change default verbosity to LearnAPI.default_verbosity()

Author: ablaom

src/learners/classification.jl

@@ -44,7 +44,12 @@ LearnAPI.features(learner::ConstantClassifier, data) =
 LearnAPI.target(learner::ConstantClassifier, data) =
     LearnAPI.target(learner, data, front_end)
 
-function LearnAPI.fit(learner::ConstantClassifier, observations::FrontEnds.Obs; verbosity=1)
+function LearnAPI.fit(
+    learner::ConstantClassifier,
+    observations::FrontEnds.Obs;
+    verbosity=LearnAPI.default_verbosity(),
+    )
+
     y = observations.target # integer "codes"
     names = observations.names
     classes_seen = observations.classes_seen

src/learners/dimension_reduction.jl

@@ -65,7 +65,11 @@ LearnAPI.obs(model::TruncatedSVDFitted, data) =
 LearnAPI.features(learner::TruncatedSVD, data) =
     LearnAPI.features(learner, data, FrontEnds.Tarragon())
 
-function LearnAPI.fit(learner::TruncatedSVD, observations::FrontEnds.Obs; verbosity=1)
+function LearnAPI.fit(
+    learner::TruncatedSVD,
+    observations::FrontEnds.Obs;
+    verbosity=LearnAPI.default_verbosity(),
+    )
 
     # unpack hyperparameters:
     codim = learner.codim

src/learners/ensembling.jl

@@ -69,7 +69,7 @@ LearnAPI.obs(model::EnsembleFitted, data) = LearnAPI.obs(first(model.models), da
 LearnAPI.target(learner::Ensemble, data) = LearnAPI.target(learner.atom, data)
 LearnAPI.features(learner::Ensemble, data) = LearnAPI.features(learner.atom, data)
 
-function LearnAPI.fit(learner::Ensemble, data; verbosity=1)
+function LearnAPI.fit(learner::Ensemble, data; verbosity=LearnAPI.default_verbosity())
 
     # unpack hyperparameters:
     atom = learner.atom
@@ -112,7 +112,7 @@ function LearnAPI.update(
     model::EnsembleFitted,
     data,
     replacements::Pair{Symbol}...;
-    verbosity=1,
+    verbosity=LearnAPI.default_verbosity(),
     )
     learner_old = LearnAPI.learner(model)
     learner = LearnAPI.clone(learner_old, replacements...)
@@ -361,7 +361,7 @@ struct StumpRegressorFitted
     rng
 end
 
-function LearnAPI.fit(learner::StumpRegressor, data; verbosity=1)
+function LearnAPI.fit(learner::StumpRegressor, data; verbosity=LearnAPI.default_verbosity())
 
     x, y = data
     rng = deepcopy(learner.rng)
@@ -426,7 +426,7 @@ function LearnAPI.update(
     model::StumpRegressorFitted,
     data, # ignored as cached
     replacements::Pair{Symbol}...;
-    verbosity=1,
+    verbosity=LearnAPI.default_verbosity(),
     )
 
     learner_old = LearnAPI.learner(model)

src/learners/gradient_descent.jl

@@ -111,7 +111,7 @@ point predictions with `predict(model, Point(), Xnew)`.
 
 # Warm restart options
 
-    update(model, newdata, :epochs=>n, other_replacements...; verbosity=1)
+    update(model, newdata, :epochs=>n, other_replacements...)
 
 If `Δepochs = n - perceptron.epochs` is non-negative, then return an updated model, with
 the weights and bias of the previously learned perceptron used as the starting state in
@@ -120,7 +120,7 @@ instead of the previous training data. Any other hyperparaameter `replacements`
 adopted. If `Δepochs` is negative or not specified, instead return `fit(learner,
 newdata)`, where `learner=LearnAPI.clone(learner; epochs=n, replacements....)`.
 
-    update_observations(model, newdata, replacements...; verbosity=1)
+    update_observations(model, newdata, replacements...)
 
 Return an updated model, with the weights and bias of the previously learned perceptron
 used as the starting state in new gradient descent updates. Adopt any specified
@@ -197,7 +197,7 @@ LearnAPI.learner(model::PerceptronClassifierFitted) = model.learner
 function LearnAPI.fit(
     learner::PerceptronClassifier,
     observations::PerceptronClassifierObs;
-    verbosity=1,
+    verbosity=LearnAPI.default_verbosity(),
     )
 
     # unpack hyperparameters:
@@ -233,7 +233,7 @@ function LearnAPI.update_observations(
     model::PerceptronClassifierFitted,
     observations_new::PerceptronClassifierObs,
     replacements...;
-    verbosity=1,
+    verbosity=LearnAPI.default_verbosity(),
     )
 
     # unpack data:
@@ -265,7 +265,7 @@ function LearnAPI.update(
     model::PerceptronClassifierFitted,
     observations::PerceptronClassifierObs,
     replacements...;
-    verbosity=1,
+    verbosity=LearnAPI.default_verbosity(),
     )
 
     # unpack data:

src/learners/incremental_algorithms.jl

@@ -48,15 +48,19 @@ end
 
 LearnAPI.learner(::NormalEstimatorFitted) = NormalEstimator()
 
-function LearnAPI.fit(::NormalEstimator, y; verbosity=1)
+function LearnAPI.fit(::NormalEstimator, y; verbosity=LearnAPI.default_verbosity())
     n = length(y)
     Σy = sum(y)
     ȳ = Σy/n
     ss = sum(x->x^2, y) - n*ȳ^2
     return NormalEstimatorFitted(Σy, ȳ, ss, n)
 end
 
-function LearnAPI.update_observations(model::NormalEstimatorFitted, ynew; verbosity=1)
+function LearnAPI.update_observations(
+    model::NormalEstimatorFitted,
+    ynew;
+    verbosity=LearnAPI.default_verbosity(),
+    )
     m = length(ynew)
     n = model.n + m
     Σynew = sum(ynew)

src/learners/regression.jl

@@ -46,7 +46,11 @@ LearnAPI.obs(model::RidgeFitted, data) = obs(model, data, frontend)
 LearnAPI.features(learner::Ridge, data) = LearnAPI.features(learner, data, frontend)
 LearnAPI.target(learner::Ridge, data) = LearnAPI.target(learner, data, frontend)
 
-function LearnAPI.fit(learner::Ridge, observations::FrontEnds.Obs; verbosity=1)
+function LearnAPI.fit(
+    learner::Ridge,
+    observations::FrontEnds.Obs;
+    verbosity=LearnAPI.default_verbosity(),
+    )
 
     # unpack hyperparameters and data:
     lambda = learner.lambda
@@ -130,7 +134,7 @@ struct BabyRidgeFitted{T,F}
     feature_importances::F
 end
 
-function LearnAPI.fit(learner::BabyRidge, data; verbosity=1)
+function LearnAPI.fit(learner::BabyRidge, data; verbosity=LearnAPI.default_verbosity())
 
     X, y = data
 

src/learners/static_algorithms.jl

@@ -36,7 +36,7 @@ Selector(; names=Symbol[]) =  Selector(names) # LearnAPI.constructor defined lat
 
 # `fit` consumes no observational data, does no "learning", and just returns a thinly
 # wrapped `learner` (to distinguish it from the learner in dispatch):
-LearnAPI.fit(learner::Selector; verbosity=1) = Ref(learner)
+LearnAPI.fit(learner::Selector; verbosity=LearnAPI.default_verbosity()) = Ref(learner)
 LearnAPI.learner(model::Base.RefValue{Selector}) = model[]
 
 function LearnAPI.transform(model::Base.RefValue{Selector}, X)
@@ -107,7 +107,8 @@ LearnAPI.learner(model::FancySelectorFitted) = model.learner
 rejected(model::FancySelectorFitted) = model.rejected
 
 # Here we are wrapping `learner` with a place-holder for the `rejected` feature names.
-LearnAPI.fit(learner::FancySelector; verbosity=1) = FancySelectorFitted(learner)
+LearnAPI.fit(learner::FancySelector; verbosity=LearnAPI.default_verbosity()) =
+    FancySelectorFitted(learner)
 
 # output the filtered table and add `rejected` field to model (mutatated!)
 function LearnAPI.transform(model::FancySelectorFitted, X)

test/learners/incremental_algorithms.jl

@@ -10,7 +10,7 @@ y = rand(rng, 50);
 ynew = rand(rng, 10);
 
 learner = LearnTestAPI.NormalEstimator()
-@testapi learner y verbosity=1
+@testapi learner y verbosity=0
 
 @testset "NormalEstimator" begin
     model = fit(learner, y)