EvoTreeCount

EvoTreeCount(;kwargs...)

A model type for constructing a EvoTreeCount, based on EvoTrees.jl, and implementing both an internal API the MLJ model interface. EvoTreeCount is used to perform Poisson probabilistic regression on count target.

Hyper-parameters

nrounds=100: Number of rounds. It corresponds to the number of trees that will be sequentially stacked. Must be >= 1.
eta=0.1: Learning rate. Each tree raw predictions are scaled by eta prior to be added to the stack of predictions. Must be > 0. A lower eta results in slower learning, requiring a higher nrounds but typically improves model performance.
L2::T=0.0: L2 regularization factor on aggregate gain. Must be >= 0. Higher L2 can result in a more robust model.
lambda::T=0.0: L2 regularization factor on individual gain. Must be >= 0. Higher lambda can result in a more robust model.
gamma::T=0.0: Minimum gain imprvement needed to perform a node split. Higher gamma can result in a more robust model.
max_depth=6: Maximum depth of a tree. Must be >= 1. A tree of depth 1 is made of a single prediction leaf. A complete tree of depth N contains 2^(N - 1) terminal leaves and 2^(N - 1) - 1 split nodes. Compute cost is proportional to 2^max_depth. Typical optimal values are in the 3 to 9 range.
min_weight=1.0: Minimum weight needed in a node to perform a split. Matches the number of observations by default or the sum of weights as provided by the weights vector. Must be > 0.
rowsample=1.0: Proportion of rows that are sampled at each iteration to build the tree. Should be ]0, 1].
colsample=1.0: Proportion of columns / features that are sampled at each iteration to build the tree. Should be ]0, 1].
nbins=64: Number of bins into which each feature is quantized. Buckets are defined based on quantiles, hence resulting in equal weight bins. Should be between 2 and 255.
monotone_constraints=Dict{Int, Int}(): Specify monotonic constraints using a dict where the key is the feature index and the value the applicable constraint (-1=decreasing, 0=none, 1=increasing).
tree_type="binary" Tree structure to be used. One of:
- binary: Each node of a tree is grown independently. Tree are built depthwise until max depth is reach or if min weight or gain (see gamma) stops further node splits.
- oblivious: A common splitting condition is imposed to all nodes of a given depth.
rng=123: Either an integer used as a seed to the random number generator or an actual random number generator (::Random.AbstractRNG).

Internal API

Do config = EvoTreeCount() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in EvoTreeCount(max_depth=...).

Training model

A model is built using fit_evotree:

model = fit_evotree(config; x_train, y_train, kwargs...)

Inference

Predictions are obtained using predict which returns a Vector of length nobs:

EvoTrees.predict(model, X)

Alternatively, models act as a functor, returning predictions when called as a function with features as argument:

model(X)

MLJ

From MLJ, the type can be imported using:

EvoTreeCount = @load EvoTreeCount pkg=EvoTrees

Do model = EvoTreeCount() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in EvoTreeCount(loss=...).

Training data

In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where

X: any table of input features (eg, a DataFrame) whose columns each have one of the following element scitypes: Continuous, Count, or <:OrderedFactor; check column scitypes with schema(X)
y: is the target, which can be any AbstractVector whose element scitype is <:Count; check the scitype with scitype(y)

Train the machine using fit!(mach, rows=...).

Operations

predict(mach, Xnew): returns a vector of Poisson distributions given features Xnew having the same scitype as X above. Predictions are probabilistic.

Specific metrics can also be predicted using:

predict_mean(mach, Xnew)
predict_mode(mach, Xnew)
predict_median(mach, Xnew)

Fitted parameters

The fields of fitted_params(mach) are:

:fitresult: The GBTree object returned by EvoTrees.jl fitting algorithm.

Report

The fields of report(mach) are:

:features: The names of the features encountered in training.

Examples

## Internal API
using EvoTrees
config = EvoTreeCount(max_depth=5, nbins=32, nrounds=100)
nobs, nfeats = 1_000, 5
x_train, y_train = randn(nobs, nfeats), rand(0:2, nobs)
model = fit_evotree(config; x_train, y_train)
preds = EvoTrees.predict(model, x_train)

using MLJ
EvoTreeCount = @load EvoTreeCount pkg=EvoTrees
model = EvoTreeCount(max_depth=5, nbins=32, nrounds=100)
nobs, nfeats = 1_000, 5
X, y = randn(nobs, nfeats), rand(0:2, nobs)
mach = machine(model, X, y) |> fit!
preds = predict(mach, X)
preds = predict_mean(mach, X)
preds = predict_mode(mach, X)
preds = predict_median(mach, X)