Merge pull request #808 from JuliaAI/dev

For a 0.20.12 release

Author: ablaom

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBase"
 uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.20.11"
+version = "0.20.12"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -36,14 +36,14 @@ ComputationalResources = "0.3"
 Distributions = "0.25.3"
 InvertedIndices = "1"
 LossFunctions = "0.5, 0.6, 0.7, 0.8"
-MLJModelInterface = "1.5"
+MLJModelInterface = "1.6"
 Missings = "0.4, 1"
 OrderedCollections = "1.1"
 Parameters = "0.12"
 PrettyTables = "1"
 ProgressMeter = "1.7.1"
 ScientificTypes = "3"
-StatisticalTraits = "3"
+StatisticalTraits = "3.2"
 StatsBase = "0.32, 0.33"
 Tables = "0.2, 1.0"
 julia = "1.6"

src/composition/learning_networks/machines.jl

@@ -225,6 +225,22 @@ $DOC_SIGNATURES
 """
 glb(mach::Machine{<:Union{Composite,Surrogate}}) = glb(mach.fitresult)
 
+"""
+    report(fitresult::CompositeFitresult)
+
+Return a tuple combining the report from `fitresult.glb` (a `Node` report) with the
+additions coming from nodes declared as report nodes in `fitresult.signature`, but without
+merging the two.
+
+$DOC_SIGNATURES
+
+**Private method**
+"""
+function report(fitresult::CompositeFitresult)
+    basic = report(glb(fitresult))
+    additions = _call(_report_part(signature(fitresult)))
+    return (; basic, additions)
+end
 
 """
     fit!(mach::Machine{<:Surrogate};
@@ -245,11 +261,10 @@ See also [`machine`](@ref)
 
 """
 function fit!(mach::Machine{<:Surrogate}; kwargs...)
-    glb_node = glb(mach)
-    fit!(glb_node; kwargs...)
+    glb = MLJBase.glb(mach)
+    fit!(glb; kwargs...)
     mach.state += 1
-    report_additions_ = _call(_report_part(signature(mach.fitresult)))
-    mach.report = merge(report(glb_node), report_additions_)
+    mach.report = MLJBase.report(mach.fitresult)
     return mach
 end
 
@@ -347,7 +362,7 @@ the following:
 
 - Calls `fit!(mach, verbosity=verbosity, acceleration=acceleration)`.
 
-- Records a copy of `model` in a variable called `cache`.
+- Records (among other things) a copy of `model` in a variable called `cache`
 
 - Returns `cache` and outcomes of training in an appropriate form
   (specifically, `(mach.fitresult, cache, mach.report)`; see [Adding
@@ -396,6 +411,7 @@ function return!(mach::Machine{<:Surrogate},
     # record the current hyper-parameter values:
     old_model = deepcopy(model)
 
+    glb = MLJBase.glb(mach)
     cache = (; old_model)
 
     setfield!(mach.fitresult,
@@ -647,9 +663,8 @@ function restore!(mach::Machine{<:Composite})
     return mach
 end
 
-
-function setreport!(mach::Machine{<:Composite}, report)
-    basereport = MLJBase.report(glb(mach))
-    report_additions = Base.structdiff(report, basereport)
-    mach.report = merge(basereport, report_additions)
+function setreport!(copymach::Machine{<:Composite}, mach)
+    basic = report(glb(copymach.fitresult))
+    additions = mach.report.additions
+    copymach.report = (; basic, additions)
 end

src/composition/models/inspection.jl

@@ -5,8 +5,9 @@ try_scalarize(v) = length(v) == 1 ? v[1] : v
 function machines_given_model_name(mach::Machine{M}) where  M<:Composite
     network_model_names = getfield(mach.fitresult, :network_model_names)
     names = unique(filter(name->!(name === nothing), network_model_names))
-    network_models = MLJBase.models(glb(mach))
-    network_machines = MLJBase.machines(glb(mach))
+    glb = MLJBase.glb(mach)
+    network_models = MLJBase.models(glb)
+    network_machines = MLJBase.machines(glb)
     ret = LittleDict{Symbol,Any}()
     for name in names
         mask = map(==(name), network_model_names)
@@ -17,22 +18,27 @@ function machines_given_model_name(mach::Machine{M}) where  M<:Composite
     return ret
 end
 
-function tuple_keyed_on_model_names(item_given_machine, mach)
+function tuple_keyed_on_model_names(machines, mach, f)
     dict = MLJBase.machines_given_model_name(mach)
     names = tuple(keys(dict)...)
     named_tuple_values = map(names) do name
-        [item_given_machine[m] for m in dict[name]] |> try_scalarize
+        [f(m) for m in dict[name]] |> try_scalarize
     end
     return NamedTuple{names}(named_tuple_values)
 end
 
-function report(mach::Machine{<:Composite})
-    dict = mach.report.report_given_machine
-    return merge(tuple_keyed_on_model_names(dict, mach), mach.report)
+function report(mach::Machine{<:Union{Composite,Surrogate}})
+    report_additions = mach.report.additions
+    report_basic = mach.report.basic
+    report_components = mach isa Machine{<:Surrogate} ? NamedTuple() :
+        MLJBase.tuple_keyed_on_model_names(report_basic.machines, mach, MLJBase.report)
+    return merge(report_components, report_basic, report_additions)
 end
 
 function fitted_params(mach::Machine{<:Composite})
-    fp = fitted_params(mach.model, mach.fitresult)
-    dict = fp.fitted_params_given_machine
-    return merge(MLJBase.tuple_keyed_on_model_names(dict, mach), fp)
+    fp_basic = fitted_params(mach.model, mach.fitresult)
+    machines = fp_basic.machines
+    fp_components =
+        MLJBase.tuple_keyed_on_model_names(machines, mach, MLJBase.fitted_params)
+    return merge(fp_components, fp_basic)
 end

src/composition/models/methods.jl

@@ -31,24 +31,25 @@ function update(model::M,
     # underlying learning network machine.
 
     network_model_names = getfield(fitresult, :network_model_names)
-    old_model = cache.old_model
 
-    glb_node = glb(fitresult) # greatest lower bound
+    old_model = cache.old_model
+    glb = MLJBase.glb(fitresult) # greatest lower bound of network, a node
 
-    if fallback(model, old_model, network_model_names, glb_node)
+    if fallback(model, old_model, network_model_names, glb)
         return fit(model, verbosity, args...)
     end
 
-    fit!(glb_node; verbosity=verbosity)
+    fit!(glb; verbosity=verbosity)
+
     # Retrieve additional report values
-    report_additions_ = _call(_report_part(signature(fitresult)))
+    report = MLJBase.report(fitresult)
 
     # record current model state:
     cache = (; old_model = deepcopy(model))
 
     return (fitresult,
             cache,
-            merge(report(glb_node), report_additions_))
+            report)
 
 end
 

src/composition/models/pipelines.jl

@@ -608,7 +608,7 @@ MMI.target_scitype(p::SupervisedPipeline) = target_scitype(supervised_component(
 # ## Training losses
 
 function MMI.training_losses(pipe::SupervisedPipeline, pipe_report)
-    mach = supervised(pipe_report.machines)
+    mach = supervised(pipe_report.basic.machines)
     _report = report(mach)
     return training_losses(mach.model, _report)
 end

src/composition/models/transformed_target_model.jl

@@ -224,7 +224,7 @@ end
 # # TRAINING LOSSES
 
 function training_losses(model::SomeTT, tt_report)
-    mach = first(tt_report.machines)
+    mach = first(tt_report.basic.machines)
     return training_losses(mach)
 end
 

src/machines.jl

@@ -469,10 +469,11 @@ end
 # Not one, but *two*, fit methods are defined for machines here,
 # `fit!` and `fit_only!`.
 
-# - `fit_only!`: trains a machine without touching the learned
-#   parameters (`fitresult`) of any other machine. It may error if
-#   another machine on which it depends (through its node training
-#   arguments `N1, N2, ...`) has not been trained.
+# - `fit_only!`: trains a machine without touching the learned parameters (`fitresult`) of
+#   any other machine. It may error if another machine on which it depends (through its node
+#   training arguments `N1, N2, ...`) has not been trained. It's possible that a dependent
+#   machine `mach` may have it's report mutated if `reporting_operations(mach.model)` is
+#   non-empty.
 
 # - `fit!`: trains a machine after first progressively training all
 #   machines on which the machine depends. Implicitly this involves
@@ -909,13 +910,14 @@ function serializable(mach::Machine{<:Any, C}) where C
             setfield!(copymach, fieldname, ())
         # Make fitresult ready for serialization
         elseif fieldname == :fitresult
+            # this `save` does the actual emptying of fields
             copymach.fitresult = save(mach.model, getfield(mach, fieldname))
         else
             setfield!(copymach, fieldname, getfield(mach, fieldname))
         end
     end
 
-    setreport!(copymach, mach.report)
+    setreport!(copymach, mach)
 
     return copymach
 end
@@ -997,6 +999,8 @@ function save(file::Union{String,IO},
     serialize(file, smach)
 end
 
+setreport!(copymach, mach) =
+    setfield!(copymach, :report, mach.report)
 
-setreport!(mach::Machine, report) =
-    setfield!(mach, :report, report)
+# NOTE. there is also a specialization for `setreport!` for `Composite` models, defined in
+# /src/composition/learning_networks/machines/

src/operations.jl

@@ -37,37 +37,59 @@ warn_serializable_mach(operation) = "The operation $operation has been called on
                         "deserialised machine mach whose learned parameters "*
                         "may be unusable. To be sure, first run restore!(mach)."
 
+# Given return value `ret` of an operation with symbol `operation` (eg, `:predict`) return
+# `ret` in the ordinary case that the operation does not include an "report" component ;
+# otherwise update `mach.report` with that component and return the non-report part of
+# `ret`:
+function get!(ret, operation, mach)
+    if operation in reporting_operations(mach.model)
+        report = last(ret)
+        if isnothing(mach.report) || isempty(mach.report)
+            mach.report = report
+        else
+            mach.report = merge(mach.report, report)
+        end
+        return first(ret)
+    end
+    return ret
+end
+
 # 0. operations on machine, given rows=...:
 
 for operation in OPERATIONS
 
-    if operation != :inverse_transform
+    quoted_operation = QuoteNode(operation) # eg, :(:predict)
 
-        ex = quote
-            function $(operation)(mach::Machine{<:Model,false}; rows=:)
-                # catch deserialized machine with no data:
-                isempty(mach.args) && _err_serialized($operation)
-                return ($operation)(mach, mach.args[1](rows=rows))
-            end
-            function $(operation)(mach::Machine{<:Model,true}; rows=:)
-                # catch deserialized machine with no data:
-                isempty(mach.args) && _err_serialized($operation)
-                model = mach.model
-                return ($operation)(model,
-                                    mach.fitresult,
-                                    selectrows(model, rows, mach.data[1])...)
-            end
-        end
-        eval(ex)
+    operation == :inverse_transform && continue
 
+    ex = quote
+        function $(operation)(mach::Machine{<:Model,false}; rows=:)
+            # catch deserialized machine with no data:
+            isempty(mach.args) && _err_serialized($operation)
+            ret = ($operation)(mach, mach.args[1](rows=rows))
+            return get!(ret, $quoted_operation, mach)
+        end
+        function $(operation)(mach::Machine{<:Model,true}; rows=:)
+            # catch deserialized machine with no data:
+            isempty(mach.args) && _err_serialized($operation)
+            model = mach.model
+            ret = ($operation)(
+                model,
+                mach.fitresult,
+                selectrows(model, rows, mach.data[1])...,
+            )
+            return get!(ret, $quoted_operation, mach)
+        end
     end
+    eval(ex)
+
 end
 
 # special case of Static models (no training arguments):
 transform(mach::Machine{<:Static}; rows=:) = _err_rows_not_allowed()
 
 inverse_transform(mach::Machine; rows=:) =
-            throw(ArgumentError("`inverse_transform()(mach)` and "*
+            throw(ArgumentError("`inverse_transform(mach)` and "*
                                 "`inverse_transform(mach, rows=...)` are "*
                                 "not supported. Data or nodes "*
                                 "must be explictly specified, "*
@@ -77,22 +99,32 @@ _symbol(f) = Base.Core.Typeof(f).name.mt.name
 
 for operation in OPERATIONS
 
+    quoted_operation = QuoteNode(operation) # eg, :(:predict)
+
     ex = quote
         # 1. operations on machines, given *concrete* data:
         function $operation(mach::Machine, Xraw)
             if mach.state != 0
                 mach.state == -1 && @warn warn_serializable_mach($operation)
-                return $(operation)(mach.model,
-                                    mach.fitresult,
-                                    reformat(mach.model, Xraw)...)
+                ret = $(operation)(
+                    mach.model,
+                    mach.fitresult,
+                    reformat(mach.model, Xraw)...,
+                )
+                get!(ret, $quoted_operation, mach)
             else
                 error("$mach has not been trained.")
             end
         end
 
         function $operation(mach::Machine{<:Static}, Xraw, Xraw_more...)
-            return $(operation)(mach.model, mach.fitresult,
-                                    Xraw, Xraw_more...)
+            ret = $(operation)(
+                mach.model,
+                mach.fitresult,
+                Xraw,
+                Xraw_more...,
+            )
+            get!(ret, $quoted_operation, mach)
         end
 
         # 2. operations on machines, given *dynamic* data (nodes):

test/composition/learning_networks/machines.jl

@@ -88,7 +88,6 @@ end
     @test Θ.transform == Wout
     Θ.report.some_stuff == rnode
     @test report(mach).some_stuff == :stuff
-
     @test report(mach).machines == fitted_params(mach).machines
 
     # supervised
@@ -281,7 +280,7 @@ end
     end
 
     # Testing extra report field : it is a deepcopy
-    @test smach.report.cv_report === mach.report.cv_report
+    @test report(smach).cv_report === report(mach).cv_report
 
     @test smach.fitresult isa MLJBase.CompositeFitresult
 
@@ -356,7 +355,8 @@ end
         metalearner = FooBarRegressor(lambda=1.),
         resampling = dcv,
         model_1 = DeterministicConstantRegressor(),
-        model_2=ConstantRegressor())
+        model_2=ConstantRegressor()
+    )
 
     filesizes = []
     for n in [100, 500, 1000]