Merge pull request #17 from JuliaAI/dev

For a 0.2.0 release

Author: ablaom

.github/workflows/ci.yml

@@ -3,9 +3,11 @@ on:
   pull_request:
     branches:
       - master
+      - dev
   push:
     branches:
       - master
+      - dev
     tags: '*'
 jobs:
   test:

Project.toml

@@ -1,16 +1,18 @@
 name = "MLJDecisionTreeInterface"
 uuid = "c6f25543-311c-4c74-83dc-3ea6d1015661"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.1.4"
+version = "0.2.0"
 
 [deps]
 DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
 DecisionTree = "0.10"
 MLJModelInterface = "^0.3,^0.4, 1.0"
+Tables = "1.6"
 julia = "1.6"
 
 [extras]

src/MLJDecisionTreeInterface.jl

@@ -3,6 +3,7 @@ module MLJDecisionTreeInterface
 import MLJModelInterface
 using MLJModelInterface.ScientificTypesBase
 import DecisionTree
+import Tables
 
 using Random
 import Random.GLOBAL_RNG
@@ -37,15 +38,21 @@ 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
 
 function MMI.fit(m::DecisionTreeClassifier, verbosity::Int, X, y)
+    schema = Tables.schema(X)
     Xmatrix = MMI.matrix(X)
     yplain  = MMI.int(y)
 
+    if schema === nothing
+        features = [Symbol("x$j") for j in 1:size(Xmatrix, 2)]
+    else
+        features = schema.names |> collect
+    end
+
     classes_seen  = filter(in(unique(y)), MMI.classes(y[1]))
     integers_seen = MMI.int(classes_seen)
 
@@ -61,11 +68,12 @@ function MMI.fit(m::DecisionTreeClassifier, verbosity::Int, X, y)
     end
     verbosity < 2 || DT.print_tree(tree, m.display_depth)
 
-    fitresult = (tree, classes_seen, integers_seen)
+    fitresult = (tree, classes_seen, integers_seen, features)
 
     cache  = nothing
     report = (classes_seen=classes_seen,
-              print_tree=TreePrinter(tree))
+              print_tree=TreePrinter(tree),
+              features=features)
 
     return fitresult, cache, report
 end
@@ -76,7 +84,9 @@ function get_encoding(classes_seen)
 end
 
 MMI.fitted_params(::DecisionTreeClassifier, fitresult) =
-    (tree=fitresult[1], encoding=get_encoding(fitresult[2]))
+    (tree=fitresult[1],
+     encoding=get_encoding(fitresult[2]),
+     features=fitresult[4])
 
 function smooth(scores, smoothing)
     iszero(smoothing) && return scores
@@ -92,10 +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
 
 
@@ -109,7 +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
 
@@ -140,16 +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)
@@ -174,8 +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
 
 
@@ -228,7 +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
 
@@ -364,14 +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
 
@@ -394,6 +390,9 @@ The fields of `fitted_params(mach)` are:
   of tree (obtained by calling `fit!(mach, verbosity=2)` or from
   report - see below)
 
+- `features`: the names of the features encountered in training, in an
+  order consistent with the output of `print_tree` (see below)
+
 
 # Report
 
@@ -405,6 +404,9 @@ The fields of `report(mach)` are:
   tree, with single argument the tree depth; interpretation requires
   internal integer-class encoding (see "Fitted parameters" above).
 
+- `features`: the names of the features encountered in training, in an
+  order consistent with the output of `print_tree` (see below)
+
 
 # Examples
 
@@ -495,9 +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
 
@@ -569,9 +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

@@ -37,11 +37,15 @@ yhat = MLJBase.predict(baretree, fitresult, X);
 yyhat = predict_mode(baretree, fitresult, MLJBase.selectrows(X, 1:3))
 @test MLJBase.classes(yyhat[1]) == MLJBase.classes(y[1])
 
+# check report and fitresult fields:
+@test Set([:classes_seen, :print_tree, :features]) == Set(keys(report))
+@test Set(report.classes_seen) == Set(levels(y))
+@test report.print_tree(2) === nothing # :-(
+@test report.features == [:sepal_length, :sepal_width, :petal_length, :petal_width]
+fp = fitted_params(baretree, fitresult)
+@test Set([:tree, :encoding, :features]) == Set(keys(fp))
+@test fp.features == report.features
 
-# # testing machine interface:
-# tree = machine(baretree, X, y)
-# fit!(tree)
-# yyhat = predict_mode(tree, MLJBase.selectrows(X, 1:3))
 using Random: seed!
 seed!(0)
 
@@ -79,7 +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