From 8c05d039f7c99eaf1c96149d92c9b925077f1fcf Mon Sep 17 00:00:00 2001 From: Samuel Brand <48288458+SamuelBrand1@users.noreply.github.com> Date: Mon, 9 Dec 2024 15:32:09 +0000 Subject: [PATCH] Issue 537: Fix making analysis dataframes and record priors (#535) * Update changelog.md * Update make_model_priors.jl * Make `define_epiprob` more modular _make_epidata can also be reused elsewhere * restructure analysis dataframe func * fix make_prediction_dataframe_from_output And add simple unit test with committed test data * Revert "fix make_prediction_dataframe_from_output" This reverts commit 7e6238bc275a79381b318204527748754932657d. * Reapply "fix make_prediction_dataframe_from_output" This reverts commit 14d29b42608132bc8603daf9f16926251677da45. --- .gitignore | 3 + manuscript/changelog.md | 34 +++++++ .../make_prediction_dataframe_from_output.jl | 86 ++++++++++-------- .../src/constructors/make_model_priors.jl | 14 +-- pipeline/src/infer/define_epiprob.jl | 33 +++++-- pipeline/test/Project.toml | 2 + .../make_prediction_dataframe_from_output.jl | 20 ++++ pipeline/test/analysis/test_analysis.jl | 1 + pipeline/test/analysis/test_data.jld2 | Bin 0 -> 463435 bytes 9 files changed, 141 insertions(+), 52 deletions(-) create mode 100644 pipeline/test/analysis/make_prediction_dataframe_from_output.jl create mode 100644 pipeline/test/analysis/test_analysis.jl create mode 100644 pipeline/test/analysis/test_data.jld2 diff --git a/.gitignore b/.gitignore index 58790f702..17022d4a5 100644 --- a/.gitignore +++ b/.gitignore @@ -393,3 +393,6 @@ EpiAware/docs/src/getting-started/tutorials/censored-obs.md EpiAware/docs/Manifest*.toml !benchmark/Manifest.toml + +# Test data +!pipeline/test/analysis/test_data.jld2 diff --git a/manuscript/changelog.md b/manuscript/changelog.md index ef4eb686f..2dd1de5f7 100644 --- a/manuscript/changelog.md +++ b/manuscript/changelog.md @@ -2,3 +2,37 @@ ## 16th Aug 2024 Changed the proposed latent processes to daily rather than weekly incremental changes. + +## 5th Dec 2024 +Changed priors to + +```julia +""" +Constructs and returns a dictionary of prior distributions for the latent model +parameters. This is the default method. + +# Arguments +- `pipeline`: An instance of the `AbstractEpiAwarePipeline` type. + +# Returns +A dictionary containing the following prior distributions: +- `"transformed_process_init_prior"`: A normal distribution with mean 0.0 and +standard deviation 0.25. +- `"std_prior"`: A half-normal distribution with standard deviation 0.25. +- `"damp_param_prior"`: A beta distribution with shape parameters 0.5 and 0.5. + +""" +function make_model_priors(pipeline::AbstractEpiAwarePipeline) + transformed_process_init_prior = Normal(0.0, 0.25) + std_prior = HalfNormal(0.025) + damp_param_prior = Beta(1, 9) + log_I0_prior = Normal(log(100.0), 1e-1) + + return Dict( + "transformed_process_init_prior" => transformed_process_init_prior, + "std_prior" => std_prior, + "damp_param_prior" => damp_param_prior, + "log_I0_prior" => log_I0_prior + ) +end +``` diff --git a/pipeline/src/analysis/make_prediction_dataframe_from_output.jl b/pipeline/src/analysis/make_prediction_dataframe_from_output.jl index 8abbed5c5..c1980a7cd 100644 --- a/pipeline/src/analysis/make_prediction_dataframe_from_output.jl +++ b/pipeline/src/analysis/make_prediction_dataframe_from_output.jl @@ -3,7 +3,9 @@ Create a dataframe containing prediction results based on the given output and i # Arguments - `filename`: The name of the file. -- `output`: The output data containing inference configuration, IGP model, and other information. +- `output`: The output data containing inference configuration, IGP model, and other +using Base: infer_effects + information. - `epi_datas`: The input data for the epidemiological model. - `qs`: An optional array of quantiles to calculate. Default is `[0.025, 0.5, 0.975]`. @@ -12,53 +14,57 @@ A dataframe containing the prediction results. """ function make_prediction_dataframe_from_output( - filename, output, epi_datas, pipelines; qs = [0.025, 0.5, 0.975]) - #Get the scenario, IGP model, latent model and true mean GI + output, true_mean_gi; qs = [0.025, 0.25, 0.5, 0.75, 0.975], + transformation = oneexpy) + #Unpack the output inference_config = output["inference_config"] - igp_model = output["inference_config"].igp |> string - scenario = EpiAwarePipeline._get_scenario_from_filename(filename, pipelines) - latent_model = EpiAwarePipeline._get_latent_model_from_filename(filename) - true_mean_gi = EpiAwarePipeline._get_true_gi_mean_from_filename(filename) + forecasts = output["forecast_results"] + #Get the scenario, IGP model, latent model and true mean GI + igp_model = inference_config["igp"] |> igp_name -> split(igp_name, ".")[end] + scenario = inference_config["scenario"] + latent_model = inference_config["latent_model"] + used_gi_mean = inference_config["gi_mean"] + used_gi_std = inference_config["gi_std"] + (start_time, reference_time) = inference_config["tspan"] |> + tspan -> split(tspan, "_") |> + tspan -> ( + parse(Int, tspan[1]), parse(Int, tspan[2])) #Get the quantiles for the targets across the gi mean scenarios #if Renewal model, then we use the underlying epi model #otherwise we use the epi datas to loop over different gi mean implications - used_epi_datas = igp_model == "Renewal" ? [output["epiprob"].epi_model.data] : epi_datas + used_gi_means = igp_model == "Renewal" ? + [used_gi_mean] : + make_gi_params(EpiAwareExamplePipeline())["gi_means"] - preds = nothing - try - preds = map(used_epi_datas) do epi_data - generate_quantiles_for_targets(output, epi_data, qs) - end - used_gi_means = igp_model == "Renewal" ? - [EpiAwarePipeline._get_used_gi_mean_from_filename(filename)] : - make_gi_params(EpiAwareExamplePipeline())["gi_means"] + used_epidatas = map(used_gi_means) do ḡ + _make_epidata(ḡ, used_gi_std; transformation = transformation) + end + + preds = map(used_epidatas) do epi_data + generate_quantiles_for_targets(forecasts, epi_data, qs) + end - #Create the dataframe columnwise - df = mapreduce(vcat, preds, used_gi_means) do pred, used_gi_mean - mapreduce(vcat, keys(pred)) do target - target_mat = pred[target] - target_times = collect(1:size(target_mat, 1)) .+ - (inference_config.tspan[1] - 1) - _df = DataFrame(target_times = target_times) - _df[!, "Scenario"] .= scenario - _df[!, "IGP_Model"] .= igp_model - _df[!, "Latent_Model"] .= latent_model - _df[!, "True_GI_Mean"] .= true_mean_gi - _df[!, "Used_GI_Mean"] .= used_gi_mean - _df[!, "Reference_Time"] .= inference_config.tspan[2] - _df[!, "Target"] .= string(target) - # quantile predictions - for (j, q) in enumerate(qs) - q_str = split(string(q), ".")[end] - _df[!, "q_$(q_str)"] = target_mat[:, j] - end - return _df + #Create the dataframe columnwise + df = mapreduce(vcat, preds, used_gi_means) do pred, used_gi_mean + mapreduce(vcat, keys(pred)) do target + target_mat = pred[target] + target_times = collect(1:size(target_mat, 1)) .+ (start_time - 1) + _df = DataFrame(target_times = target_times) + _df[!, "Scenario"] .= scenario + _df[!, "IGP_Model"] .= igp_model + _df[!, "Latent_Model"] .= latent_model + _df[!, "True_GI_Mean"] .= true_mean_gi + _df[!, "Used_GI_Mean"] .= used_gi_mean + _df[!, "Reference_Time"] .= reference_time + _df[!, "Target"] .= string(target) + # quantile predictions + for (j, q) in enumerate(qs) + q_str = split(string(q), ".")[end] + _df[!, "q_$(q_str)"] = target_mat[:, j] end + return _df end - return df - catch - @warn "Error in generating quantiles for targets in file $filename" - return nothing end + return df end diff --git a/pipeline/src/constructors/make_model_priors.jl b/pipeline/src/constructors/make_model_priors.jl index 01348e7e6..88a90cf17 100644 --- a/pipeline/src/constructors/make_model_priors.jl +++ b/pipeline/src/constructors/make_model_priors.jl @@ -8,16 +8,18 @@ parameters. This is the default method. # Returns A dictionary containing the following prior distributions: - `"transformed_process_init_prior"`: A normal distribution with mean 0.0 and -standard deviation 0.25. -- `"std_prior"`: A half-normal distribution with standard deviation 0.25. -- `"damp_param_prior"`: A beta distribution with shape parameters 0.5 and 0.5. +standard deviation 0.025. +- `"std_prior"`: A half-normal distribution with standard deviation 0.025. +- `"damp_param_prior"`: A beta distribution with shape parameters 1 and 9. +- `"log_I0_prior"`: A normal distribution with mean log(100.0) and standard +deviation 1e-1. """ function make_model_priors(pipeline::AbstractEpiAwarePipeline) transformed_process_init_prior = Normal(0.0, 0.25) - std_prior = HalfNormal(0.25) - damp_param_prior = Beta(0.5, 0.5) - log_I0_prior = Normal(log(100.0), 1e-5) + std_prior = HalfNormal(0.025) + damp_param_prior = Beta(1, 9) + log_I0_prior = Normal(log(100.0), 1e-1) return Dict( "transformed_process_init_prior" => transformed_process_init_prior, diff --git a/pipeline/src/infer/define_epiprob.jl b/pipeline/src/infer/define_epiprob.jl index 4290556db..3a574e368 100644 --- a/pipeline/src/infer/define_epiprob.jl +++ b/pipeline/src/infer/define_epiprob.jl @@ -1,3 +1,28 @@ +""" +Create an `EpiData` object based on the provided generation interval mean and standard +deviation by assuming a gamma distribution. + +# Arguments +- `gi_mean::Float64`: Mean of the generation interval. +- `gi_std::Float64`: Standard deviation of the generation interval. +- `transformation`: A transformation function to be applied + (default is `EpiAwarePipeline.oneexpy` a custom implementation of `exp`). + +# Returns +- `model_data::EpiData`: An `EpiData` object containing the generation interval distribution + and transformation. + +""" +function _make_epidata(gi_mean, gi_std; transformation = EpiAwarePipeline.oneexpy) + shape = (gi_mean / gi_std)^2 + scale = gi_std^2 / gi_mean + gen_distribution = Gamma(shape, scale) + + model_data = EpiData( + gen_distribution = gen_distribution, transformation = transformation) + return model_data +end + """ Create an `EpiProblem` object based on the provided `InferenceConfig`. @@ -9,12 +34,8 @@ Create an `EpiProblem` object based on the provided `InferenceConfig`. """ function define_epiprob(config::InferenceConfig) - shape = (config.gi_mean / config.gi_std)^2 - scale = config.gi_std^2 / config.gi_mean - gen_distribution = Gamma(shape, scale) - - model_data = EpiData( - gen_distribution = gen_distribution, transformation = config.transformation) + model_data = _make_epidata( + config.gi_mean, config.gi_std; transformation = config.transformation) #Build the epidemiological model epi = config.igp(data = model_data, initialisation_prior = config.log_I0_prior) diff --git a/pipeline/test/Project.toml b/pipeline/test/Project.toml index 61209072e..6a4164ae3 100644 --- a/pipeline/test/Project.toml +++ b/pipeline/test/Project.toml @@ -2,9 +2,11 @@ ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b" AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001" CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" +DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964" Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1" EpiAware = "b2eeebe4-5992-4301-9193-7ebc9f62c855" +JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688" MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d" RCall = "6f49c342-dc21-5d91-9882-a32aef131414" diff --git a/pipeline/test/analysis/make_prediction_dataframe_from_output.jl b/pipeline/test/analysis/make_prediction_dataframe_from_output.jl new file mode 100644 index 000000000..a4c015172 --- /dev/null +++ b/pipeline/test/analysis/make_prediction_dataframe_from_output.jl @@ -0,0 +1,20 @@ +@testset "test dataframe construct for one dataset" begin + using JLD2, DataFramesMeta + output = load(joinpath(@__DIR__(), "test_data.jld2")) + true_mean_gi = 10.0 + + df = make_prediction_dataframe_from_output(output, true_mean_gi) + @test !isempty(df) + @test "Scenario" in names(df) + @test "IGP_Model" in names(df) + @test "Latent_Model" in names(df) + @test "True_GI_Mean" in names(df) + @test "Used_GI_Mean" 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