Merge pull request #2 from alan-turing-institute/explore

rename learning_curve! -> learning_curve
JuliaAI · Jan 27, 2020 · dab955d · dab955d
2 parents 8df9e99 + 72523fa
commit dab955d
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diff --git a/README.md b/README.md
@@ -5,7 +5,7 @@ Hyperparameter optimization for
 learning models.
 
 [![Build Status](https://travis-ci.com/alan-turing-institute/MLJTuning.jl.svg?branch=master)](https://travis-ci.com/alan-turing-institute/MLJTuning.jl)
-[![Coverage](http://codecov.io/github/alan-turing-institute/MLJTuning.jl/coverage.svg?branch=master)](http://codecov.io/github/alan-turing-institute/MLJTuning.jl?branch=master)
+[![Coverage](http://coveralls.io/github/alan-turing-institute/MLJTuning.jl/coverage.svg?branch=master)](http://codecov.io/github/alan-turing-institute/MLJTuning.jl?branch=master)
 
 *Note:* This component of the [MLJ
   stack](https://github.com/alan-turing-institute/MLJ.jl#the-mlj-universe)
@@ -16,17 +16,17 @@ learning models.
 
 ## Who is this repo for?
 
-This repository is not intended for the general MLJ user but is:
-
-- a dependency of the
-  [MLJ](https://github.com/alan-turing-institute/MLJ.jl) machine
-  learning platform, allowing MLJ users to perform a variety of
-  hyperparameter optimization tasks
+This repository is not intended to be directly imported by the general
+MLJ user. Rather, MLJTuning is a dependency of the
+[MLJ](https://github.com/alan-turing-institute/MLJ.jl) machine
+learning platform, which allows MLJ users to perform a variety of
+hyperparameter optimization tasks from there.
 
-- a place for developers to integrate hyperparameter optimization
-  algorithms (here called *tuning strategies*) into MLJ, either
-  natively (by adding code to [/src/strategies](/src/strategies)) or
-  by importing and implementing an interface provided by this repo
+MLJTUning is the place for developers to integrate hyperparameter
+optimization algorithms (here called *tuning strategies*) into MLJ,
+either by adding code to [/src/strategies](/src/strategies), or by
+importing MLJTuning into a third-pary package and and implementing
+MLJTuning's interface.
 
 MLJTuning is a component of the [MLJ
   stack](https://github.com/alan-turing-institute/MLJ.jl#the-mlj-universe)

diff --git a/src/learning_curves.jl b/src/learning_curves.jl
@@ -1,17 +1,17 @@
 ## LEARNING CURVES
 
 """
-    curve = learning_curve!(mach; resolution=30,
-                                  resampling=Holdout(),
-                                  repeats=1,
-                                  measure=rms,
-                                  weights=nothing,
-                                  operation=predict,
-                                  range=nothing,
-                                  acceleration=default_resource(),
-                                  acceleration_grid=CPU1(),
-                                  rngs=nothing,
-                                  rng_name=nothing)
+    curve = learning_curve(mach; resolution=30,
+                                 resampling=Holdout(),
+                                 repeats=1,
+                                 measure=rms,
+                                 weights=nothing,
+                                 operation=predict,
+                                 range=nothing,
+                                 acceleration=default_resource(),
+                                 acceleration_grid=CPU1(),
+                                 rngs=nothing,
+                                 rng_name=nothing)
 
 Given a supervised machine `mach`, returns a named tuple of objects
 suitable for generating a plot of performance estimates, as a function
@@ -34,7 +34,7 @@ atom = @load RidgeRegressor pkg=MultivariateStats
 ensemble = EnsembleModel(atom=atom, n=1000)
 mach = machine(ensemble, X, y)
 r_lambda = range(ensemble, :(atom.lambda), lower=10, upper=500, scale=:log10)
-curve = learning_curve!(mach; range=r_lambda, resampling=CV(), measure=mav)
+curve = learning_curve(mach; range=r_lambda, resampling=CV(), measure=mav)
 using Plots
 plot(curve.parameter_values,
      curve.measurements,
@@ -52,15 +52,15 @@ of the parameter, ie the model is trained progressively.
 ```julia
 atom.lambda=200
 r_n = range(ensemble, :n, lower=1, upper=250)
-curves = learning_curve!(mach; range=r_n, verbosity=0, rng_name=:rng, rngs=3)
+curves = learning_curve(mach; range=r_n, verbosity=0, rng_name=:rng, rngs=3)
 plot!(curves.parameter_values,
      curves.measurements,
      xlab=curves.parameter_name,
      ylab="Holdout estimate of RMS error")
 ```
 
 """
-function learning_curve!(mach::Machine{<:Supervised};
+function learning_curve(mach::Machine{<:Supervised};
                          resolution=30,
                          resampling=Holdout(),
                          weights=nothing,
@@ -176,16 +176,18 @@ function _tuning_results(rngs::AbstractVector, acceleration::CPUProcesses,
     return ret
 end
 
+learning_curve!(machine::Machine, args...) =
+    learning_curve(machine, args...)
 
 """
     learning_curve(model::Supervised, args...; kwargs...)
 
 Plot a learning curve (or curves) without first constructing a
-machine. Equivalent to `learing_curve!(machine(model, args...);
+machine. Equivalent to `learing_curve(machine(model, args...);
 kwargs...)
 
-See [learning_curve!](@ref)
+See [learning_curve](@ref)
 
 """
 learning_curve(model::Supervised, args...; kwargs...) =
-    learning_curve!(machine(model, args...); kwargs...)
+    learning_curve(machine(model, args...); kwargs...)
diff --git a/test/learning_curves.jl b/test/learning_curves.jl
@@ -34,10 +34,10 @@ y = 2*x1 .+ 5*x2 .- 3*x3 .+ 0.2*rand(100);
     if accel == CPU1() && VERSION > v"1.2"
             curve = @test_logs((:info, r"No measure"),
                                (:info, r"Training"),
-                               learning_curve!(mach; range=r_lambda,
+                               learning_curve(mach; range=r_lambda,
                                                acceleration=accel))
     else
-        curve = learning_curve!(mach; range=r_lambda,
+        curve = learning_curve(mach; range=r_lambda,
                                 acceleration=accel)
     end
     @test curve isa NamedTuple{(:parameter_name,
@@ -48,7 +48,7 @@ y = 2*x1 .+ 5*x2 .- 3*x3 .+ 0.2*rand(100);
     atom.lambda=0.3
     r_n = range(ensemble, :n, lower=10, upper=100)
 
-    curves = learning_curve!(mach; range=r_n, resolution=7,
+    curves = learning_curve(mach; range=r_n, resolution=7,
                              acceleration=accel,
                              rngs = MersenneTwister.(1:3),
                              rng_name=:rng)
@@ -60,7 +60,7 @@ y = 2*x1 .+ 5*x2 .- 3*x3 .+ 0.2*rand(100);
     @test !(curves.measurements[1,1] ≈ curves.measurements[1,3])
 
     # reproducibility:
-    curves2 = learning_curve!(mach; range=r_n, resolution=7,
+    curves2 = learning_curve(mach; range=r_n, resolution=7,
                              acceleration=accel,
                              rngs = 3,
                              rng_name=:rng)