Issue 51: Handle flat targets for GP fit

src/make_and_fit_model.jl

@@ -1,5 +1,33 @@
 """
-    make_and_fit_model(data; n_particles=8, smc_data_proportion=0.1, n_mcmc=200, n_hmc=50, kwargs...)
+    _stabilize_for_fit(y; flat_threshold=1.0e-3)
+
+Guard against a degenerate (near-constant) transformed series before fitting.
+
+Gaussian process fitting needs the data to have some spread: `AutoGP` rescales `y`
+by its range, so a genuinely flat series makes the covariance singular
+(`PosDefException`, issue #51). When the relative range of `y`
+(`(max - min) / (|mean| + 1)`) falls below `flat_threshold`, add small Gaussian
+jitter so the series is fittable; otherwise return `y` unchanged. Operates on the
+(already transformed) values, so it is independent of which transformation was used.
+
+This is a secondary safety net for data that is flat even after transformation. The
+common case of a pathological Box-Cox λ on otherwise-fittable data is handled earlier,
+in [`get_transformations`](@ref), by falling back to a log transformation.
+"""
+function _stabilize_for_fit(y::AbstractVector{<:Real}; flat_threshold = 1.0e-3)
+    n = length(y)
+    n > 1 || return y
+    scale = abs(sum(y) / n) + 1
+    rel_range = (maximum(y) - minimum(y)) / scale
+    rel_range >= flat_threshold && return y  # enough spread → leave untouched
+    σ = flat_threshold * scale
+    @warn "Near-constant series (relative range $rel_range < $flat_threshold); adding \
+jitter (σ = $σ) so the GP covariance stays positive-definite (issue #51)."
+    return y .+ σ .* randn(n)
+end
+
+"""
+    make_and_fit_model(data; n_particles=8, smc_data_proportion=0.1, n_mcmc=200, n_hmc=50, flat_threshold=1.0e-3, kwargs...)
 
 Create and fit a Gaussian Process (GP) model using Sequential Monte Carlo (SMC) sampling.
 
@@ -9,17 +37,21 @@ Create and fit a Gaussian Process (GP) model using Sequential Monte Carlo (SMC)
 - `smc_data_proportion`: The proportion of the data to use in each SMC step (default: 0.1).
 - `n_mcmc`: The number of MCMC samples (default: 200).
 - `n_hmc`: The number of HMC samples (default: 50).
+- `flat_threshold`: If the (transformed) target's relative range is below this, small
+  Gaussian jitter is added so the GP covariance stays positive-definite (default: 1.0e-3).
+  Series with enough spread are left untouched, preserving existing behaviour.
 - `kwargs...`: Additional keyword arguments to pass to the `AutoGP.fit_smc!` function.
 
 # Returns
 - `model`: The fitted GP model.
 """
 function make_and_fit_model(
         data::TData; n_particles = 8, smc_data_proportion = 0.1,
-        n_mcmc = 200, n_hmc = 50, kwargs...
+        n_mcmc = 200, n_hmc = 50, flat_threshold = 1.0e-3, kwargs...
     )
     n_train = length(data.y)
-    model = AutoGP.GPModel(data.ds, data.y; n_particles = n_particles)
+    y_fit = _stabilize_for_fit(data.y; flat_threshold = flat_threshold)
+    model = AutoGP.GPModel(data.ds, y_fit; n_particles = n_particles)
     # Ensure smc_data_proportion results in at least step=1 for the schedule
     effective_proportion = max(smc_data_proportion, 1.0 / n_train)
     schedule = AutoGP.Schedule.linear_schedule(n_train, effective_proportion)

src/transformations.jl

@@ -100,6 +100,10 @@ A tuple `(forward_transform, inverse_transform)` where:
 - **Forward**: `y ↦ BoxCox_λ(y + offset)` where λ is automatically fitted
 - **Inverse**: Custom inverse function handling edge cases for numerical stability
 - **Note**: Automatically determines optimal λ parameter via maximum likelihood
+- **Fallback**: On near-constant data the Box-Cox MLE can return a pathological λ that
+  collapses the transform to a constant (singular GP covariance, issue #51). When the
+  transformed spread degenerates relative to a plain log, this falls back to the
+  `"positive"` (log) transformation and emits a warning.
 
 # Offset Calculation
 An offset is automatically calculated using `_get_offet(values)`:
@@ -146,8 +150,22 @@ function get_transformations(
         return (y -> log(y + offset), y -> max(exp(y) - offset, zero(F)))
     elseif transform_name == "boxcox"
         max_values = maximum(values)
-        bc = fit(BoxCoxTransformation, values .+ offset) # Fit Box-Cox transformation
+        shifted = values .+ offset
+        bc = fit(BoxCoxTransformation, shifted) # Fit Box-Cox transformation
         λ = bc.λ
+        transformed = bc.(shifted)
+        bc_range = maximum(transformed) - minimum(transformed)
+        log_range = maximum(log, shifted) - minimum(log, shifted)
+        # On near-constant data the Box-Cox MLE can pick a pathological λ (e.g.
+        # λ ≈ -178 for large counts), which underflows the transform to a constant
+        # and makes the GP covariance singular (issue #51). When the Box-Cox spread
+        # collapses relative to a plain log, fall back to the well-behaved "positive"
+        # (log) transformation, which fits this data fine.
+        if !all(isfinite, transformed) || bc_range <= 1.0e-2 * log_range
+            @warn "Box-Cox transformation degenerate (λ = $λ, transformed range = \
+$bc_range); falling back to log transformation (issue #51)."
+            return get_transformations("positive", values)
+        end
         @info "Using Box-Cox transformation with λ = $λ and offset = $offset"
         return (y -> bc(y + offset), _inv_boxcox(λ, offset, max_values))
     else

test/test_helper_functions.jl

@@ -143,6 +143,27 @@ end
     end
 end
 
+@testitem "get_transformations BoxCox Fallback on Flat Data" setup = [DataSnippet] begin
+    # Near-constant data makes the Box-Cox MLE pick a pathological λ that collapses the
+    # transform; get_transformations falls back to log instead (issue #51).
+    flat_values = [
+        75000.0, 75100.0, 74950.0, 75050.0, 75000.0,
+        74980.0, 75020.0, 75010.0, 74990.0, 75005.0,
+    ]
+    forward_transform, inverse_transform = get_transformations("boxcox", flat_values)
+
+    # Fallback ⟹ forward transform is log (offset = 0 for these positive values).
+    @test forward_transform(flat_values[1]) ≈ log(flat_values[1]) rtol = 1.0e-9
+    # Round-trip still holds on the original scale.
+    for val in flat_values
+        @test inverse_transform(forward_transform(val)) ≈ val rtol = 1.0e-9
+    end
+
+    # Healthy, well-spread data should NOT fall back (genuine Box-Cox, not log).
+    healthy_forward, _ = get_transformations("boxcox", values)
+    @test !isapprox(healthy_forward(values[1]), log(values[1]); rtol = 1.0e-9)
+end
+
 @testitem "get_transformations Percentage with data with zeros" setup = [DataSnippet] begin
     forward_transform,
         inverse_transform = get_transformations("percentage", values_with_zero)

test/test_model_fitting.jl

@@ -78,3 +78,61 @@ end
     model = make_and_fit_model(data; smc_data_proportion = 0.05, minimal_params...)
     @test model isa AutoGP.GPModel
 end
+
+@testsnippet FlatData begin
+    using AutoGP, Dates, Random
+
+    flat_dates = collect(Date(2024, 1, 1):Day(1):Date(2024, 1, 10))
+    # Near-constant counts: the Box-Cox MLE picks a pathological λ here (issue #51).
+    flat_values = [
+        75000.0, 75100.0, 74950.0, 75050.0, 75000.0,
+        74980.0, 75020.0, 75010.0, 74990.0, 75005.0,
+    ]
+    # Exactly constant: degenerate even after any monotonic transform.
+    const_values = fill(75000.0, length(flat_dates))
+    flat_forecast_dates = collect(Date(2024, 1, 11):Day(1):Date(2024, 1, 18))
+    minimal_params = (n_particles = 1, n_mcmc = 5, n_hmc = 3)
+end
+
+@testitem "make_and_fit_model+forecast Box-Cox-degenerate flat data (issue #51)" setup = [FlatData] begin
+    # Regression: this previously threw PosDefException at forecast time.
+    Random.seed!(51)
+    transformation, inv_transformation = get_transformations("boxcox", flat_values)
+    data = create_transformed_data(flat_dates, flat_values; transformation)
+    model = make_and_fit_model(data; smc_data_proportion = 0.5, minimal_params...)
+    @test model isa AutoGP.GPModel
+
+    fc = forecast(model, flat_forecast_dates, 25; inv_transformation = inv_transformation)
+    @test size(fc) == (length(flat_forecast_dates), 25)
+    @test all(isfinite, fc)
+    @test all(fc .>= 0)
+    @test 50_000 < sum(fc) / length(fc) < 100_000  # forecasts stay near ~75,000
+end
+
+@testitem "make_and_fit_model+forecast exactly constant data (issue #51)" setup = [FlatData] begin
+    # Genuinely flat data is rescued by the jitter safety net in make_and_fit_model.
+    Random.seed!(52)
+    transformation, inv_transformation = get_transformations("boxcox", const_values)
+    data = create_transformed_data(flat_dates, const_values; transformation)
+    model = make_and_fit_model(data; smc_data_proportion = 0.5, minimal_params...)
+    @test model isa AutoGP.GPModel
+
+    fc = forecast(model, flat_forecast_dates, 25; inv_transformation = inv_transformation)
+    @test size(fc) == (length(flat_forecast_dates), 25)
+    @test all(isfinite, fc)
+    @test all(fc .>= 0)
+end
+
+@testitem "_stabilize_for_fit leaves healthy data unchanged" setup = [FlatData] begin
+    healthy = [10.0, 15.0, 12.0, 18.0, 22.0, 25.0, 20.0, 16.0, 14.0, 11.0]
+    @test NowcastAutoGP._stabilize_for_fit(healthy) === healthy
+end
+
+@testitem "_stabilize_for_fit jitters near-constant data" setup = [FlatData] begin
+    Random.seed!(1)
+    flat = fill(11.2256, 30)
+    jittered = NowcastAutoGP._stabilize_for_fit(flat)
+    @test jittered != flat
+    rel_range = (maximum(jittered) - minimum(jittered)) / (abs(sum(jittered) / length(jittered)) + 1)
+    @test rel_range >= 1.0e-3
+end