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@ablaom
ablaom committed May 31, 2024
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Showing 1 changed file with 78 additions and 78 deletions.
156 changes: 78 additions & 78 deletions test/balanced_model.jl
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@testset "BalancedModel" begin
### end-to-end test
# Create and split data
X, y = generate_imbalanced_data(100, 5; class_probs = [0.2, 0.3, 0.5], rng=Random.MersenneTwister(42))
X = DataFrame(X)
train_inds, test_inds =
partition(eachindex(y), 0.8, shuffle = true, stratify = y, rng = Random.MersenneTwister(42))
X_train, X_test = X[train_inds, :], X[test_inds, :]
y_train, y_test = y[train_inds], y[test_inds]

# Load models and balancers
DeterministicConstantClassifier = @load DeterministicConstantClassifier pkg=MLJModels
LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels
# Here are a probabilistic and a deterministic model
model_prob = LogisticClassifier()
model_det = DeterministicConstantClassifier()
# And here are three resamplers from Imbalance.
# The package should actually work with any `Static` transformer of the form `(X, y) -> (Xout, yout)`
# provided that it implements the MLJ interface. Here, the balancer is the transformer
balancer1 = Imbalance.MLJ.RandomOversampler(ratios = 1.0, rng = Random.MersenneTwister(42))
balancer2 = Imbalance.MLJ.SMOTENC(k = 10, ratios = 1.2, rng = Random.MersenneTwister(42))
balancer3 = Imbalance.MLJ.ROSE(ratios = 1.3, rng = Random.MersenneTwister(42))

### 1. Make a pipeline of the three balancers and a probablistic model
## ordinary way
mach = machine(balancer1)
Xover, yover = MLJBase.transform(mach, X_train, y_train)
mach = machine(balancer2)
Xover, yover = MLJBase.transform(mach, Xover, yover)
mach = machine(balancer3)
Xover, yover = MLJBase.transform(mach, Xover, yover)

mach = machine(model_prob, Xover, yover)
fit!(mach)
y_pred = MLJBase.predict(mach, X_test)

# with MLJ balancing
@test_throws MLJBalancing.ERR_MODEL_UNSPECIFIED begin
BalancedModel(b1 = balancer1, b2 = balancer2, b3 = balancer3)
end
### end-to-end test
# Create and split data
X, y = generate_imbalanced_data(100, 5; class_probs = [0.2, 0.3, 0.5], rng=Random.MersenneTwister(42))
X = DataFrame(X)
train_inds, test_inds =
partition(eachindex(y), 0.8, shuffle = true, stratify = y, rng = Random.MersenneTwister(42))
X_train, X_test = X[train_inds, :], X[test_inds, :]
y_train, y_test = y[train_inds], y[test_inds]

# Load models and balancers
DeterministicConstantClassifier = @load DeterministicConstantClassifier pkg=MLJModels
LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels

# Here are a probabilistic and a deterministic model
model_prob = LogisticClassifier()
model_det = DeterministicConstantClassifier()
# And here are three resamplers from Imbalance.
# The package should actually work with any `Static` transformer of the form `(X, y) -> (Xout, yout)`
# provided that it implements the MLJ interface. Here, the balancer is the transformer
balancer1 = Imbalance.MLJ.RandomOversampler(ratios = 1.0, rng = Random.MersenneTwister(42))
balancer2 = Imbalance.MLJ.SMOTENC(k = 10, ratios = 1.2, rng = Random.MersenneTwister(42))
balancer3 = Imbalance.MLJ.ROSE(ratios = 1.3, rng = Random.MersenneTwister(42))

### 1. Make a pipeline of the three balancers and a probablistic model
## ordinary way
mach = machine(balancer1)
Xover, yover = MLJBase.transform(mach, X_train, y_train)
mach = machine(balancer2)
Xover, yover = MLJBase.transform(mach, Xover, yover)
mach = machine(balancer3)
Xover, yover = MLJBase.transform(mach, Xover, yover)

mach = machine(model_prob, Xover, yover)
fit!(mach)
y_pred = MLJBase.predict(mach, X_test)

# with MLJ balancing
@test_throws MLJBalancing.ERR_MODEL_UNSPECIFIED begin
BalancedModel(b1 = balancer1, b2 = balancer2, b3 = balancer3)
end
@test_throws(
MLJBalancing.ERR_UNSUPPORTED_MODEL(1),
BalancedModel(model = 1, b1 = balancer1, b2 = balancer2, b3 = balancer3),
)
@test_logs (:warn, MLJBalancing.WRN_BALANCER_UNSPECIFIED) begin
BalancedModel(model = model_prob)
end
balanced_model =
BalancedModel(model = model_prob, b1 = balancer1, b2 = balancer2, b3 = balancer3)
mach = machine(balanced_model, X_train, y_train)
fit!(mach)
y_pred2 = MLJBase.predict(mach, X_test)
@test_logs (:warn, MLJBalancing.WRN_BALANCER_UNSPECIFIED) begin
BalancedModel(model = model_prob)
end
balanced_model =
BalancedModel(model = model_prob, b1 = balancer1, b2 = balancer2, b3 = balancer3)
mach = machine(balanced_model, X_train, y_train)
fit!(mach)
y_pred2 = MLJBase.predict(mach, X_test)

@test y_pred ≈ y_pred2

# traits:
@test fit_data_scitype(balanced_model) == fit_data_scitype(model_prob)
@test is_wrapper(balanced_model)

### 2. Make a pipeline of the three balancers and a deterministic model
## ordinary way
mach = machine(balancer1)
Xover, yover = MLJBase.transform(mach, X_train, y_train)
mach = machine(balancer2)
Xover, yover = MLJBase.transform(mach, Xover, yover)
mach = machine(balancer3)
Xover, yover = MLJBase.transform(mach, Xover, yover)

mach = machine(model_det, Xover, yover)
fit!(mach)
y_pred = MLJBase.predict(mach, X_test)

# with MLJ balancing
balanced_model =
BalancedModel(model = model_det, b1 = balancer1, b2 = balancer2, b3 = balancer3)
mach = machine(balanced_model, X_train, y_train)
fit!(mach)
y_pred2 = MLJBase.predict(mach, X_test)

@test y_pred == y_pred2

### check that setpropertyname and getpropertyname work
Base.getproperty(balanced_model, :b1) == balancer1
Base.setproperty!(balanced_model, :b1, balancer2)
Base.getproperty(balanced_model, :b1) == balancer2


### 2. Make a pipeline of the three balancers and a deterministic model
## ordinary way
mach = machine(balancer1)
Xover, yover = MLJBase.transform(mach, X_train, y_train)
mach = machine(balancer2)
Xover, yover = MLJBase.transform(mach, Xover, yover)
mach = machine(balancer3)
Xover, yover = MLJBase.transform(mach, Xover, yover)

mach = machine(model_det, Xover, yover)
fit!(mach)
y_pred = MLJBase.predict(mach, X_test)

# with MLJ balancing
balanced_model =
BalancedModel(model = model_det, b1 = balancer1, b2 = balancer2, b3 = balancer3)
mach = machine(balanced_model, X_train, y_train)
fit!(mach)
y_pred2 = MLJBase.predict(mach, X_test)

@test y_pred == y_pred2

### check that setpropertyname and getpropertyname work
Base.getproperty(balanced_model, :b1) == balancer1
Base.setproperty!(balanced_model, :b1, balancer2)
Base.getproperty(balanced_model, :b1) == balancer2
@test_throws(
MLJBalancing.ERR_NO_PROP,
Base.setproperty!(balanced_model, :name11, balancer2),
Base.setproperty!(balanced_model, :name11, balancer2),
)
end

Expand All @@ -96,7 +96,7 @@ end
## setup parameters
R = Random.MersenneTwister(42)
LogisticClassifier = @load LogisticClassifier pkg = MLJLinearModels verbosity = 0
balancer1 = Imbalance.MLJ.RandomOversampler(ratios = 1.0, rng = Random.MersenneTwister(42))
balancer1 = Imbalance.MLJ.RandomOversampler(ratios = 1.0, rng = Random.MersenneTwister(42))
model = LogisticClassifier()
BalancedModel(model=model, balancer1=balancer1) == BalancedModel(model; balancer1=balancer1)

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