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Merge pull request #63 from alan-turing-institute/dev
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For a 0.4.1 release
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@ablaom
ablaom authored Jul 20, 2020
2 parents fcc05fa + d0f73d9 commit 6fed9cb
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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "MLJTuning"
uuid = "03970b2e-30c4-11ea-3135-d1576263f10f"
authors = ["Anthony D. Blaom <[email protected]>"]
version = "0.4.0"
version = "0.4.1"

[deps]
ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
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198 changes: 129 additions & 69 deletions src/learning_curves.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,8 @@
curve = learning_curve(mach; resolution=30,
resampling=Holdout(),
repeats=1,
measure=rms,
measure=default_measure(machine.model),
rows=nothing,
weights=nothing,
operation=predict,
range=nothing,
Expand Down Expand Up @@ -67,6 +68,46 @@ plot!(curves.parameter_values,
Plot a learning curve (or curves) directly, without first constructing
a machine.
### Summary of key-word options
- `resolution` - number of points generated from `range` (number model
evaluations); default is `30`
- `resampling` - resampling strategy; default is `Holdout(fraction_train=0.7)`
- `repeats` - set to more than `1` for repeated (Monte Carlo) resampling
- `measure` - performance measure (metric); automatically inferred
from model by default when possible
- `rows` - row indices to which resampling should be restricted;
default is all rows
- `weights` - sample weights used by `measure`; defaults to weights
bound to `mach` if these exist
- `operation` - operation, such as `predict`, to be used in
evaluations. If `prediction_type(mach.model) == :probabilistic` but
`prediction_type(measure) == :deterministic` consider `,`predict_mode`,
`predict_mode` or `predict_median`; default is `predict`.
- `range` - object constructed using `range(model, ...)` or
`range(type, ...)` representing one-dimensional hyper-parameter
range.
- `acceleration` - parallelization option for passing to `evaluate!`;
an instance of `CPU1`, `CPUProcesses` or `CPUThreads` from the
`ComputationalResources.jl`; default is `default_resource()`
- `acceleration_grid` - parallelization option for distributing each
performancde evaluation
- `rngs` - for specifying random number generator(s) to be passed to
the model (see above)
- `rng_name` - name of the model hyper-parameter representing a random
number generator (see above); possibly nested
"""
learning_curve(mach::Machine{<:Supervised}; kwargs...) =
learning_curve(mach.model, mach.args...; kwargs...)
Expand All @@ -81,6 +122,7 @@ function learning_curve(model::Supervised, args...;
weights=nothing,
measures=nothing,
measure=measures,
rows=nothing,
operation=predict,
ranges::Union{Nothing,ParamRange}=nothing,
range::Union{Nothing,ParamRange},
Expand All @@ -107,27 +149,27 @@ function learning_curve(model::Supervised, args...;
"`AbstractVector{<:AbstractRNG}`. ")
end
end
if (acceleration isa CPUProcesses &&

if (acceleration isa CPUProcesses &&
acceleration_grid isa CPUProcesses)
message =
message =
"The combination acceleration=$(acceleration) and"*
" acceleration_grid=$(acceleration_grid) is"*
" not generally optimal. You may want to consider setting"*
" `acceleration = CPUProcesses()` and"*
" `acceleration_grid = CPUThreads()`."
@warn message
end
if (acceleration isa CPUThreads &&
if (acceleration isa CPUThreads &&
acceleration_grid isa CPUProcesses)
message =
message =
"The combination acceleration=$(acceleration) and"*
" acceleration_grid=$(acceleration_grid) isn't supported. \n"*
"Resetting to"*
" `acceleration = CPUProcesses()` and"*
" `acceleration_grid = CPUThreads()`."
@warn message
acceleration = CPUProcesses()
acceleration = CPUProcesses()
acceleration_grid = CPUThreads()
end
_acceleration = _process_accel_settings(acceleration)
Expand All @@ -145,7 +187,8 @@ function learning_curve(model::Supervised, args...;

tuned = machine(tuned_model, args...)

results = _tuning_results(rngs, _acceleration, tuned, rng_name, verbosity)
results =
_tuning_results(rngs, _acceleration, tuned, rows, rng_name, verbosity)

parameter_name=results.parameter_names[1]
parameter_scale=results.parameter_scales[1]
Expand All @@ -164,68 +207,80 @@ _collate(plotting1, plotting2) =
plotting2.measurements),))

# fallback:
#_tuning_results(rngs, acceleration, tuned, rngs_name, verbosity) =
#_tuning_results(rngs, acceleration, tuned, rows, rngs_name, verbosity) =
# error("acceleration=$acceleration unsupported. ")

# single curve:
_tuning_results(rngs::Nothing, acceleration, tuned, rngs_name, verbosity) =
_single_curve(tuned, verbosity)

function _single_curve(tuned, verbosity)
fit!(tuned, verbosity=verbosity, force=true)
_tuning_results(rngs::Nothing,
acceleration,
tuned,
rows,
rngs_name,
verbosity) = _single_curve(tuned, rows, verbosity)

function _single_curve(tuned, rows, verbosity)
fit!(tuned, rows=rows, verbosity=verbosity, force=true)
tuned.report.plotting
end

# CPU1:
function _tuning_results(rngs::AbstractVector, acceleration::CPU1,
tuned, rng_name, verbosity)

function _tuning_results(rngs::AbstractVector,
acceleration::CPU1,
tuned,
rows,
rng_name,
verbosity)

old_rng = recursive_getproperty(tuned.model.model, rng_name)
n_rngs = length(rngs)

p = Progress(n_rngs,
dt = 0,
desc = "Evaluating Learning curve with $(n_rngs) rngs: ",
barglyphs = BarGlyphs("[=> ]"),
barlen = 18,
color = :yellow)

verbosity < 1 || update!(p,0)

ret = mapreduce(_collate, rngs) do rng
recursive_setproperty!(tuned.model.model, rng_name, rng)
fit!(tuned, verbosity=verbosity-1, force=true)
fit!(tuned, rows=rows, verbosity=verbosity-1, force=true)
r =tuned.report.plotting
verbosity < 1 || begin
p.counter += 1
ProgressMeter.updateProgress!(p)
ProgressMeter.updateProgress!(p)
end
r
end

recursive_setproperty!(tuned.model.model, rng_name, old_rng)

return ret
end

# CPUProcesses:
function _tuning_results(rngs::AbstractVector, acceleration::CPUProcesses,
tuned, rng_name, verbosity)

function _tuning_results(rngs::AbstractVector,
acceleration::CPUProcesses,
tuned,
rows,
rng_name,
verbosity)

old_rng = recursive_getproperty(tuned.model.model, rng_name)
n_rngs = length(rngs)

ret = @sync begin

p = Progress(n_rngs,
dt = 0,
desc = "Evaluating Learning curve with $(n_rngs) rngs: ",
barglyphs = BarGlyphs("[=> ]"),
barlen = 18,
color = :yellow)

channel = RemoteChannel(()->Channel{Bool}(min(1000, n_rngs)), 1)

# printing the progress bar
verbosity < 1 || begin
update!(p,0)
Expand All @@ -237,12 +292,12 @@ function _tuning_results(rngs::AbstractVector, acceleration::CPUProcesses,

ret_ = @distributed (_collate) for rng in rngs
recursive_setproperty!(tuned.model.model, rng_name, rng)
fit!(tuned, verbosity=verbosity-1, force=true)
fit!(tuned, rows=rows, verbosity=verbosity-1, force=true)
r=tuned.report.plotting
verbosity < 1 || put!(channel, true)
r
end

verbosity < 1 || put!(channel, false)
ret_
end
Expand All @@ -252,15 +307,19 @@ end

# CPUThreads:
@static if VERSION >= v"1.3.0-DEV.573"
function _tuning_results(rngs::AbstractVector, acceleration::CPUThreads,
tuned, rng_name, verbosity)

function _tuning_results(rngs::AbstractVector,
acceleration::CPUThreads,
tuned,
rows,
rng_name,
verbosity)

n_threads = Threads.nthreads()
if n_threads == 1
return _tuning_results(rngs, CPU1(),
tuned, rng_name, verbosity)
end

old_rng = recursive_getproperty(tuned.model.model, rng_name)
n_rngs = length(rngs)
ntasks = acceleration.settings
Expand All @@ -272,7 +331,7 @@ function _tuning_results(rngs::AbstractVector, acceleration::CPUThreads,
barglyphs = BarGlyphs("[=> ]"),
barlen = 18,
color = :yellow)

ch = Channel{Bool}(length(partitions))

ret_ = Vector(undef, length(partitions))
Expand All @@ -281,43 +340,44 @@ function _tuning_results(rngs::AbstractVector, acceleration::CPUThreads,
verbosity < 1 || begin
update!(p,0)
@async while take!(ch)
p.counter +=1
p.counter +=1
ProgressMeter.updateProgress!(p)
end
end

# One t_tuned per task
## deepcopy of model is because other threads can still change the state
## of tuned.model.model
tmachs = [tuned, [machine(TunedModel(model = deepcopy(tuned.model.model),
range=deepcopy(tuned.model.range),
tuning=tuned.model.tuning,
resampling=tuned.model.resampling,
operation=tuned.model.operation,
measure=tuned.model.measure,
train_best=tuned.model.train_best,
weights=tuned.model.weights,
repeats=tuned.model.repeats,
acceleration=tuned.model.acceleration),
tuned.args...) for _ in 2:length(partitions)]...]
@sync for (i,rng_part) in enumerate(partitions)
Threads.@spawn begin
ret_[i] = mapreduce(_collate, rng_part) do k
recursive_setproperty!(tmachs[i].model.model, rng_name, rngs[k])
fit!(tmachs[i], verbosity=verbosity-1, force=true)
verbosity < 1 || put!(ch, true)
tmachs[i].report.plotting
end

# One t_tuned per task
## deepcopy of model is because other threads can still change the state
## of tuned.model.model
tmachs = [tuned,
[machine(TunedModel(model = deepcopy(tuned.model.model),
range=deepcopy(tuned.model.range),
tuning=tuned.model.tuning,
resampling=tuned.model.resampling,
operation=tuned.model.operation,
measure=tuned.model.measure,
train_best=tuned.model.train_best,
weights=tuned.model.weights,
repeats=tuned.model.repeats,
acceleration=tuned.model.acceleration),
tuned.args...) for _ in 2:length(partitions)]...]
@sync for (i,rng_part) in enumerate(partitions)
Threads.@spawn begin
ret_[i] = mapreduce(_collate, rng_part) do k
recursive_setproperty!(tmachs[i].model.model,
rng_name, rngs[k])
fit!(tmachs[i], rows=rows,
verbosity=verbosity-1, force=true)
verbosity < 1 || put!(ch, true)
tmachs[i].report.plotting
end
end
end
end
verbosity < 1 || put!(ch, false)
end

ret = reduce(_collate, ret_)
recursive_setproperty!(tuned.model.model, rng_name, old_rng)
return ret
end

end
end

ret = reduce(_collate, ret_)
recursive_setproperty!(tuned.model.model, rng_name, old_rng)
return ret
end

end
27 changes: 27 additions & 0 deletions test/learning_curves.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,15 @@ y = 2*x1 .+ 5*x2 .- 3*x3 .+ 0.2*rand(100);

@test curves2.measurements ≈ curves3.measurements

# restricting rows gives different answer:
curves4 = learning_curve(ensemble, X, y; range=r_n, resolution=7,
rows = 1:60,
acceleration=accel,
rngs = 3,
rng_name=:rng, verbosity=0)

@test !(curves4.measurements[1] ≈ curves2.measurements[1])

end

@static if VERSION >= v"1.3.0-DEV.573"
Expand Down Expand Up @@ -124,6 +133,15 @@ end

@test curves2.measurements ≈ curves3.measurements

# restricting rows gives different answer:
curves4 = learning_curve(ensemble, X, y; range=r_n, resolution=7,
acceleration=accel, acceleration_grid = CPUThreads(),
rows = 1:60,
rngs = 3,
rng_name=:rng, verbosity=0)

@test !(curves4.measurements[1] ≈ curves2.measurements[1])

end
end

Expand Down Expand Up @@ -177,6 +195,15 @@ end

@test curves2.measurements ≈ curves3.measurements

# restricting rows gives different answer:
curves4 = learning_curve(ensemble, X, y; range=r_n, resolution=7,
acceleration=accel, acceleration_grid = CPUProcesses(),
rows=1:60,
rngs = 3,
rng_name=:rng, verbosity=0)

@test !(curves4.measurements[1] ≈ curves2.measurements[1])

end


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