Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

For a 1.7 release #988

Merged
merged 5 commits into from
Jul 19, 2024
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion Project.toml
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "MLJBase"
uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
version = "1.6"
version = "1.7.0"

[deps]
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
Expand Down
46 changes: 35 additions & 11 deletions src/resampling.jl
Original file line number Diff line number Diff line change
Expand Up @@ -31,10 +31,6 @@ const ERR_INVALID_OPERATION = ArgumentError(
_ambiguous_operation(model, measure) =
"`$measure` does not support a `model` with "*
"`prediction_type(model) == :$(prediction_type(model))`. "
err_ambiguous_operation(model, measure) = ArgumentError(
_ambiguous_operation(model, measure)*
"\nUnable to infer an appropriate operation for `$measure`. "*
"Explicitly specify `operation=...` or `operations=...`. ")
err_incompatible_prediction_types(model, measure) = ArgumentError(
_ambiguous_operation(model, measure)*
"If your model is truly making probabilistic predictions, try explicitly "*
Expand Down Expand Up @@ -65,11 +61,37 @@ ERR_MEASURES_DETERMINISTIC(measure) = ArgumentError(
"and so is not supported by `$measure`. "*LOG_AVOID
)

# ==================================================================
## MODEL TYPES THAT CAN BE EVALUATED
err_ambiguous_operation(model, measure) = ArgumentError(
_ambiguous_operation(model, measure)*
"\nUnable to infer an appropriate operation for `$measure`. "*
"Explicitly specify `operation=...` or `operations=...`. "*
"Possible value(s) are: $PREDICT_OPERATIONS_STRING. "
)

const ERR_UNSUPPORTED_PREDICTION_TYPE = ArgumentError(
"""

# not exported:
const Measurable = Union{Supervised, Annotator}
The `prediction_type` of your model needs to be one of: `:deterministic`,
`:probabilistic`, or `:interval`. Does your model implement one of these operations:
$PREDICT_OPERATIONS_STRING? If so, you can try explicitly specifying `operation=...`
or `operations=...` (and consider posting an issue to have the model review it's
definition of `MLJModelInterface.prediction_type`). Otherwise, performance
evaluation is not supported.

"""
)

const ERR_NEED_TARGET = ArgumentError(
"""

To evaluate a model's performance you must provide a target variable `y`, as in
`evaluate(model, X, y; options...)` or

mach = machine(model, X, y)
evaluate!(mach; options...)

"""
)

# ==================================================================
## RESAMPLING STRATEGIES
Expand Down Expand Up @@ -987,7 +1009,7 @@ function _actual_operations(operation::Nothing,
throw(err_ambiguous_operation(model, m))
end
else
throw(err_ambiguous_operation(model, m))
throw(ERR_UNSUPPORTED_PREDICTION_TYPE)
end
end
end
Expand Down Expand Up @@ -1137,7 +1159,7 @@ See also [`evaluate`](@ref), [`PerformanceEvaluation`](@ref),

"""
function evaluate!(
mach::Machine{<:Measurable};
mach::Machine;
resampling=CV(),
measures=nothing,
measure=measures,
Expand All @@ -1160,6 +1182,8 @@ function evaluate!(
# weights, measures, operations, and dispatches a
# strategy-specific `evaluate!`

length(mach.args) > 1 || throw(ERR_NEED_TARGET)

repeats > 0 || error("Need `repeats > 0`. ")

if resampling isa TrainTestPairs
Expand Down Expand Up @@ -1235,7 +1259,7 @@ Returns a [`PerformanceEvaluation`](@ref) object.
See also [`evaluate!`](@ref).

"""
evaluate(model::Measurable, args...; cache=true, kwargs...) =
evaluate(model::Model, args...; cache=true, kwargs...) =
evaluate!(machine(model, args...; cache=cache); kwargs...)

# -------------------------------------------------------------------
Expand Down
32 changes: 31 additions & 1 deletion test/resampling.jl
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,8 @@ end
struct DummyInterval <: Interval end
dummy_interval=DummyInterval()

struct GoofyTransformer <: Unsupervised end

dummy_measure_det(yhat, y) = 42
API.@trait(
typeof(dummy_measure_det),
Expand Down Expand Up @@ -115,6 +117,12 @@ API.@trait(
MLJBase.err_ambiguous_operation(dummy_interval, LogLoss()),
MLJBase._actual_operations(nothing,
[LogLoss(), ], dummy_interval, 1))

# model does not have a valid `prediction_type`:
@test_throws(
MLJBase.ERR_UNSUPPORTED_PREDICTION_TYPE,
MLJBase._actual_operations(nothing, [LogLoss(),], GoofyTransformer(), 0),
)
end

@everywhere begin
Expand Down Expand Up @@ -935,7 +943,29 @@ end
end
end

# DUMMY LOGGER

# # TRANSFORMER WITH PREDICT

struct PredictingTransformer <:Unsupervised end
MLJBase.fit(::PredictingTransformer, verbosity, X, y) = (mean(y), nothing, nothing)
MLJBase.fit(::PredictingTransformer, verbosity, X) = (nothing, nothing, nothing)
MLJBase.predict(::PredictingTransformer, fitresult, X) = fill(fitresult, nrows(X))
MLJBase.predict(::PredictingTransformer, ::Nothing, X) = nothing
MLJBase.prediction_type(::Type{<:PredictingTransformer}) = :deterministic

@testset "`Unsupervised` model with a predict" begin
X = rand(10)
y = fill(42.0, 10)
e = evaluate(PredictingTransformer(), X, y, resampling=Holdout(), measure=l2)
@test e.measurement[1] ≈ 0
@test_throws(
MLJBase.ERR_NEED_TARGET,
evaluate(PredictingTransformer(), X, measure=l2),
)
end


# # DUMMY LOGGER

struct DummyLogger end

Expand Down
Loading