lnr::TreeLearner, node_index::Int)

Return true if node node_index in the trained lnr is a parallel split.

Example

IAI.is_parallel_split(lnr, 1)

true

is_hyperplane_split(lnr::TreeLearner, node_index::Int)

Return true if node node_index in the trained lnr is a hyperplane split.

Example

IAI.is_hyperplane_split(lnr, 2)

true

is_categoric_split(lnr::TreeLearner, node_index::Int)

Return true if node node_index in the trained lnr is a categoric split.

Example

IAI.is_categoric_split(lnr, 5)

true

is_ordinal_split(lnr::TreeLearner, node_index::Int)

Return true if node node_index in the trained lnr is an ordinal split.

Example

IAI.is_ordinal_split(lnr, 1)

false

is_mixed_parallel_split(lnr::TreeLearner, node_index::Int)

Return true if node node_index in the trained lnr is a mixed categoric/parallel split.

Example

IAI.is_mixed_parallel_split(lnr, 2)

false

is_mixed_ordinal_split(lnr::TreeLearner, node_index::Int)

Return true if node node_index in the trained lnr is a mixed categoric/ordinal split.

Example

IAI.is_mixed_ordinal_split(lnr, 5)

false

missing_goes_lower(lnr::TreeLearner, node_index::Int)

Return true if missing values take the lower branch at node node_index in the trained lnr.

Applies to non-leaf nodes.

Example

IAI.missing_goes_lower(lnr, 1)

false

get_split_feature(lnr::TreeLearner, node_index::Int)

Return the feature used in the split at node node_index in the trained lnr.

Applies to categoric, ordinal, parallel, categoric/ordinal, and categoric/parallel splits.

Example

IAI.get_split_feature(lnr, 1)

:score1

get_split_threshold(lnr::TreeLearner, node_index::Int)

Return the threshold used in the split at node node_index in the trained lnr.

Applies to hyperplane, parallel, and categoric/parallel splits.

Example

IAI.get_split_threshold(lnr, 1)

60.04421

get_split_categories(lnr::TreeLearner, node_index::Int)

Return a Dict containing the categoric/ordinal information used in the split at node node_index in the trained lnr, where the keys are the levels used in the split and the values are true if that level follows the lower branch and false if that level follows the upper branch.

Applies to categoric, ordinal, categoric/ordinal, and categoric/parallel splits.

Example

IAI.get_split_categories(lnr, 5)

Dict{Any, Bool} with 5 entries:
  "B" => 1
  "A" => 1
  "C" => 0
  "D" => 0
  "E" => 0

get_split_weights(lnr::TreeLearner, node_index::Int)

Return two Dicts containing the weights for numeric and categoric features, respectively, used in the hyperplane split at node node_index in the trained lnr.

The numeric Dict has key-value pairs of feature names and their corresponding weights in the hyperplane split.

The categoric Dict has key-value pairs of feature names and a corresponding Dict that maps the categoric levels for that feature to their weights in the hyperplane.

Any features not included in either Dict has zero weight in the hyperplane, and similarly, any categoric levels that are not included have zero weight.

Applies to hyperplane splits.

Example

numeric_weights, categoric_weights = IAI.get_split_weights(lnr, 2)
numeric_weights

Dict{Symbol, Float64} with 2 entries:
  :score3 => 1.20415
  :score2 => 0.0189015

categoric_weights

Dict{Symbol, Dict{Any, Float64}} with 1 entry:
  :region => Dict("E"=>1.47922)

get_classification_label(lnr::ClassificationTreeLearner, node_index::Int;
                         check_leaf::Bool=true)

Return the predicted label at node node_index in the trained lnr.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

IAI.get_classification_label(lnr, 2)

"setosa"

get_classification_proba(lnr::ClassificationTreeLearner, node_index::Int;
                         check_leaf::Bool=true)

Return the predicted probabilities of class membership at node node_index in the trained lnr.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

IAI.get_classification_proba(lnr, 4)

Dict{String, Float64} with 3 entries:
  "virginica"  => 0.0925926
  "setosa"     => 0.0
  "versicolor" => 0.907407

get_regression_constant(lnr::ClassificationTreeLearner, node_index::Int;
                        check_leaf::Bool=true)

Return the constant term in the logistic regression prediction at node node_index in the trained lnr, or NaN if the node does not contain a logistic regression model.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

get_regression_weights(lnr::ClassificationTreeLearner, node_index::Int;
                       check_leaf::Bool=true)

Return the weights for each feature in the logistic regression prediction at node node_index in the trained lnr. The weights are returned as two Dicts in the same format as described for get_split_weights.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

get_regression_constant(lnr::RegressionTreeLearner, node_index::Int;
                        check_leaf::Bool=true)

Return the constant term in the regression prediction at node node_index in the trained lnr.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

IAI.get_regression_constant(lnr, 2)

30.88

IAI.get_regression_constant(lnr, 3)

30.8876

get_regression_weights(lnr::RegressionTreeLearner, node_index::Int;
                       check_leaf::Bool=true)

Return the weights for each feature in the regression prediction at node node_index in the trained lnr. The weights are returned as two Dicts in the same format as described for get_split_weights.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

numeric_weights, categoric_weights = IAI.get_regression_weights(lnr, 3)
numeric_weights

Dict{Symbol, Float64} with 4 entries:
  :Cyl  => -0.794566
  :WT   => -1.64974
  :Gear => 0.0585196
  :HP   => -0.0126672

categoric_weights

Dict{Symbol, Dict{Any, Float64}}()

get_survival_curve(lnr::SurvivalTreeLearner, node_index::Int;
                   check_leaf::Bool=true)

Return the SurvivalCurve fitted at node node_index in the trained lnr.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

IAI.get_survival_curve(lnr, 2)

SurvivalCurve with 22 breakpoints

get_survival_expected_time(lnr::SurvivalTreeLearner, node_index::Int;
                           check_leaf::Bool=true)

Return the predicted expected survival time at node node_index in the trained lnr.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

IAI.get_survival_expected_time(lnr, 2)

23443.187287749995

get_survival_hazard(lnr::SurvivalTreeLearner, node_index::Int;
                    check_leaf::Bool=true)

Return the predicted hazard ratio at node node_index in the trained lnr.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

IAI.get_survival_hazard(lnr, 2)

0.8880508

get_regression_constant(lnr::SurvivalTreeLearner, node_index::Int;
                        check_leaf::Bool=true)

Return the constant term in the Cox regression prediction at node node_index in the trained lnr, or NaN if the node does not contain a Cox regression model.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

get_regression_weights(lnr::SurvivalTreeLearner, node_index::Int;
                       check_leaf::Bool=true)

Return the weights for each feature in the Cox regression prediction at node node_index in the trained lnr. The weights are returned as two Dicts in the same format as described for get_split_weights.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

get_prescription_treatment_rank(lnr::PrescriptionTreeLearner,
                                node_index::Int; check_leaf::Bool=true)

Return a Vector containing the treatments ordered from most effective to least effective at node node_index in the trained lnr.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

IAI.get_prescription_treatment_rank(lnr, 2)

2-element Vector{String}:
 "A"
 "B"

get_regression_constant(lnr::PrescriptionTreeLearner, node_index::Int,
                       treatment::Any; check_leaf::Bool=true)

Return the constant in the regression prediction for treatment at node node_index in the trained lnr.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

IAI.get_regression_constant(lnr, 2, "A")

28.68282

get_regression_weights(lnr::PrescriptionTreeLearner, node_index::Int,
                       treatment::Any; check_leaf::Bool=true)

Return the weights for each feature in the regression prediction for treatment at node node_index in the trained lnr. The weights are returned as two Dicts in the same format as described for get_split_weights.

Applies to leaf nodes by default, set check_leaf=false to enable retrieving the same information from a split node as though it was a leaf node.

Example

numeric_weights, categoric_weights = IAI.get_regression_weights(lnr, 2, "A")
numeric_weights

Dict{Symbol, Float64} with 1 entry:
  :SystolicBP => -1.37769

categoric_weights

Dict{Symbol, Dict{Any, Float64}}()