remove pdf_smoothing option #11

Author: ablaom

src/MLJDecisionTreeInterface.jl

@@ -38,7 +38,6 @@ MMI.@mlj_model mutable struct DecisionTreeClassifier <: MMI.Probabilistic
     n_subfeatures::Int           = 0::(_ ≥ -1)
     post_prune::Bool             = false
     merge_purity_threshold::Float64 = 1.0::(_ ≤ 1)
-    pdf_smoothing::Float64       = 0.0::(0 ≤ _ ≤ 1)
     display_depth::Int           = 5::(_ ≥ 1)
     rng::Union{AbstractRNG,Integer} = GLOBAL_RNG
 end
@@ -103,10 +102,9 @@ function MMI.predict(m::DecisionTreeClassifier, fitresult, Xnew)
     tree, classes_seen, integers_seen = fitresult
     # retrieve the predicted scores
     scores = DT.apply_tree_proba(tree, Xmatrix, integers_seen)
-    # smooth if required
-    sm_scores = smooth(scores, m.pdf_smoothing)
+
     # return vector of UF
-    return MMI.UnivariateFinite(classes_seen, sm_scores)
+    return MMI.UnivariateFinite(classes_seen, scores)
 end
 
 
@@ -120,7 +118,6 @@ MMI.@mlj_model mutable struct RandomForestClassifier <: MMI.Probabilistic
     n_subfeatures::Int           = (-)(1)::(_ ≥ -1)
     n_trees::Int                 = 10::(_ ≥ 2)
     sampling_fraction::Float64   = 0.7::(0 < _ ≤ 1)
-    pdf_smoothing::Float64       = 0.0::(0 ≤ _ ≤ 1)
     rng::Union{AbstractRNG,Integer} = GLOBAL_RNG
 end
 
@@ -151,16 +148,14 @@ function MMI.predict(m::RandomForestClassifier, fitresult, Xnew)
     Xmatrix = MMI.matrix(Xnew)
     forest, classes_seen, integers_seen = fitresult
     scores = DT.apply_forest_proba(forest, Xmatrix, integers_seen)
-    sm_scores = smooth(scores, m.pdf_smoothing)
-    return MMI.UnivariateFinite(classes_seen, sm_scores)
+    return MMI.UnivariateFinite(classes_seen, scores)
 end
 
 
 # # ADA BOOST STUMP CLASSIFIER
 
 MMI.@mlj_model mutable struct AdaBoostStumpClassifier <: MMI.Probabilistic
     n_iter::Int            = 10::(_ ≥ 1)
-    pdf_smoothing::Float64 = 0.0::(0 ≤ _ ≤ 1)
 end
 
 function MMI.fit(m::AdaBoostStumpClassifier, verbosity::Int, X, y)
@@ -185,8 +180,7 @@ function MMI.predict(m::AdaBoostStumpClassifier, fitresult, Xnew)
     stumps, coefs, classes_seen, integers_seen = fitresult
     scores = DT.apply_adaboost_stumps_proba(stumps, coefs,
                                             Xmatrix, integers_seen)
-    sm_scores = smooth(scores, m.pdf_smoothing)
-    return MMI.UnivariateFinite(classes_seen, sm_scores)
+    return MMI.UnivariateFinite(classes_seen, scores)
 end
 
 
@@ -239,7 +233,6 @@ MMI.@mlj_model mutable struct RandomForestRegressor <: MMI.Deterministic
     n_subfeatures::Int           = (-)(1)::(_ ≥ -1)
     n_trees::Int                 = 10::(_ ≥ 2)
     sampling_fraction::Float64   = 0.7::(0 < _ ≤ 1)
-    pdf_smoothing::Float64       = 0.0::(0 ≤ _ ≤ 1)
     rng::Union{AbstractRNG,Integer} = GLOBAL_RNG
 end
 
@@ -375,14 +368,6 @@ Train the machine using `fit!(mach, rows=...)`.
 
 - `rng=Random.GLOBAL_RNG`: random number generator or seed
 
-- `pdf_smoothing=0.0`: threshold for smoothing the predicted scores.
-  Raw leaf-based probabilities are smoothed as follows: If `n` is the
-  number of observed classes, then each class probability is replaced
-  by `pdf_smoothing/n`, if it falls below that ratio, and the
-  resulting vector of probabilities is renormalized. Smoothing is only
-  applied to classes actually observed in training. Unseen classes
-  retain zero-probability predictions.
-
 
 # Operations
 
@@ -512,9 +497,6 @@ Train the machine with `fit!(mach, rows=...)`.
 
 - `rng=Random.GLOBAL_RNG`: random number generator or seed
 
-- `pdf_smoothing=0.0`: threshold for smoothing the predicted scores of
-  each tree.  See [`DecisionTreeClassifier`](@ref)
-
 
 # Operations
 
@@ -586,9 +568,6 @@ Train the machine with `fit!(mach, rows=...)`.
 
 - `n_iter=10`:   number of iterations of AdaBoost
 
-- `pdf_smoothing=0.0`: threshold for smoothing the predicted scores.
-  See [`DecisionTreeClassifier`](@ref)
-
 
 # Operations
 

test/runtests.jl

@@ -83,7 +83,7 @@ rgs = DecisionTreeRegressor()
 fitresult, _, _ = MLJBase.fit(rgs, 1, X, ycont)
 @test rms(predict(rgs, fitresult, X), ycont) < 1.5
 
-clf = DecisionTreeClassifier(pdf_smoothing=0)
+clf = DecisionTreeClassifier()
 fitresult, _, _ = MLJBase.fit(clf, 1, X, yfinite)
 @test sum(predict(clf, fitresult, X) .== yfinite) == 0 # perfect prediction