Remove upstream tests for predict

test/mcmc/utilities.jl

@@ -1,145 +1,9 @@
 module MCMCUtilitiesTests
 
 using ..Models: gdemo_default
-using Distributions: Normal, sample, truncated
-using LinearAlgebra: I, vec
-using Random: Random
-using Random: MersenneTwister
 using Test: @test, @testset
 using Turing
 
-@testset "predict" begin
-    Random.seed!(100)
-
-    @model function linear_reg(x, y, σ=0.1)
-        β ~ Normal(0, 1)
-
-        for i in eachindex(y)
-            y[i] ~ Normal(β * x[i], σ)
-        end
-    end
-
-    @model function linear_reg_vec(x, y, σ=0.1)
-        β ~ Normal(0, 1)
-        return y ~ MvNormal(β .* x, σ^2 * I)
-    end
-
-    f(x) = 2 * x + 0.1 * randn()
-
-    Δ = 0.1
-    xs_train = 0:Δ:10
-    ys_train = f.(xs_train)
-    xs_test = [10 + Δ, 10 + 2 * Δ]
-    ys_test = f.(xs_test)
-
-    # Infer
-    m_lin_reg = linear_reg(xs_train, ys_train)
-    chain_lin_reg = sample(m_lin_reg, NUTS(100, 0.65), 200)
-
-    # Predict on two last indices
-    m_lin_reg_test = linear_reg(xs_test, fill(missing, length(ys_test)))
-    predictions = Turing.Inference.predict(m_lin_reg_test, chain_lin_reg)
-
-    ys_pred = vec(mean(Array(group(predictions, :y)); dims=1))
-
-    @test sum(abs2, ys_test - ys_pred) ≤ 0.1
-
-    # Ensure that `rng` is respected
-    predictions1 = let rng = MersenneTwister(42)
-        predict(rng, m_lin_reg_test, chain_lin_reg[1:2])
-    end
-    predictions2 = let rng = MersenneTwister(42)
-        predict(rng, m_lin_reg_test, chain_lin_reg[1:2])
-    end
-    @test all(Array(predictions1) .== Array(predictions2))
-
-    # Predict on two last indices for vectorized
-    m_lin_reg_test = linear_reg_vec(xs_test, missing)
-    predictions_vec = Turing.Inference.predict(m_lin_reg_test, chain_lin_reg)
-    ys_pred_vec = vec(mean(Array(group(predictions_vec, :y)); dims=1))
-
-    @test sum(abs2, ys_test - ys_pred_vec) ≤ 0.1
-
-    # Multiple chains
-    chain_lin_reg = sample(m_lin_reg, NUTS(100, 0.65), MCMCThreads(), 200, 2)
-    m_lin_reg_test = linear_reg(xs_test, fill(missing, length(ys_test)))
-    predictions = Turing.Inference.predict(m_lin_reg_test, chain_lin_reg)
-
-    @test size(chain_lin_reg, 3) == size(predictions, 3)
-
-    for chain_idx in MCMCChains.chains(chain_lin_reg)
-        ys_pred = vec(mean(Array(group(predictions[:, :, chain_idx], :y)); dims=1))
-        @test sum(abs2, ys_test - ys_pred) ≤ 0.1
-    end
-
-    # Predict on two last indices for vectorized
-    m_lin_reg_test = linear_reg_vec(xs_test, missing)
-    predictions_vec = Turing.Inference.predict(m_lin_reg_test, chain_lin_reg)
-
-    for chain_idx in MCMCChains.chains(chain_lin_reg)
-        ys_pred_vec = vec(mean(Array(group(predictions_vec[:, :, chain_idx], :y)); dims=1))
-        @test sum(abs2, ys_test - ys_pred_vec) ≤ 0.1
-    end
-
-    # https://github.com/TuringLang/Turing.jl/issues/1352
-    @model function simple_linear1(x, y)
-        intercept ~ Normal(0, 1)
-        coef ~ MvNormal(zeros(2), I)
-        coef = reshape(coef, 1, size(x, 1))
-
-        mu = vec(intercept .+ coef * x)
-        error ~ truncated(Normal(0, 1), 0, Inf)
-        return y ~ MvNormal(mu, error^2 * I)
-    end
-
-    @model function simple_linear2(x, y)
-        intercept ~ Normal(0, 1)
-        coef ~ filldist(Normal(0, 1), 2)
-        coef = reshape(coef, 1, size(x, 1))
-
-        mu = vec(intercept .+ coef * x)
-        error ~ truncated(Normal(0, 1), 0, Inf)
-        return y ~ MvNormal(mu, error^2 * I)
-    end
-
-    @model function simple_linear3(x, y)
-        intercept ~ Normal(0, 1)
-        coef = Vector(undef, 2)
-        for i in axes(coef, 1)
-            coef[i] ~ Normal(0, 1)
-        end
-        coef = reshape(coef, 1, size(x, 1))
-
-        mu = vec(intercept .+ coef * x)
-        error ~ truncated(Normal(0, 1), 0, Inf)
-        return y ~ MvNormal(mu, error^2 * I)
-    end
-
-    @model function simple_linear4(x, y)
-        intercept ~ Normal(0, 1)
-        coef1 ~ Normal(0, 1)
-        coef2 ~ Normal(0, 1)
-        coef = [coef1, coef2]
-        coef = reshape(coef, 1, size(x, 1))
-
-        mu = vec(intercept .+ coef * x)
-        error ~ truncated(Normal(0, 1), 0, Inf)
-        return y ~ MvNormal(mu, error^2 * I)
-    end
-
-    # Some data
-    x = randn(2, 100)
-    y = [1 + 2 * a + 3 * b for (a, b) in eachcol(x)]
-
-    for model in [simple_linear1, simple_linear2, simple_linear3, simple_linear4]
-        m = model(x, y)
-        chain = sample(m, NUTS(), 100)
-        chain_predict = predict(model(x, missing), chain)
-        mean_prediction = [mean(chain_predict["y[$i]"].data) for i in 1:length(y)]
-        @test mean(abs2, mean_prediction - y) ≤ 1e-3
-    end
-end
-
 @testset "Timer" begin
     chain = sample(gdemo_default, MH(), 1000)