new version

Project.toml

@@ -4,7 +4,7 @@ authors = [ "Peter Thestrup Waade [email protected]",
             "Anna Hedvig Møller [email protected]",
             "Jacopo Comoglio [email protected]",
             "Christoph Mathys [email protected]"]
-version = "0.5.4"
+version = "0.5.5"
 
 
 [deps]
@@ -13,7 +13,7 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 
 [compat]
-ActionModels = "0.5"
+ActionModels = "0.6"
 Distributions = "0.25"
 RecipesBase = "1"
 julia = "1.10"

README.md

@@ -94,7 +94,12 @@ plot_trajectory!(agent, ("x", "prediction"))
 using Distributions
 prior = Dict(("xprob", "volatility") => Normal(1, 0.5))
 
-model = fit_model(agent, prior, inputs, actions, n_iterations = 20)
+#Create model
+model = create_model(agent, prior, inputs, actions;)
+
+#Fit single chain with 10 iterations
+fitted_model = fit_model(model; n_iterations = 10, n_chains = 1)
+
 ````
 ![Image1](docs/src/images/readme/fit_model.png)
 ### Plot chains
@@ -106,7 +111,7 @@ plot(model)
 ### Plot prior angainst posterior
 
 ````@example index
-plot_parameter_distribution(model, prior)
+# plot_parameter_distribution(model, prior)
 ````
 ![Image1](docs/src/images/readme/prior_posterior.png)
 ### Get posterior

docs/julia_files/index.jl

@@ -55,7 +55,11 @@ plot_trajectory!(agent, ("xbin", "prediction"))
 using Distributions
 prior = Dict(("xprob", "volatility") => Normal(1, 0.5))
 
-model = fit_model(agent, prior, inputs, actions, n_iterations = 20)
+#Create model
+model = create_model(agent, prior, inputs, actions)
+
+#Fit single chain with 10 iterations
+fitted_model = fit_model(model; n_iterations = 10, n_chains = 1)
 
 #-
 
@@ -65,7 +69,7 @@ plot(model)
 #-
 
 # ### Plot prior angainst posterior
-plot_parameter_distribution(model, prior)
+# plot_parameter_distribution(model, prior)
 #-
 # ### Get posterior
 get_posteriors(model)

docs/julia_files/tutorials/classic_binary.jl

@@ -81,27 +81,23 @@ plot_predictive_simulation(
 actions = CSV.read(data_path * "classic_binary_actions.csv", DataFrame)[!, 1];
 #-
 # Fit the actions
-fitted_model = fit_model(
-    agent,
-    param_priors,
-    inputs,
-    actions,
-    fixed_parameters = fixed_parameters,
-    verbose = true,
-    n_iterations = 10,
-)
+#Create model
+model = create_model(agent, param_priors, inputs, actions)
+
+#Fit single chain with 10 iterations
+fitted_model = fit_model(model; n_iterations = 10, n_chains = 1)
 #-
 #Plot the chains
 plot(fitted_model)
 #-
 # Plot the posterior
-plot_parameter_distribution(fitted_model, param_priors)
+# plot_parameter_distribution(fitted_model, param_priors)
 #-
 # Posterior predictive plot
-plot_predictive_simulation(
-    fitted_model,
-    agent,
-    inputs,
-    ("xbin", "prediction_mean"),
-    n_simulations = 3,
-)
+# plot_predictive_simulation(
+#     fitted_model,
+#     agent,
+#     inputs,
+#     ("xbin", "prediction_mean"),
+#     n_simulations = 3,
+# )

docs/julia_files/tutorials/classic_usdchf.jl

@@ -104,19 +104,14 @@ plot_predictive_simulation(
 )
 #-
 # Do parameter recovery
-fitted_model = fit_model(
-    agent,
-    param_priors,
-    inputs,
-    actions,
-    fixed_parameters = fixed_parameters,
-    verbose = false,
-    n_iterations = 10,
-)
+model = create_model(agent, param_priors, inputs, actions)
+
+#Fit single chain with 10 iterations
+fitted_model = fit_model(model; n_iterations = 10, n_chains = 1)
 #-
 # Plot the chains
 plot(fitted_model)
 #-
 # Plot prior posterior distributions
-plot_parameter_distribution(fitted_model, param_priors)
+# plot_parameter_distribution(fitted_model, param_priors)
 #-

docs/julia_files/user_guide/fitting_hgf_models.jl

@@ -103,23 +103,19 @@ param_priors = Dict(("xprob", "volatility") => Normal(-3.0, 0.5));
 
 # We can fit the evolution rate by inputting the variables:
 
-# Fit the actions
-fitted_model = fit_model(
-    agent,
-    param_priors,
-    inputs,
-    actions,
-    fixed_parameters = fixed_parameters,
-    verbose = true,
-    n_iterations = 10,
-)
+# Create model
+model = create_model(agent, param_priors, inputs, actions)
+
+#Fit single chain with 10 iterations
+fitted_model = fit_model(model; n_iterations = 10, n_chains = 1)
+
 set_parameters!(agent, hgf_parameters)
 
 # ## Plotting Functions
 plot(fitted_model)
 
 # Plot the posterior
-plot_parameter_distribution(fitted_model, param_priors)
+# plot_parameter_distribution(fitted_model, param_priors)
 
 
 # # Predictive Simulations with plot\_predictive\_distributions()
@@ -151,13 +147,13 @@ fitted_model =
 set_parameters!(agent, hgf_parameters)
 
 # We can place our turing chain as a our posterior in the function, and get our posterior predictive simulation plot:
-plot_predictive_simulation(
-    fitted_model,
-    agent,
-    inputs,
-    ("xbin", "prediction_mean"),
-    n_simulations = 100,
-)
+# plot_predictive_simulation(
+#     fitted_model,
+#     agent,
+#     inputs,
+#     ("xbin", "prediction_mean"),
+#     n_simulations = 100,
+# )
 
 # We can get the posterior 
 get_posteriors(fitted_model)

docs/make.jl

@@ -3,7 +3,7 @@ using Documenter
 using Literate
 
 
-hgf_path = dirname(dirname(pathof(HierarchicalGaussianFiltering)))
+hgf_path = dirname(pathof(HierarchicalGaussianFiltering))
 
 juliafiles_path = hgf_path * "/docs/julia_files"
 user_guides_path = juliafiles_path * "/user_guide"

src/ActionModels_variations/utils/get_history.jl

@@ -85,32 +85,20 @@
 
 function ActionModels.get_history(node::AbstractNode)
 
-    #Initialize dictionary
-    state_histories = Dict()
-
-    #Go through all states in the node's history
-    for state_key in fieldnames(typeof(node.history))
+    #Get the node's name and history
+    node_name = node.name
+    node_history = node.history
 
-        #And add their histories to the output
-        state_histories[String(state_key)] = getproperty(node.history, state_key)
+    #Return a dictionary of the node's history
+    Dict((node_name,string(key))=>getfield(node_history, key) for key ∈ fieldnames(typeof(node_history)))
 
-    end
-
-    return state_histories
 end
 
 function ActionModels.get_history(hgf::HGF)
 
-    #Initialize dict for state histories
-    state_histories = Dict()
-
-    #For each node
-    for node in hgf.ordered_nodes.all_nodes
-        #Get out the histories of the node
-        node_histories = get_history(node)
-        #And merge them with the dict
-        merge(state_histories, node_histories)
-    end
+    #Get the histories of all nodes
+    merge(
+        [get_history(node) for node in hgf.ordered_nodes.all_nodes]...
+        )
 
-    return state_histories
 end

src/ActionModels_variations/utils/get_states.jl

@@ -100,38 +100,27 @@
         throw(ArgumentError("The node $node_name does not exist"))
     end
 
-    #Initialize dict
-    states = Dict()
-
-    #Get out the node
-    node = hgf.all_nodes[node_name]
+    #Get the states of the node
+    node = get_states(hgf.all_nodes[node_name])
+end
 
-    #For each state in the node
-    for state_key in fieldnames(typeof(node.states))
+function ActionModels.get_states(node::AbstractNode)
 
-        #Add it to the dictionary
-        states[(node_name, String(state_key))] = get_states(node, String(state_key))
+    #Get the node's name and states
+    node_name = node.name
+    node_states = node.states
 
-    end
+    #Return a dictionary of the node's states
+    Dict((node_name,string(key))=>getfield(node_states, key) for key ∈ fieldnames(typeof(node_states)))
 
-    #Get its states
-    return states
 end
 
-
 ### For getting all states of an HGF ###
 function ActionModels.get_states(hgf::HGF)
 
-    #Initialize dict for state states
-    states = Dict()
-
-    #For each node
-    for node_name in keys(hgf.all_nodes)
-        #Get out the states of the node
-        node_states = get_states(hgf, node_name)
-        #And merge them with the dict
-        states = merge(states, node_states)
-    end
-
-    return states
+    #Get the states of all nodes
+    merge(
+        [get_states(node) for node in hgf.ordered_nodes.all_nodes]...
+        )
+
 end

test/testsuite/test_fit_model.jl

@@ -38,30 +38,26 @@ using Turing
             ("x", "drift") => Normal(0, 1),
         )
 
+        #Create model
+        model = create_model(test_agent, test_param_priors, test_input, test_responses;)
+
         #Fit single chain with defaults
-        fitted_model = fit_model(
-            test_agent,
-            test_param_priors,
-            test_input,
-            test_responses;
-            fixed_parameters = test_fixed_parameters,
-            verbose = false,
-            n_iterations = 10,
-        )
-        @test fitted_model isa Turing.Chains
+        fitted_model = fit_model(model; n_iterations = 10, n_chains = 1)
+
+        @test fitted_model isa ActionModels.FitModelResults
 
         #Plot the parameter distribution
-        plot_parameter_distribution(fitted_model, test_param_priors)
+        # plot_parameter_distribution(fitted_model, test_param_priors)
 
         # Posterior predictive plot
-        plot_predictive_simulation(
-            fitted_model,
-            test_agent,
-            test_input,
-            ("x", "posterior_mean");
-            verbose = false,
-            n_simulations = 3,
-        )
+        # plot_predictive_simulation(
+        #     fitted_model,
+        #     test_agent,
+        #     test_input,
+        #     ("x", "posterior_mean");
+        #     verbose = false,
+        #     n_simulations = 3,
+        # )
     end
 
 
@@ -95,29 +91,25 @@ using Turing
             ("xprob", "volatility") => Normal(-7, 5),
         )
 
+        #Create model
+        model = create_model(test_agent, test_param_priors, test_input, test_responses;)
+
         #Fit single chain with defaults
-        fitted_model = fit_model(
-            test_agent,
-            test_param_priors,
-            test_input,
-            test_responses;
-            fixed_parameters = test_fixed_parameters,
-            verbose = false,
-            n_iterations = 10,
-        )
-        @test fitted_model isa Turing.Chains
+        fitted_model = fit_model(model; n_iterations = 10, n_chains = 1)
+
+        @test fitted_model isa ActionModels.FitModelResults
 
         #Plot the parameter distribution
-        plot_parameter_distribution(fitted_model, test_param_priors)
+        # plot_parameter_distribution(fitted_model, test_param_priors)
 
         # Posterior predictive plot
-        plot_predictive_simulation(
-            fitted_model,
-            test_agent,
-            test_input,
-            ("xbin", "posterior_mean"),
-            verbose = false,
-            n_simulations = 3,
-        )
+        # plot_predictive_simulation(
+        #     fitted_model,
+        #     test_agent,
+        #     test_input,
+        #     ("xbin", "posterior_mean"),
+        #     verbose = false,
+        #     n_simulations = 3,
+        # )
     end
 end