Merge bb7b762 into b55a06f

Project.toml

@@ -10,10 +10,12 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJModels = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
+Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

docs/src/internals.md

@@ -41,7 +41,7 @@ function fit!(machine::Machine; rows=nothing, force=false, verbosity=1)
     warning = clean!(mach.model)
     isempty(warning) || verbosity < 0 || @warn warning 
 
-    if rows == nothing
+    if rows === nothing
         rows = (:) 
     end
 
@@ -61,7 +61,7 @@ function fit!(machine::Machine; rows=nothing, force=false, verbosity=1)
         mach.rows = deepcopy(rows)
     end
 
-    if report != nothing
+    if report !== nothing
         merge!(mach.report, report)
     end
 

src/MLJ.jl

@@ -1,61 +1,60 @@
 module MLJ
 
 # defined in include files:
-export @curve, @pcurve                               # utilities.jl
-export mav, rms, rmsl, rmslp1, rmsp                  # metrics.jl
-export misclassification_rate, cross_entropy         # metrics.jl
-export default_measure                               # metrics.jl
-export coerce, supervised, unsupervised              # tasks.jl
-export report                                        # machines.jl
-export Holdout, CV, evaluate!, Resampler             # resampling.jl
-export Params, params, set_params!                   # parameters.jl
-export strange, iterator                             # parameters.jl
-export Grid, TunedModel, learning_curve!             # tuning.jl
-export EnsembleModel                                 # ensembles.jl
-export ConstantRegressor, ConstantClassifier         # builtins/Constant.jl
-export models, localmodels, @load                    # loading.jl
-export KNNRegressor                                  # builtins/KNN.jl
-export @from_network, machines, sources              # composites.jl
+export @curve, @pcurve,                               # utilities.jl
+        mav, mae, rms, rmsl, rmslp1, rmsp,            # metrics.jl
+        misclassification_rate, cross_entropy,        # metrics.jl
+        default_measure,                              # metrics.jl
+        coerce, supervised, unsupervised,             # tasks.jl
+        report,                                       # machines.jl
+        Holdout, CV, evaluate!, Resampler,            # resampling.jl
+        Params, params, set_params!,                  # parameters.jl
+        strange, iterator,                            # parameters.jl
+        Grid, TunedModel, learning_curve!,            # tuning.jl
+        EnsembleModel,                                # ensembles.jl
+        ConstantRegressor, ConstantClassifier,        # builtins/Constant.jl
+        models, localmodels, @load,                   # loading.jl
+        KNNRegressor,                                 # builtins/KNN.jl
+        @from_network, machines, sources              # composites.jl
 
 # defined in include files "machines.jl and "networks.jl":
-export Machine, NodalMachine, machine, AbstractNode
-export source, node, fit!, freeze!, thaw!, Node, sources, origins
+export Machine, NodalMachine, machine, AbstractNode,
+        source, node, fit!, freeze!, thaw!, Node, sources, origins
 
 # defined in include file "builtins/Transformers.jl":
-export FeatureSelector
-export UnivariateStandardizer, Standardizer
-export UnivariateBoxCoxTransformer
-export OneHotEncoder
-# export IntegerToInt64Transformer
-# export UnivariateDiscretizer, Discretizer
+export FeatureSelector,
+        UnivariateStandardizer, Standardizer,
+        UnivariateBoxCoxTransformer,
+        OneHotEncoder
+        # IntegerToInt64Transformer,
+        # UnivariateDiscretizer, Discretizer
 
 # rexport from Random, Statistics, Distributions, CategoricalArrays:
 export pdf, mode, median, mean, shuffle!, categorical, shuffle
 
 # reexport from MLJBase:
-export nrows, nfeatures, info
-export SupervisedTask, UnsupervisedTask, MLJTask
-export Deterministic, Probabilistic, Unsupervised, Supervised
-export DeterministicNetwork, ProbabilisticNetwork
-export Found, Continuous, Finite, Infinite    
-export OrderedFactor, Unknown
-export Count, Multiclass, Binary
-export scitype, scitype_union, scitypes
-export predict, predict_mean, predict_median, predict_mode
-export transform, inverse_transform, se, evaluate, fitted_params
-export @constant, @more, HANDLE_GIVEN_ID, UnivariateFinite
-export partition, X_and_y
-export load_boston, load_ames, load_iris, load_reduced_ames
-export load_crabs, datanow                
-export features, X_and_y
+export nrows, nfeatures, info,
+        SupervisedTask, UnsupervisedTask, MLJTask,
+        Deterministic, Probabilistic, Unsupervised, Supervised,
+        DeterministicNetwork, ProbabilisticNetwork,
+        Found, Continuous, Finite, Infinite,
+        OrderedFactor, Unknown,
+        Count, Multiclass, Binary,
+        scitype, scitype_union, scitypes,
+        predict, predict_mean, predict_median, predict_mode,
+        transform, inverse_transform, se, evaluate, fitted_params,
+        @constant, @more, HANDLE_GIVEN_ID, UnivariateFinite,
+        partition, X_and_y,
+        load_boston, load_ames, load_iris, load_reduced_ames,
+        load_crabs, datanow,
+        features, X_and_y
 
 using MLJBase
-
 # to be extended:
-import MLJBase: fit, update, clean!
-import MLJBase: predict, predict_mean, predict_median, predict_mode
-import MLJBase: transform, inverse_transform, se, evaluate, fitted_params
-import MLJBase: show_as_constructed, params
+import MLJBase: fit, update, clean!,
+                predict, predict_mean, predict_median, predict_mode,
+                transform, inverse_transform, se, evaluate, fitted_params,
+                show_as_constructed, params
 
 using RemoteFiles
 import Pkg.TOML
@@ -65,6 +64,11 @@ import Distributions
 import StatsBase
 using ProgressMeter
 import Tables
+import Random
+
+# convenience packages
+using DocStringExtensions: SIGNATURES, TYPEDEF
+using Parameters
 
 # to be extended:
 import Base.==
@@ -78,7 +82,7 @@ import Distributed: @distributed, nworkers, pmap
 using RecipesBase # for plotting
 
 # submodules of this module:
-include("registry/src/Registry.jl") 
+include("registry/src/Registry.jl")
 import .Registry
 
 const srcdir = dirname(@__FILE__) # the directory containing this file:

src/composites.jl

@@ -16,56 +16,49 @@ MLJBase.predict(composite::SupervisedNetwork, fitresult, Xnew) =
     fitresult(Xnew)
 
 """
-    MLJ.tree(N::Node)
+$SIGNATURES
 
 Return a description of the tree defined by the learning network
-terminating at node `N`.
-
+terminating at a given node.
 """
-tree(s::MLJ.Source) = (source = s,)
 function tree(W::MLJ.Node)
     mach = W.machine
-    if mach == nothing
+    if mach === nothing
         value2 = nothing
-        endkeys=[]
-        endvalues=[]
+        endkeys = []
+        endvalues = []
     else
         value2 = mach.model
-        endkeys = [Symbol("train_arg", i) for i in eachindex(mach.args)]
-        endvalues = [tree(arg) for arg in mach.args]
+        endkeys = (Symbol("train_arg", i) for i in eachindex(mach.args))
+        endvalues = (tree(arg) for arg in mach.args)
     end
     keys = tuple(:operation,  :model,
-                 [Symbol("arg", i) for i in eachindex(W.args)]...,
+                 (Symbol("arg", i) for i in eachindex(W.args))...,
                  endkeys...)
     values = tuple(W.operation, value2,
-                   [tree(arg) for arg in W.args]...,
+                   (tree(arg) for arg in W.args)...,
                    endvalues...)
     return NamedTuple{keys}(values)
 end
+tree(s::MLJ.Source) = (source = s,)
 
-# get the top level args of the tree of some node:
-function args(tree)
-    keys_ = filter(keys(tree) |> collect) do key
-        match(r"^arg[0-9]*", string(key)) != nothing
-    end
-    return [getproperty(tree, key) for key in keys_]
-end
+"""
+$SIGNATURES
 
-# get the top level train_args of the tree of some node:
-function train_args(tree)
+Return a vector of the top level args of the tree associated with a node.
+If `train=true`, return the `train_args`.
+"""
+function args(tree; train=false)
     keys_ = filter(keys(tree) |> collect) do key
-        match(r"^train_arg[0-9]*", string(key)) != nothing
+        match(Regex("^$("train_"^train)arg[0-9]*"), string(key)) !== nothing
     end
     return [getproperty(tree, key) for key in keys_]
 end
 
 """
+$SIGNATURES
 
-    models(N::AbstractNode)
-
-A vector of all models referenced by node `N`, each model
-appearing exactly once.
-
+A vector of all models referenced by a node, each model appearing exactly once.
 """
 function models(W::MLJ.AbstractNode)
     models_ = filter(flat_values(tree(W)) |> collect) do model
@@ -75,16 +68,16 @@ function models(W::MLJ.AbstractNode)
 end
 
 """
-   sources(N::AbstractNode)
+$SIGNATURES
 
 A vector of all sources referenced by calls `N()` and `fit!(N)`. These
 are the sources of the directed acyclic graph associated with the
 learning network terminating at `N`.
 
-Not to be confused with `origins(N)` which refers to the same graph with edges corresponding to training arguments deleted.
+Not to be confused with `origins(N)` which refers to the same graph with edges
+corresponding to training arguments deleted.
 
 See also: [`origins`](@ref), [`source`](@ref).
-
 """
 function sources(W::MLJ.AbstractNode)
     sources_ = filter(MLJ.flat_values(tree(W)) |> collect) do model
@@ -94,30 +87,28 @@ function sources(W::MLJ.AbstractNode)
 end
 
 """
-    machines(N)
-
-List all machines in the learning network terminating at node `N`.
+$SIGNATURES
 
+List all machines in the learning network terminating at a given node.
 """
-machines(W::MLJ.Source) = Any[]
 function machines(W::MLJ.Node)
-    if W.machine == nothing
-        return vcat([machines(arg) for arg in W.args]...) |> unique
+    if W.machine === nothing
+        return vcat((machines(arg) for arg in W.args) |> collect) |> unique
     else
         return vcat(Any[W.machine, ],
-                   [machines(arg) for arg in W.args]...,
-                   [machines(arg) for arg in W.machine.args]...) |> unique
+                   (machines(arg) for arg in W.args)...,
+                   (machines(arg) for arg in W.machine.args)...) |> unique
     end
 end
+machines(W::MLJ.Source) = Any[]
 
 """
-    replace(W::MLJ.Node, a1=>b1, a2=>b2, ....)
+    replace(W::MLJ.Node, a1=>b1, a2=>b2, ...)
 
 Create a deep copy of a node `W`, and thereby replicate the learning
 network terminating at `W`, but replacing any specified sources and
 models `a1, a2, ...` of the original network with the specified targets
 `b1, b2, ...`.
-
 """
 function Base.replace(W::Node, pairs::Pair...)
 
@@ -161,7 +152,7 @@ function Base.replace(W::Node, pairs::Pair...)
     # build the new network:
     for N in nodes_
        args = [newnode_given_old[arg] for arg in N.args]
-         if N.machine == nothing
+         if N.machine === nothing
              newnode_given_old[N] = node(N.operation, args...)
          else
              if N.machine in keys(newmach_given_old)
@@ -199,7 +190,7 @@ end
 function supervised_fit_method(network_Xs, network_ys, network_N,
                                network_models...)
 
-    function fit(model::M, verbosity, X, y) where M <:Supervised
+    function fit(model::M, verbosity, X, y) where M <: Supervised
         Xs = source(X)
         ys = source(y)
         replacement_models = [getproperty(model, fld)

src/loading.jl

@@ -10,7 +10,7 @@ to be specified.
 
 """
 function MLJBase.info(model::String; pkg=nothing)
-    if pkg == nothing
+    if pkg === nothing
         if model in string.(MLJBase.finaltypes(Model))
             pkg = "MLJ"
         else