Merge pull request #46 from ilabcode/dev

Updated package to 3.0

Author: PTWaade

.github/workflows/CI.yml renamed to .github/workflows/CI_full.yml

@@ -1,29 +1,30 @@
-name: CI
+name: CI_full
 
 on:
   push:
     branches:
       - main
-      - dev
     tags: '*'
   pull_request:
+    branches:
+      - main
   workflow_dispatch:
 
-concurrency:
-  # Skip intermediate builds: always.
-  # Cancel intermediate builds: only if it is a pull request build.
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
 
 jobs:
   test:
     name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
     runs-on: ${{ matrix.os }}
+    # This allows a subsequently queued workflow run to interrupt previous runs
+    concurrency:
+      group: "${{ github.workflow }} - ${{ matrix.os }} - ${{ matrix.version }} @ ${{ github.ref }}"
+      cancel-in-progress: true
     strategy:
       fail-fast: false
       matrix:
         version:
           - '1'
+          - 'nightly'
         os:
           - ubuntu-latest
           - macOS-latest

.github/workflows/CI_small.yml

@@ -0,0 +1,43 @@
+name: CI_small
+
+on:
+  push:
+    branches:
+      - dev
+    tags: '*'
+  pull_request:
+    branches:
+      - dev
+  workflow_dispatch:
+
+jobs:
+  test:
+    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
+    runs-on: ${{ matrix.os }}
+    # This allows a subsequently queued workflow run to interrupt previous runs
+    concurrency:
+      group: "${{ github.workflow }} - ${{ matrix.os }} @ ${{ github.ref }}"
+      cancel-in-progress: true
+    strategy:
+      fail-fast: false
+      matrix:
+        version:
+          - '1'
+        os:
+          - ubuntu-latest
+        arch:
+          - x64
+    steps:
+      - uses: actions/checkout@v3
+      - uses: julia-actions/setup-julia@v1
+        with:
+          version: ${{ matrix.version }}
+          arch: ${{ matrix.arch }}
+      - uses: julia-actions/cache@v1
+      - uses: julia-actions/julia-buildpkg@v1
+      - uses: julia-actions/julia-runtest@v1
+      - uses: julia-actions/julia-processcoverage@v1
+      - uses: codecov/codecov-action@v3
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          files: lcov.info

.github/workflows/CompatHelper.yml

@@ -13,4 +13,4 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
-        run: julia -e 'using CompatHelper; CompatHelper.main()'
+        run: julia -e 'using CompatHelper; CompatHelper.main(; master_branch = "dev")'

.github/workflows/automerge_dependabot.yml

@@ -0,0 +1,28 @@
+# GitHub action to automerge dependabot PRs. Only merges if tests pass due to branch protections in the repo.
+
+name: automerge-dependabot-prs
+
+on: pull_request
+
+permissions:
+  contents: write
+  pull-requests: write
+
+jobs:
+  dependabot-automerge:
+    runs-on: ubuntu-latest
+    if: ${{ github.actor == 'dependabot[bot]'}}    
+    
+    steps:
+    # Checkout action is required for token to persist
+    - name: Enable auto-merge for Dependabot PRs
+      run: gh pr merge --auto --merge "$PR_URL" # Use Github CLI to merge automatically the PR
+      env:
+        PR_URL: ${{github.event.pull_request.html_url}}
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+    
+    - name: Auto approve dependabot PRs
+      if: github.actor == 'dependabot[bot]'
+      uses: hmarr/auto-approve-action@v2
+      with: 
+        github-token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/documentation.yml

@@ -4,15 +4,15 @@ on:
   push:
     branches:
       - main
-      - dev
     tags: '*'
   pull_request:
+    branches:
+      - main
 
+# This allows a subsequently queued workflow run to interrupt previous runs
 concurrency:
-  # Skip intermediate builds: always.
-  # Cancel intermediate builds: only if it is a pull request build.
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
+  group: "${{ github.workflow }} @ ${{ github.ref }}"
+  cancel-in-progress: true
 
 jobs:
   docs:

.github/workflows/formatting.yml

@@ -1,17 +1,15 @@
 name = "HierarchicalGaussianFiltering"
 uuid = "63d42c3e-681c-42be-892f-a47f35336a79"
 authors = ["Peter Thestrup Waade [email protected]", "Jacopo Comoglio [email protected]", "Christoph Mathys [email protected]\n and contributors"]
-version = "0.2.3"
+version = "0.3.0"
 
 [deps]
 ActionModels = "320cf53b-cc3b-4b34-9a10-0ecb113566a3"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
-Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
-ActionModels = "0.2"
+ActionModels = "0.3"
 Distributions = "0.25"
 RecipesBase = "1"
-Turing = "0.21, 0.22, 0.23"
 julia = "1.8"

Project.toml

@@ -5,8 +5,8 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 HierarchicalGaussianFiltering = "63d42c3e-681c-42be-892f-a47f35336a79"
+JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
-Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

docs/Project.toml

@@ -4,7 +4,6 @@
 # First load packages
 using ActionModels
 using HierarchicalGaussianFiltering
-using Turing
 using CSV
 using DataFrames
 using Plots
@@ -19,7 +18,7 @@ data_path = hgf_path * "/docs/tutorials/data/"
 inputs = CSV.read(data_path * "classic_binary_inputs.csv", DataFrame)[!, 1];
 
 # Create an HGF
-hgf_params = Dict(
+hgf_parameters = Dict(
     ("u", "category_means") => Real[0.0, 1.0],
     ("u", "input_precision") => Inf,
     ("x2", "evolution_rate") => -2.5,
@@ -31,11 +30,12 @@ hgf_params = Dict(
     ("x1", "x2", "value_coupling") => 1.0,
     ("x2", "x3", "volatility_coupling") => 1.0,
 );
-hgf = premade_hgf("binary_3level", hgf_params, verbose = false);
+hgf = premade_hgf("binary_3level", hgf_parameters, verbose = false);
 
 # Create an agent
-agent_params = Dict("sigmoid_action_precision" => 5);
-agent = premade_agent("hgf_unit_square_sigmoid_action", hgf, agent_params, verbose = false);
+agent_parameters = Dict("sigmoid_action_precision" => 5);
+agent =
+    premade_agent("hgf_unit_square_sigmoid_action", hgf, agent_parameters, verbose = false);
 
 # Evolve agent and save actions
 actions = give_inputs!(agent, inputs);
@@ -48,7 +48,7 @@ plot_trajectory(agent, ("x2", "posterior"))
 plot_trajectory(agent, ("x3", "posterior"))
 
 # Set fixed parameters
-fixed_params = Dict(
+fixed_parameters = Dict(
     "sigmoid_action_precision" => 5,
     ("u", "category_means") => Real[0.0, 1.0],
     ("u", "input_precision") => Inf,
@@ -58,26 +58,31 @@ fixed_params = Dict(
     ("x3", "initial_precision") => 1,
     ("x1", "x2", "value_coupling") => 1.0,
     ("x2", "x3", "volatility_coupling") => 1.0,
-    ("x2", "evolution_rate") => -3.0,
     ("x3", "evolution_rate") => -6.0,
 );
 
 # Set priors for parameter recovery
 param_priors = Dict(("x2", "evolution_rate") => Normal(-3.0, 0.5));
 
 # Prior predictive plot
-plot_predictive_simulation(param_priors, agent, inputs, ("x1", "prediction_mean"), n_simulations = 100)
+plot_predictive_simulation(
+    param_priors,
+    agent,
+    inputs,
+    ("x1", "prediction_mean"),
+    n_simulations = 100,
+)
 
 # Get the actions from the MATLAB tutorial
 actions = CSV.read(data_path * "classic_binary_actions.csv", DataFrame)[!, 1];
 
 # Fit the actions
 fitted_model = fit_model(
     agent,
+    param_priors,
     inputs,
     actions,
-    param_priors,
-    fixed_params,
+    fixed_parameters = fixed_parameters,
     verbose = true,
     n_iterations = 10,
 )
@@ -89,4 +94,10 @@ plot(fitted_model)
 plot_parameter_distribution(fitted_model, param_priors)
 
 # Posterior predictive plot
-plot_predictive_simulation(fitted_model, agent, inputs, ("x1", "prediction_mean"), n_simulations = 3)
+plot_predictive_simulation(
+    fitted_model,
+    agent,
+    inputs,
+    ("x1", "prediction_mean"),
+    n_simulations = 3,
+)

docs/tutorials/classic_binary.jl

@@ -3,7 +3,6 @@
 # First load packages
 using ActionModels
 using HierarchicalGaussianFiltering
-using Turing
 using Plots
 using StatsPlots
 
@@ -38,7 +37,7 @@ parameters = Dict(
     "gaussian_action_precision" => 100,
 );
 
-set_params!(agent, parameters)
+set_parameters!(agent, parameters)
 reset!(agent)
 
 # Evolve agent
@@ -74,11 +73,11 @@ plot_trajectory(
     size = (1300, 500),
     xlims = (0, 615),
     xlabel = "Trading days since 1 January 2010",
-    title = "Volatility parent trajectory"
+    title = "Volatility parent trajectory",
 )
 
-# Set priors for turing fitting
-fixed_params = Dict(
+# Set priors for fitting
+fixed_parameters = Dict(
     ("u", "x1", "value_coupling") => 1.0,
     ("x1", "x2", "volatility_coupling") => 1.0,
     ("x1", "initial_mean") => 0,
@@ -95,15 +94,21 @@ param_priors = Dict(
 );
 
 # Prior predictive simulation plot
-plot_predictive_simulation(param_priors, agent, inputs, ("x1", "posterior_mean"); n_simulations = 3)
+plot_predictive_simulation(
+    param_priors,
+    agent,
+    inputs,
+    ("x1", "posterior_mean");
+    n_simulations = 3,
+)
 
 # Do parameter recovery
 fitted_model = fit_model(
     agent,
+    param_priors,
     inputs,
     actions,
-    param_priors,
-    fixed_params,
+    fixed_parameters = fixed_parameters,
     verbose = true,
     n_iterations = 10,
 )