alan-turing-institute / MLJTuning.jl / 86

Committed 19 Mar 2020 - 4:43 coverage: 83.951% (+3.5%) from 80.455%

Build # 86

Build Type

Pull #21

travis-ci-com

Committed by

web-flow

Commit Message

add tests and fixes for model metadata enhancement

Pull Request Pull Request #21: Enhancements to the tuning interface

Run Details

19 of 21 new or added lines in 3 files covered. (90.48%)

204 of 243 relevant lines covered (83.95%)

239.65 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

81.82

/src/tuning_strategy_interface.jl

abstract type TuningStrategy <: MLJBase.MLJType end
MLJBase.show_as_constructed(::Type{<:TuningStrategy}) = true

# for initialization of state (compulsory)
setup(tuning::TuningStrategy, model, range, verbosity) = range

# for building each element of the history:
result(tuning::TuningStrategy, history, state, e, metadata) =
    (measure=e.measure, measurement=e.measurement)

# for generating batches of new models and updating the state (but not
# history):
function models! end

# for extracting the optimal model (and its performance) from the
# history:
function best(tuning::TuningStrategy, history)
   measurements = [h[2].measurement[1] for h in history]
   measure = first(history)[2].measure[1]
   if orientation(measure) == :score
       measurements = -measurements
   end
   best_index = argmin(measurements)
   return history[best_index]
end

# for selecting what to report to the user apart from the optimal
# model:
tuning_report(tuning::TuningStrategy, history, state) = (history=history,)

# for declaring the default number of models to evaluate:
function default_n(tuning::TuningStrategy, range)
    try
        length(range)
    catch MethodError
        DEFAULT_N
    end
end


1	abstract type TuningStrategy <: MLJBase.MLJType end
2	MLJBase.show_as_constructed(::Type{<:TuningStrategy}) = true	!
3
4	# for initialization of state (compulsory)
5	setup(tuning::TuningStrategy, model, range, verbosity) = range	9×
6
7	# for building each element of the history:
8	result(tuning::TuningStrategy, history, state, e, metadata) =	3,138×
9	(measure=e.measure, measurement=e.measurement)
10
11	# for generating batches of new models and updating the state (but not
12	# history):
13	function models! end
14
15	# for extracting the optimal model (and its performance) from the
16	# history:
17	function best(tuning::TuningStrategy, history)
18	measurements = [h[2].measurement[1] for h in history]	463×
19	measure = first(history)[2].measure[1]
20	if orientation(measure) == :score	206×
21	measurements = -measurements
22	end
23	best_index = argmin(measurements)	1,626×
24	return history[best_index]	166×
25	end
26
27	# for selecting what to report to the user apart from the optimal
28	# model:
29	tuning_report(tuning::TuningStrategy, history, state) = (history=history,)	90×
30
31	# for declaring the default number of models to evaluate:
32	function default_n(tuning::TuningStrategy, range)
33	try	63×
34	length(range)	35×
35	catch MethodError
NEW 36	DEFAULT_N	!
37	end
38	end
39

alan-turing-institute / MLJTuning.jl / 86

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous