Fix broken latentdiffmodel test and switch off progress reports in te…

…st sampling (#153)

* Fix difflatentmodel test

* switch `sample` to silent progress

EpiAware/test/test_autoregressive.jl

@@ -73,7 +73,7 @@ end
     fixed_model = fix(model, (σ_AR = σ_AR, damp_AR = damp, ar_init = ar_init))
 
     n_samples = 100
-    samples = sample(fixed_model, Prior(), n_samples) |>
+    samples = sample(fixed_model, Prior(), n_samples; progress = false) |>
               chn -> mapreduce(vcat, generated_quantities(fixed_model, chn)) do gen
         gen[1]
     end

EpiAware/test/test_difflatentmodel.jl

@@ -38,7 +38,7 @@ end
         latent_model, (σ_RW = 0, rw_init = 0.0))
 
     n_samples = 2000
-    samples = sample(fixed_model, Prior(), n_samples) |>
+    samples = sample(fixed_model, Prior(), n_samples; progress = false) |>
               chn -> mapreduce(hcat, generated_quantities(fixed_model, chn)) do gen
         gen[1]
     end
@@ -56,7 +56,8 @@ end
     end
 
     #Plus day two distribution
-    day2_dist = foldl((x, y) -> _add_normals(x, init_priors[1]), 1:d, init = init_priors[2])
+    day2_dist = _add_normals(
+        Normal(d * init_priors[1].μ, d * init_priors[1].σ), init_priors[2])
 
     ks_test_pval_day1 = ExactOneSampleKSTest(samples[1, :], init_priors[1]) |> pvalue
     ks_test_pval_day2 = ExactOneSampleKSTest(samples[2, :], day2_dist) |> pvalue
@@ -81,7 +82,7 @@ end
         (latent_init = [0.0, 1.0], σ_AR = 1.0, damp_AR = [0.8], ar_init = [0.0]))
 
     n_samples = 100
-    samples = sample(fixed_model, Prior(), n_samples) |>
+    samples = sample(fixed_model, Prior(), n_samples; progress = false) |>
               chn -> mapreduce(hcat, generated_quantities(fixed_model, chn)) do gen
         gen[1]
     end

EpiAware/test/test_epi-models.jl

@@ -69,7 +69,8 @@ end
     rt = [log(recent_incidence[1]) - log_init; diff(log.(recent_incidence))]
 
     #Check log_init is sampled from the correct distribution
-    sample_init_inc = sample(generate_latent_infs(rt_model, rt), Prior(), 1000) |>
+    sample_init_inc = sample(
+        generate_latent_infs(rt_model, rt), Prior(), 1000; progress = false) |>
                       chn -> chn[:init_incidence] |>
                              Array |>
                              vec
@@ -100,7 +101,7 @@ end
     #Check log_init is sampled from the correct distribution
     sample_init_inc = sample(
         generate_latent_infs(direct_inf_model, log_incidence),
-        Prior(), 1000) |>
+        Prior(), 1000; progress = false) |>
                       chn -> chn[:init_incidence] |>
                              Array |>
                              vec
@@ -143,7 +144,7 @@ end
 
     #Check log_init is sampled from the correct distribution
     @time sample_init_inc = sample(generate_latent_infs(renewal_model, log_Rt),
-        Prior(), 1000) |>
+        Prior(), 1000; progress = false) |>
                             chn -> chn[:init_incidence] |>
                                    Array |>
                                    vec

EpiAware/test/test_models.jl

@@ -68,7 +68,7 @@ end
         latent_model = rwp,
         observation_model = obs_model)
 
-    chn = sample(test_mdl, Prior(), 1000)
+    chn = sample(test_mdl, Prior(), 1000; progress = false)
     gens = generated_quantities(test_mdl, chn)
 
     #Check model sampled
@@ -108,7 +108,7 @@ end
         observation_model = obs_model
     )
 
-    chn = sample(test_mdl, Prior(), 1000)
+    chn = sample(test_mdl, Prior(), 1000; progress = false)
     gens = generated_quantities(test_mdl, chn)
 
     #Check model sampled

EpiAware/test/test_observation-models.jl

@@ -21,7 +21,7 @@
     fix_mdl = fix(mdl, (neg_bin_cluster_factor = neg_bin_cf,))
 
     n_samples = 1000
-    first_obs = sample(fix_mdl, Prior(), n_samples) |>
+    first_obs = sample(fix_mdl, Prior(), n_samples; progress = false) |>
                 chn -> generated_quantities(fix_mdl, chn) .|>
                        (gen -> gen[1][1]) |>
                        vec
@@ -64,7 +64,7 @@ end
         @testset "$scenario_name y_t" begin
             mdl = generate_observations(
                 delay_obs, y_t_scenario, I_t)
-            sampled_obs = sample(mdl, Prior(), 1000) |>
+            sampled_obs = sample(mdl, Prior(), 1000; progress = false) |>
                           chn -> generated_quantities(mdl, chn) .|>
                                  (gen -> gen[1]) |>
                                  collect

EpiAware/test/test_randomwalk.jl

@@ -9,7 +9,7 @@
     model = generate_latent(rw_process, n)
     fixed_model = fix(model, (σ_RW = 1.0, init_rw_value = 0.0)) #Fixing the standard deviation of the random walk process
     n_samples = 1000
-    samples_day_5 = sample(fixed_model, Prior(), n_samples) |>
+    samples_day_5 = sample(fixed_model, Prior(), n_samples; progress = false) |>
                     chn -> mapreduce(vcat, generated_quantities(fixed_model, chn)) do gen
         gen[1][5] #Extracting day 5 samples
     end