Add functionality for early stopping rounds.

docs/src/index.md

@@ -127,6 +127,7 @@ Unlike feature data, label data can be extracted after construction of the `DMat
 [`XGBoost.getlabel`](@ref).
 
 
+
 ## Booster
 The [`Booster`](@ref) object holds model data.  They are created with training data.  Internally
 this is always a `DMatrix` but arguments will be automatically converted.
@@ -182,3 +183,43 @@ is equivalent to
 bst = xgboost((X, y), num_round=10)
 update!(bst, (X, y), num_round=10)
 ```
+
+### Early Stopping
+To help prevent overfitting to the training set, it is helpful to use a validation set to evaluate against to ensure that the XGBoost iterations continue to generalise outside training loss reduction. Early stopping provides a convenient way to automatically stop the
+boosting process if it's observed that the generalisation capability of the model does not improve for `k` rounds.
+
+If there is more than one element in watchlist, by default the last element will be used. In this case, you must use an ordered data structure (`OrderedDict`) compared to a standard unordered dictionary otherwise an exception will be generated. There will be
+a warning if you want to execute early stopping mechanism (`early_stopping_rounds > 0`) but have provided a watchlist with type `Dict` with
+more than 1 element.
+
+Similarly, if there is more than one element in eval_metric, by default the last element will be used.
+
+For example:
+
+```julia
+using LinearAlgebra
+using OrderedCollections
+
+𝒻(x) = 2norm(x)^2 - norm(x)
+
+X = randn(100,3)
+y = 𝒻.(eachrow(X))
+
+dtrain = DMatrix((X, y))
+
+X_valid = randn(50,3)
+y_valid = 𝒻.(eachrow(X_valid))
+
+dvalid = DMatrix((X_valid, y_valid))
+
+bst = xgboost(dtrain, num_round = 100, eval_metric = "rmse", watchlist = OrderedDict(["train" => dtrain, "eval" => dvalid]), early_stopping_rounds = 5, max_depth=6, η=0.3)
+
+# get the best iteration and use it for prediction
+ŷ = predict(bst, X_valid, ntree_limit = bst.best_iteration)
+
+using Statistics
+println("RMSE from model prediction $(round((mean((ŷ - y_valid).^2).^0.5), digits = 8)).")
+
+# we can also retain / use the best score (based on eval_metric) which is stored in the booster
+println("Best RMSE from model training $(round((bst.best_score), digits = 8)).")
+```

src/booster.jl

@@ -1,4 +1,3 @@
-
 """
     Booster
 
@@ -50,11 +49,17 @@ mutable struct Booster
     # out what the hell is happening, it's never used for program logic
     params::Dict{Symbol,Any}
 
-    function Booster(h::BoosterHandle, fsn::AbstractVector{<:AbstractString}=String[], params::AbstractDict=Dict())
+    # store early stopping information
+    best_iteration::Union{Int64, Missing}
+    best_score::Union{Float64, Missing}
+
+    function Booster(h::BoosterHandle, fsn::AbstractVector{<:AbstractString}=String[], params::AbstractDict=Dict(), best_iteration::Union{Int64, Missing}=missing, 
+        best_score::Union{Float64, Missing}=missing)
         finalizer(x -> xgbcall(XGBoosterFree, x.handle), new(h, fsn, params))
     end
 end
 
+
 """
     setparam!(b::Booster, name, val)
 
@@ -366,7 +371,6 @@ function updateone!(b::Booster, Xy::DMatrix;
                     update_feature_names::Bool=false,
                    )
     xgbcall(XGBoosterUpdateOneIter, b.handle, round_number, Xy.handle)
-    isempty(watchlist) || logeval(b, watchlist, round_number)
     _maybe_update_feature_names!(b, Xy, update_feature_names)
     b
 end
@@ -382,7 +386,6 @@ function updateone!(b::Booster, Xy::DMatrix, g::AbstractVector{<:Real}, h::Abstr
     g = convert(Vector{Cfloat}, g)
     h = convert(Vector{Cfloat}, h)
     xgbcall(XGBoosterBoostOneIter, b.handle, Xy.handle, g, h, length(g))
-    isempty(watchlist) || logeval(b, watchlist, round_number)
     _maybe_update_feature_names!(b, Xy, update_feature_names)
     b
 end
@@ -422,14 +425,105 @@ Run `num_round` rounds of gradient boosting on [`Booster`](@ref) `b`.
 The first and second derivatives of the loss function (`ℓ′` and `ℓ″` respectively) can be provided
 for custom loss.
 """
-function update!(b::Booster, data, a...; num_round::Integer=1, kw...)
+function update!(b::Booster, data, a...;
+                 num_round::Integer=1, 
+                 watchlist::Any = Dict("train" => data), 
+                 early_stopping_rounds::Integer=0,
+                 maximize=false,
+                 kw...,
+                 )
+
+    if !isempty(watchlist) && early_stopping_rounds > 0
+        @info("Will train until there has been no improvement in $early_stopping_rounds rounds.\n")
+        best_round = 0
+        best_score = maximize ? -Inf : Inf
+    end 
+
     for j ∈ 1:num_round
         round_number = getnrounds(b) + 1
-        updateone!(b, data, a...; round_number, kw...)
+
+        updateone!(b, data, a...; round_number, watchlist, kw...)
+
+        # Evaluate if watchlist is not empty
+        if !isempty(watchlist)
+            msg = evaliter(b, watchlist, round_number)
+            @info msg
+            if early_stopping_rounds > 0
+                score, dataset, metric = extract_metric_value(msg)
+                if (maximize && score > best_score || (!maximize && score < best_score))
+                    best_score = score
+                    best_round = j
+                elseif j - best_round >= early_stopping_rounds
+                    @info(
+                        "Xgboost: Stopping. \n\tBest iteration: $best_round. \n\tNo improvement in $dataset-$metric result in $early_stopping_rounds rounds."
+                    )
+                    # add additional fields to record the best iteration
+                    b.best_iteration = best_round
+                    b.best_score = best_score
+                    return b
+                end
+            end
+        end
     end
     b
 end
 
+
+
+"""
+    extract_metric_value(msg, dataset=nothing, metric=nothing)
+
+Extracts a numeric value from a message based on the specified dataset and metric.
+If dataset or metric is not provided, the function will automatically find the last
+mentioned dataset or metric in the message.
+
+# Arguments
+- `msg::AbstractString`: The message containing the numeric values.
+- `dataset::Union{AbstractString, Nothing}`: The dataset to extract values for (default: `nothing`).
+- `metric::Union{AbstractString, Nothing}`: The metric to extract values for (default: `nothing`).
+
+# Returns
+- Returns the parsed Float64 value if a match is found, otherwise returns `nothing`.
+
+# Examples
+```julia
+msg = "train-rmsle:0.09516384803222511 train-rmse:0.12458323318968342 eval-rmsle:0.09311178520817574 eval-rmse:0.12088154560829874"
+
+# Without specifying dataset and metric
+value_without_params = extract_metric_value(msg)
+println(value_without_params)  # Output: (0.09311178520817574, "eval", "rmsle")
+
+# With specifying dataset and metric
+value_with_params = extract_metric_value(msg, "train", "rmsle")
+println(value_with_params)  # Output: (0.0951638480322251, "train", "rmsle")
+"""
+
+function extract_metric_value(msg, dataset=nothing, metric=nothing)
+    if isnothing(dataset)
+            # Find the last mentioned dataset - whilst retaining order
+            datasets = unique([m.match for m in eachmatch(r"\w+(?=-)", msg)])
+            dataset = last(collect(datasets))
+    end
+
+    if isnothing(metric)
+            # Find the last mentioned metric - whilst retaining order
+            metrics = unique([m.match for m in eachmatch(r"(?<=-)\w+", msg)])
+            metric = last(collect(metrics))
+    end
+
+    pattern = Regex("$dataset-$metric:([\\d.]+)")
+
+    match_result = match(pattern, msg)
+
+    if match_result != nothing
+            parsed_value = parse(Float64, match_result.captures[1])
+            return parsed_value, dataset, metric
+    end
+
+    # there was no match result - should error out
+    error("No match found for pattern: $dataset-$metric in message: $msg")
+end
+
 """
     xgboost(data; num_round=10, watchlist=Dict(), kw...)
     xgboost(data, ℓ′, ℓ″; kw...)
@@ -439,7 +533,19 @@ This is essentially an alias for constructing a [`Booster`](@ref) with `data` an
 followed by [`update!`](@ref) for `nrounds`.
 
 `watchlist` is a dict the keys of which are strings giving the name of the data to watch
-and the values of which are [`DMatrix`](@ref) objects containing the data.
+and the values of which are [`DMatrix`](@ref) objects containing the data. It is mandatory to use an OrderedDict
+when utilising early_stopping_rounds and there is more than 1 element in watchlist to ensure XGBoost uses the 
+correct and intended dataset to perform early stop.
+
+`early_stopping_rounds` activates early stopping if set to > 0. Validation metric needs to improve at 
+least once in every k rounds. If `watchlist` is not explicitly provided, it will use the training dataset 
+to evaluate the stopping criterion. Otherwise, it will use the last data element in `watchlist` and the
+last metric in `eval_metric` (if more than one). Note that `watchlist` cannot be empty if 
+`early_stopping_rounds` is enabled.
+
+`maximize` If early_stopping_rounds is set, then this parameter must be set as well.
+When it is false, it means the smaller the evaluation score the better. When set to true,
+the larger the evaluation score the better.
 
 All other keyword arguments are passed to [`Booster`](@ref).  With few exceptions these are model
 training hyper-parameters, see [here](https://xgboost.readthedocs.io/en/stable/parameter.html) for
@@ -450,23 +556,51 @@ See [`updateone!`](@ref) for more details.
 
 ## Examples
 ```julia
+# Example 1: Basic usage of XGBoost
 (X, y) = (randn(100,3), randn(100))
 
-b = xgboost((X, y), 10, max_depth=10, η=0.1)
+b = xgboost((X, y), num_round=10, max_depth=10, η=0.1)
 
 ŷ = predict(b, X)
+
+# Example 2: Using early stopping (using a validation set) with a watchlist
+dtrain = DMatrix((randn(100,3), randn(100)))
+dvalid = DMatrix((randn(100,3), randn(100)))
+
+watchlist = OrderedDict(["train" => dtrain, "valid" => dvalid])
+
+b = xgboost(dtrain, num_round=10, early_stopping_rounds = 2, watchlist = watchlist, max_depth=10, η=0.1)
+
+# note that ntree_limit in the predict function helps assign the upper bound for iteration_range in the XGBoost API 1.4+
+ŷ = predict(b, dvalid, ntree_limit = b.best_iteration)
 ```
 """
 function xgboost(dm::DMatrix, a...;
-                 num_round::Integer=10,
-                 watchlist=Dict("train"=>dm),
-                 kw...
-                )
+                num_round::Integer=10,
+                watchlist::AbstractDict = Dict("train" => dm),
+                early_stopping_rounds::Integer=0, 
+                maximize=false,
+                kw...
+            )
+
     Xy = DMatrix(dm)
     b = Booster(Xy; kw...)
+
+    # We have a watchlist - give a warning if early stopping is provided and watchlist is a Dict type with length > 1
+    if isa(watchlist, Dict)
+        if early_stopping_rounds > 0 && length(watchlist) > 1
+            error("You must supply an OrderedDict type for watchlist if early stopping rounds is enabled and there is more than one element in watchlist.")
+        end
+    end
+
+    if isempty(watchlist) && early_stopping_rounds > 0
+        error("Watchlist must be supplied if early_stopping_rounds is enabled.")
+    end
+
     isempty(watchlist) || @info("XGBoost: starting training.")
-    update!(b, Xy, a...; num_round, watchlist)
+    update!(b, Xy, a...; num_round, watchlist, early_stopping_rounds, maximize)
     isempty(watchlist) || @info("Training rounds complete.")
     b
 end
+
 xgboost(data, a...; kw...) = xgboost(DMatrix(data), a...; kw...)