fixes

docs/src/models.md

@@ -1,14 +1,3 @@
-[EvoTrees.jl](https://github.com/Evovest/EvoTrees.jl) supports four model families: 
-
-- EvoTreeRegressor
-    - Linear (minimize mean-squared error)
-    - Logistic (minimize cross-entropy)
-    - L1: minimize mean-absolute error
-    - Quantile: minimize mean-absolute off the quantile
-- EvoTreeClassifier
-- EvoTreeCount
-- EvoTreeGaussian
-
 ## EvoTreeRegressor
 ```@docs
 EvoTreeRegressor

experiments/readme_plots_cpu.jl

@@ -29,8 +29,8 @@ Y_train, Y_eval = Y[𝑖_train], Y[𝑖_eval]
 # linear
 params1 = EvoTreeRegressor(T=Float64,
     loss=:linear, metric=:mse,
-    nrounds=100, nbins=64,
-    lambda=0.1, gamma=0.1, eta=0.1,
+    nrounds=200, nbins=64,
+    lambda=0.1, gamma=0.1, eta=0.05,
     max_depth=6, min_weight=1.0,
     rowsample=0.5, colsample=1.0,
     rng=123)
@@ -52,8 +52,8 @@ sqrt(mean((pred_train_linear .- Y_train) .^ 2))
 # linear weighted
 params1 = EvoTreeRegressor(T=Float64,
     loss=:linear, metric=:mse,
-    nrounds=100, nbins=64,
-    λ=0.1, γ=0.1, η=1.0,
+    nrounds=200, nbins=64,
+    lambda=0.1, gamma=0.1, eta=0.05,
     max_depth=6, min_weight=1.0,
     rowsample=0.5, colsample=1.0,
     rng=123)
@@ -107,35 +107,48 @@ plot(X_train, Y_train, msize=1, mcolor="gray", mswidth=0, background_color=RGB(1
 plot!(X_train[:, 1][x_perm], pred_train_linear[x_perm], color="navy", linewidth=1.5, label="Linear")
 plot!(X_train[:, 1][x_perm], pred_train_linear_w[x_perm], color="lightblue", linewidth=1.5, label="LinearW")
 plot!(X_train[:, 1][x_perm], pred_train_logistic[x_perm], color="darkred", linewidth=1.5, label="Logistic")
-plot!(X_train[:, 1][x_perm], pred_train_poisson[x_perm], color="green", linewidth=1.5, label="Poisson")
 plot!(X_train[:, 1][x_perm], pred_train_L1[x_perm], color="pink", linewidth=1.5, label="L1")
 savefig("figures/regression_sinus.png")
 
-
-
 # Poisson
 params1 = EvoTreeCount(
     loss=:poisson, metric=:poisson,
     nrounds=200, nbins=64,
-    lambda=0.1, gamma=0.1, eta=0.05,
+    lambda=0.5, gamma=0.1, eta=0.1,
     max_depth=6, min_weight=1.0,
     rowsample=0.5, colsample=1.0)
 @time model = fit_evotree(params1, X_train, Y_train, X_eval=X_eval, Y_eval=Y_eval, print_every_n=25);
-# @btime model = grow_gbtree($X_train, $Y_train, $params1, X_eval = $X_eval, Y_eval = $Y_eval)
 @time pred_train_poisson = predict(model, X_train);
-@time pred_eval_poisson = predict(model, X_eval)
 sqrt(mean((pred_train_poisson .- Y_train) .^ 2))
 
+# Gamma
+params1 = EvoTreeRegressor(
+    loss=:gamma, metric=:gamma,
+    nrounds=200, nbins=64,
+    lambda=0.5, gamma=0.1, eta=0.1,
+    max_depth=6, min_weight=1.0,
+    rowsample=0.5, colsample=1.0)
+@time model = fit_evotree(params1, X_train, Y_train, X_eval=X_eval, Y_eval=Y_eval, print_every_n=25);
+@time pred_train_gamma = predict(model, X_train);
+sqrt(mean((pred_train_gamma .- Y_train) .^ 2))
+
+# Tweedie
+params1 = EvoTreeRegressor(
+    loss=:tweedie, metric=:tweedie,
+    nrounds=200, nbins=64,
+    lambda=0.5, gamma=0.1, eta=0.1,
+    max_depth=6, min_weight=1.0,
+    rowsample=0.5, colsample=1.0)
+@time model = fit_evotree(params1, X_train, Y_train, X_eval=X_eval, Y_eval=Y_eval, print_every_n=25);
+@time pred_train_tweedie = predict(model, X_train);
+sqrt(mean((pred_train_tweedie .- Y_train) .^ 2))
 
 x_perm = sortperm(X_train[:, 1])
 plot(X_train, Y_train, msize=1, mcolor="gray", mswidth=0, background_color=RGB(1, 1, 1), seriestype=:scatter, xaxis=("feature"), yaxis=("target"), legend=true, label="")
-plot!(X_train[:, 1][x_perm], pred_train_linear[x_perm], color="navy", linewidth=1.5, label="Linear")
-plot!(X_train[:, 1][x_perm], pred_train_linear_w[x_perm], color="lightblue", linewidth=1.5, label="LinearW")
-plot!(X_train[:, 1][x_perm], pred_train_logistic[x_perm], color="darkred", linewidth=1.5, label="Logistic")
-plot!(X_train[:, 1][x_perm], pred_train_poisson[x_perm], color="green", linewidth=1.5, label="Poisson")
-plot!(X_train[:, 1][x_perm], pred_train_L1[x_perm], color="pink", linewidth=1.5, label="L1")
-savefig("figures/regression_sinus.png")
-
+plot!(X_train[:, 1][x_perm], pred_train_poisson[x_perm], color="navy", linewidth=1.5, label="Poisson")
+plot!(X_train[:, 1][x_perm], pred_train_gamma[x_perm], color="lightblue", linewidth=1.5, label="Gamma")
+plot!(X_train[:, 1][x_perm], pred_train_tweedie[x_perm], color="darkred", linewidth=1.5, label="Tweedie")
+savefig("figures/regression_sinus2.png")
 
 
 ###############################

src/models.jl

@@ -53,7 +53,7 @@ function EvoTreeRegressor(; kwargs...)
         :nbins => 32,
         :alpha => 0.5,
         :monotone_constraints => Dict{Int,Int}(),
-        :metric => :mse,
+        :metric => :none,
         :rng => 123,
         :device => "cpu"
     )
@@ -145,7 +145,7 @@ function EvoTreeCount(; kwargs...)
         :nbins => 32,
         :alpha => 0.5,
         :monotone_constraints => Dict{Int,Int}(),
-        :metric => :mse,
+        :metric => :none,
         :rng => 123,
         :device => "cpu"
     )
@@ -169,6 +169,10 @@ function EvoTreeCount(; kwargs...)
         args[:loss] = Poisson()
     end
 
+    if args[:metric] == :poisson
+        @warn "Poisson metric breaking change.\nStarting with EvoTrees.jl v0.11.0, `:poisson` metric now returns the deviance, while it previously returned the log-likelihood."
+    end
+
     args[:rng] = mk_rng(args[:rng])::Random.AbstractRNG
 
     model = EvoTreeCount(
@@ -224,7 +228,7 @@ function EvoTreeClassifier(; kwargs...)
         :colsample => 1.0,
         :nbins => 32,
         :alpha => 0.5,
-        :metric => :mse,
+        :metric => :none,
         :rng => 123,
         :device => "cpu"
     )
@@ -304,7 +308,7 @@ function EvoTreeGaussian(; kwargs...)
         :nbins => 32,
         :alpha => 0.5,
         :monotone_constraints => Dict{Int,Int}(),
-        :metric => :mse,
+        :metric => :none,
         :rng => 123,
         :device => "cpu"
     )