ActivityDrivenModel: Parse reluctance options

Reluctance options are not part of ConfigParser. Defaults are set and some checks are completed. Also added an example with the reluctance. TODO: Make a unit test on the ActivityDrivenModel with reluctances. Co-authored-by: Moritz Sallermann <[email protected]>
seldon-code · Mar 16, 2024 · 20d7eac · 20d7eac
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Showing 9 changed files with 83 additions and 20 deletions.
diff --git a/examples/ActivityDrivenReluctance/conf.toml b/examples/ActivityDrivenReluctance/conf.toml
@@ -0,0 +1,33 @@
+[simulation]
+model = "ActivityDriven"
+# rng_seed = 120 # Leaving this empty will pick a random seed
+
+[io]
+n_output_network = 20 # Write the network every 20 iterations
+n_output_agents = 1 # Write the opinions of agents after every iteration
+print_progress = true # Print the iteration time ; if not set, then always print
+
+[model]
+max_iterations = 500 # If not set, max iterations is infinite
+
+[ActivityDriven]
+dt = 0.01               	# Timestep for the integration of the coupled ODEs
+m = 10                  	# Number of agents contacted, when the agent is active
+eps = 0.01              	# Minimum activity epsilon; a_i belongs to [epsilon,1]
+gamma = 2.1             	# Exponent of activity power law distribution of activities
+reciprocity = 0.5       	# probability that when agent i contacts j via weighted reservoir sampling, j also sends feedback to i. So every agent can have more than m incoming connections
+homophily = 0.5         	# aka beta. if zero, agents pick their interaction partners at random
+alpha = 3.0             	# Controversialness of the issue, must be greater than 0.
+K = 3.0                 	# Social interaction strength
+mean_activities = false 	# Use the mean value of the powerlaw distribution for the activities of all agents
+mean_weights = false    	# Use the meanfield approximation of the network edges
+
+reluctances = true 			# Assigns a "reluctance" (m_i) to each agent. By default; false and every agent has a reluctance of 1
+reluctance_mean   = 1.0 	# Mean of distribution before drawing from a truncated normal distribution (default set to 1.0)
+reluctance_sigma  = 0.25 	# Width of normal distribution (before truncating)
+reluctance_eps    = 0.01 	# Minimum such that the normal distribution is truncated at this value
+covariance_factor = 0.0 	# Determines whether activities and reluctances will be correlated. If set to 0.0 (default) then they will not be correlated.
+
+[network]
+number_of_agents = 1000
+connections_per_agent = 10
diff --git a/include/config_parser.hpp b/include/config_parser.hpp
@@ -58,6 +58,11 @@ struct ActivityDrivenSettings
     std::vector<double> bot_activity  = std::vector<double>( 0 );
     std::vector<double> bot_opinion   = std::vector<double>( 0 );
     std::vector<double> bot_homophily = std::vector<double>( 0 );
+    bool use_reluctances              = false;
+    double reluctance_mean            = 1.0;
+    double reluctance_sigma           = 0.25;
+    double reluctance_eps             = 0.01;
+    double covariance_factor          = 0.0;
 };
 
 struct InitialNetworkSettings

diff --git a/include/simulation.hpp b/include/simulation.hpp
@@ -85,7 +85,8 @@ class Simulation : public SimulationInterface
             auto degroot_settings = std::get<Config::DeGrootSettings>( options.model_settings );
 
             // DeGroot specific parameters
-            model = [&]() {
+            model = [&]()
+            {
                 auto model             = std::make_unique<DeGrootModel>( network );
                 model->max_iterations  = degroot_settings.max_iterations;
                 model->convergence_tol = degroot_settings.convergence_tol;
@@ -101,7 +102,8 @@ class Simulation : public SimulationInterface
         {
             auto activitydriven_settings = std::get<Config::ActivityDrivenSettings>( options.model_settings );
 
-            model = [&]() {
+            model = [&]()
+            {
                 auto model             = std::make_unique<ActivityDrivenModel>( network, gen );
                 model->dt              = activitydriven_settings.dt;
                 model->m               = activitydriven_settings.m;
@@ -114,6 +116,12 @@ class Simulation : public SimulationInterface
                 model->mean_activities = activitydriven_settings.mean_activities;
                 model->mean_weights    = activitydriven_settings.mean_weights;
                 model->max_iterations  = activitydriven_settings.max_iterations;
+                // Reluctance
+                model->use_reluctances   = activitydriven_settings.use_reluctances;
+                model->reluctance_mean   = activitydriven_settings.reluctance_mean;
+                model->reluctance_sigma  = activitydriven_settings.reluctance_sigma;
+                model->reluctance_eps    = activitydriven_settings.reluctance_eps;
+                model->covariance_factor = activitydriven_settings.covariance_factor;
                 // bot
                 model->n_bots        = activitydriven_settings.n_bots;
                 model->bot_opinion   = activitydriven_settings.bot_opinion;

diff --git a/src/config_parser.cpp b/src/config_parser.cpp
@@ -70,27 +70,36 @@ SimulationOptions parse_config_file( std::string_view config_file_path )
         model_settings.K               = tbl["ActivityDriven"]["K"].value_or<double>( 3.0 );
         model_settings.mean_activities = tbl["ActivityDriven"]["mean_activities"].value_or<bool>( false );
         model_settings.mean_weights    = tbl["ActivityDriven"]["mean_weights"].value_or<bool>( false );
+        // Reluctances
+        model_settings.use_reluctances   = tbl["ActivityDriven"]["reluctances"].value_or<bool>( false );
+        model_settings.reluctance_mean   = tbl["ActivityDriven"]["reluctance_mean"].value_or<double>( 1.0 );
+        model_settings.reluctance_sigma  = tbl["ActivityDriven"]["reluctance_sigma"].value_or<double>( 0.25 );
+        model_settings.reluctance_eps    = tbl["ActivityDriven"]["reluctance_eps"].value_or<double>( 0.01 );
+        model_settings.covariance_factor = tbl["ActivityDriven"]["covariance_factor"].value_or<double>( 0.0 );
 
         model_settings.max_iterations = tbl["model"]["max_iterations"].value<int>();
 
         // bot
         model_settings.n_bots = tbl["ActivityDriven"]["n_bots"].value_or<size_t>( 0 );
 
-        auto push_back_bot_array = [&]( auto toml_node, auto & options_array, auto default_value ) {
+        auto push_back_bot_array = [&]( auto toml_node, auto & options_array, auto default_value )
+        {
             if( toml_node.is_array() )
             {
                 toml::array * toml_arr = toml_node.as_array();
 
-                toml_arr->for_each( [&]( auto && elem ) {
-                    if( elem.is_integer() )
-                    {
-                        options_array.push_back( elem.as_integer()->get() );
-                    }
-                    else if( elem.is_floating_point() )
+                toml_arr->for_each(
+                    [&]( auto && elem )
                     {
-                        options_array.push_back( elem.as_floating_point()->get() );
-                    }
-                } );
+                        if( elem.is_integer() )
+                        {
+                            options_array.push_back( elem.as_integer()->get() );
+                        }
+                        else if( elem.is_floating_point() )
+                        {
+                            options_array.push_back( elem.as_floating_point()->get() );
+                        }
+                    } );
             }
             else
             {
@@ -155,7 +164,12 @@ void validate_settings( const SimulationOptions & options )
         check( name_and_var( model_settings.alpha ), geq_zero );
         // check( name_and_var( model_settings.homophily ), geq_zero );
         check( name_and_var( model_settings.reciprocity ), geq_zero );
-
+        // Reluctance options
+        check( name_and_var( model_settings.reluctance_mean ), g_zero );
+        check( name_and_var( model_settings.reluctance_sigma ), g_zero );
+        check( name_and_var( model_settings.reluctance_eps ), g_zero );
+        check( name_and_var( model_settings.covariance_factor ), geq_zero );
+        // Bot options
         size_t n_bots             = model_settings.n_bots;
         auto check_bot_size       = [&]( auto x ) { return x.size() >= n_bots; };
         const std::string bot_msg = "Length needs to be >= n_bots";
@@ -176,7 +190,7 @@ void print_settings( const SimulationOptions & options )
     fmt::print( "INFO: Seeding with seed {}\n", options.rng_seed );
     fmt::print( "Model type: {}\n", options.model_string );
     fmt::print( "Network has {} agents\n", options.network_settings.n_agents );
-
+    // @TODO: Optionally print *all* settings to the console, including defaults that were set
     if( options.model == Model::ActivityDrivenModel )
     {
         auto model_settings = std::get<ActivityDrivenSettings>( options.model_settings );

diff --git a/src/models/ActivityDrivenModel.cpp b/src/models/ActivityDrivenModel.cpp
@@ -155,7 +155,8 @@ void ActivityDrivenModel::update_network_mean()
         contact_prob_list[idx_agent] = weights; // set to zero
     }
 
-    auto probability_helper = []( double omega, size_t m ) {
+    auto probability_helper = []( double omega, size_t m )
+    {
         double p = 0;
         for( size_t i = 1; i <= m; i++ )
             p += ( std::pow( -omega, i + 1 ) + omega ) / ( omega + 1 );

diff --git a/test/res/10_agents_meanfield_activity.toml b/test/res/10_agents_meanfield_activity.toml
@@ -3,7 +3,7 @@ model = "ActivityDriven"
 # rng_seed = 120           # Leaving this empty will pick a random seed
 
 [io]
-n_output_network = 1 # Write the network every 20 iterations
+# n_output_network = 1 # Write the network every 20 iterations
 # n_output_agents = 1
 print_progress = false # Print the iteration time ; if not set, then always print
 

diff --git a/test/res/opinions.txt b/test/res/opinions.txt
@@ -2,5 +2,5 @@
 # comment
  0,   2.1127107987061544, 0.044554683389757696
     1,0.8088982488089491, 0.015813166022685163
-2,-0.8802809369462433  , 0.015863953902810535
+2,-0.8802809369462433  , 0.015863953902810535, 2.3
 
diff --git a/test/test_activity.cpp b/test/test_activity.cpp
@@ -167,7 +167,8 @@ TEST_CASE( "Test the meanfield activity driven model with 10 agents", "[activity
     auto mean_activity = dist.mean();
 
     // Calculate the critical controversialness
-    auto set_opinions_and_run = [&]( bool above_critical_controversialness ) {
+    auto set_opinions_and_run = [&]( bool above_critical_controversialness )
+    {
         auto simulation            = Simulation<AgentT>( options, std::nullopt, std::nullopt );
         auto initial_opinion_delta = 0.1; // Set the initial opinion in the interval [-delta, delta]
 
@@ -184,7 +185,7 @@ TEST_CASE( "Test the meanfield activity driven model with 10 agents", "[activity
 
         // We need an output path for Simulation, but we won't write anything out there
         fs::path output_dir_path = proj_root_path / fs::path( "test/output_meanfield_test" );
-        fs::create_directories( output_dir_path );
+        // fs::create_directories( output_dir_path );
 
         // run that mofo
         simulation.run( output_dir_path );

diff --git a/test/test_sampling.cpp b/test/test_sampling.cpp
@@ -98,7 +98,8 @@ TEST_CASE( "Testing sampling functions" )
 
         std::vector<size_t> histogram( n, 0 ); // Count how often each element occurs amongst all samples
 
-        auto weight_callback = []( size_t idx ) {
+        auto weight_callback = []( size_t idx )
+        {
             if( ( idx == ignore_idx ) || ( idx == ignore_idx2 ) )
             {
                 return 0.0;