Preparing MLJBase to receive logger instances

src/composition/models/stacking.jl

@@ -388,14 +388,17 @@ function internal_stack_report(
     # For each model we record the results mimicking the fields PerformanceEvaluation
     results = NamedTuple{modelnames}(
         [(
+            model = model,
             measure = stack.measures,
             measurement = Vector{Any}(undef, n_measures),
             operation = _actual_operations(nothing, stack.measures, model, verbosity),
             per_fold = [Vector{Any}(undef, nfolds) for _ in 1:n_measures],
             per_observation = Vector{Union{Missing, Vector{Any}}}(missing, n_measures),
             fitted_params_per_fold = [],
             report_per_fold = [],
-            train_test_pairs = tt_pairs
+            train_test_pairs = tt_pairs,
+            resampling = stack.resampling,
+            repeats = 1
         )
          for model in getfield(stack, :models)
          ]

src/resampling.jl

@@ -474,6 +474,9 @@ be interpreted with caution. See, for example, Bates et al.
 These fields are part of the public API of the `PerformanceEvaluation`
 struct.
 
+- `model`: model used to create the performance evaluation. In the case a
+    tuning model, this is the best model found.
+
 - `measure`: vector of measures (metrics) used to evaluate performance
 
 - `measurement`: vector of measurements - one for each element of
@@ -507,22 +510,31 @@ struct.
 - `train_test_rows`: a vector of tuples, each of the form `(train, test)`,
   where `train` and `test` are vectors of row (observation) indices for
   training and evaluation respectively.
+
+- `resampling`: the resampling strategy used to generate the train/test pairs.
+
+- `repeats`: the number of times the resampling strategy was repeated.
 """
 struct PerformanceEvaluation{M,
+                             Measure,
                              Measurement,
                              Operation,
                              PerFold,
                              PerObservation,
                              FittedParamsPerFold,
-                             ReportPerFold} <: MLJType
-    measure::M
+                             ReportPerFold,
+                             R} <: MLJType
+    model::M
+    measure::Measure
     measurement::Measurement
     operation::Operation
     per_fold::PerFold
     per_observation::PerObservation
     fitted_params_per_fold::FittedParamsPerFold
     report_per_fold::ReportPerFold
     train_test_rows::TrainTestPairs
+    resampling::R
+    repeats::Int
 end
 
 # pretty printing:
@@ -568,9 +580,9 @@ function Base.show(io::IO, ::MIME"text/plain", e::PerformanceEvaluation)
 
     println(io, "PerformanceEvaluation object "*
             "with these fields:")
-    println(io, "  measure, operation, measurement, per_fold,\n"*
+    println(io, "  model, measure, operation, measurement, per_fold,\n"*
             "  per_observation, fitted_params_per_fold,\n"*
-            "  report_per_fold, train_test_rows")
+            "  report_per_fold, train_test_rows, resampling, repeats")
     println(io, "Extract:")
     show_color = MLJBase.SHOW_COLOR[]
     color_off()
@@ -808,6 +820,22 @@ _process_accel_settings(accel) =  throw(ArgumentError("unsupported" *
 # --------------------------------------------------------------
 # User interface points: `evaluate!` and `evaluate`
 
+"""
+    log_evaluation(logger, performance_evaluation)
+Log a performance evaluation to `logger`, an object specific to some logging
+platform, such as mlflow. If `logger=nothing` then no logging is performed.
+The method is called at the end of every call to `evaluate/evaluate!` using
+the logger provided by the `logger` keyword argument.
+# Implementations for new logging platforms
+#
+Julia interfaces to workflow logging platforms, such as mlflow (provided by
+the MLFlowClient.jl interface) should overload
+`log_evaluation(logger::LoggerType, performance_evaluation)`,
+where `LoggerType` is a platform-specific type for logger objects. For an
+example, see the implementation provided by the MLJFlow.jl package.
+"""
+log_evaluation(logger, performance_evaluation) = nothing
+
 """
     evaluate!(mach,
               resampling=CV(),
@@ -820,7 +848,8 @@ _process_accel_settings(accel) =  throw(ArgumentError("unsupported" *
               acceleration=default_resource(),
               force=false,
               verbosity=1,
-              check_measure=true)
+              check_measure=true,
+              logger=nothing)
 
 Estimate the performance of a machine `mach` wrapping a supervised
 model in data, using the specified `resampling` strategy (defaulting
@@ -919,6 +948,8 @@ untouched.
 
 - `check_measure` - default is `true`
 
+- `logger` - a logger object (see [`MLJBase.log_evaluation`](@ref))
+
 
 ### Return value
 
@@ -939,7 +970,8 @@ function evaluate!(mach::Machine{<:Measurable};
                    repeats=1,
                    force=false,
                    check_measure=true,
-                   verbosity=1)
+                   verbosity=1,
+                   logger=nothing)
 
     # this method just checks validity of options, preprocess the
     # weights, measures, operations, and dispatches a
@@ -981,7 +1013,8 @@ function evaluate!(mach::Machine{<:Measurable};
     _acceleration= _process_accel_settings(acceleration)
 
     evaluate!(mach, resampling, weights, class_weights, rows, verbosity,
-              repeats, _measures, _operations, _acceleration, force)
+              repeats, _measures, _operations, _acceleration, force, logger,
+              resampling)
 
 end
 
@@ -1158,9 +1191,10 @@ function measure_specific_weights(measure, weights, class_weights, test)
 end
 
 # Evaluation when `resampling` is a TrainTestPairs (CORE EVALUATOR):
-function evaluate!(mach::Machine, resampling, weights,
-                   class_weights, rows, verbosity, repeats,
-                   measures, operations, acceleration, force)
+# `user_resampling` keyword argument is the user defined resampling strategy
+function evaluate!(mach::Machine, resampling, weights, class_weights, rows,
+                   verbosity, repeats, measures, operations, acceleration,
+                   force, logger, user_resampling)
 
     # Note: `rows` and `repeats` are ignored here
 
@@ -1264,17 +1298,22 @@ function evaluate!(mach::Machine, resampling, weights,
         MLJBase.aggregate(per_fold[k], m)
     end
 
-    return PerformanceEvaluation(
+    evaluation = PerformanceEvaluation(
+        mach.model,
         measures,
         per_measure,
         operations,
         per_fold,
         per_observation,
         fitted_params_per_fold |> collect,
         report_per_fold |> collect,
-        resampling
+        resampling,
+        user_resampling,
+        repeats
     )
+    log_evaluation(logger, evaluation)
 
+    evaluation
 end
 
 # ----------------------------------------------------------------
@@ -1293,7 +1332,7 @@ function evaluate!(mach::Machine, resampling::ResamplingStrategy,
             [train_test_pairs(resampling, _rows, train_args...) for i in 1:repeats]...
         )
 
-    return evaluate!(
+    evaluate!(
         mach,
         repeated_train_test_pairs,
         weights,
@@ -1303,7 +1342,6 @@ function evaluate!(mach::Machine, resampling::ResamplingStrategy,
         repeats,
         args...
     )
-
 end
 
 # ====================================================================
@@ -1319,7 +1357,8 @@ end
         operation=predict,
         repeats = 1,
         acceleration=default_resource(),
-        check_measure=true
+        check_measure=true,
+        logger=nothing
     )
 
 Resampling model wrapper, used internally by the `fit` method of
@@ -1354,7 +1393,7 @@ are not to be confused with any weights bound to a `Resampler` instance
 in a machine, used for training the wrapped `model` when supported.
 
 """
-mutable struct Resampler{S} <: Model
+mutable struct Resampler{S, L} <: Model
     model
     resampling::S # resampling strategy
     measure
@@ -1365,6 +1404,7 @@ mutable struct Resampler{S} <: Model
     check_measure::Bool
     repeats::Int
     cache::Bool
+    logger::L
 end
 
 # Some traits are markded as `missing` because we cannot determine
@@ -1403,7 +1443,8 @@ function Resampler(;
     acceleration=default_resource(),
     check_measure=true,
     repeats=1,
-    cache=true
+    cache=true,
+    logger=nothing
 )
     resampler = Resampler(
         model,
@@ -1415,7 +1456,8 @@ function Resampler(;
         acceleration,
         check_measure,
         repeats,
-        cache
+        cache,
+        logger
     )
     message = MLJModelInterface.clean!(resampler)
     isempty(message) || @warn message
@@ -1460,7 +1502,9 @@ function MLJModelInterface.fit(resampler::Resampler, verbosity::Int, args...)
         _measures,
         _operations,
         _acceleration,
-        false
+        false,
+        resampler.logger,
+        resampler.resampling
     )
 
     fitresult = (machine = mach, evaluation = e)
@@ -1523,7 +1567,9 @@ function MLJModelInterface.update(
         measures,
         operations,
         acceleration,
-        false
+        false,
+        resampler.logger,
+        resampler.resampling
     )
     report = (evaluation = e, )
     fitresult = (machine=mach2, evaluation=e)