saturday work session

mostly focused on data formatting and some data viz. minor refactors on some of the social systems logic in preparation for this weeks workshop

v2/model/economyconfig.py

@@ -13,7 +13,7 @@ from .partial_state_update_block import partial_state_update_blocks
 
 sim_config = config_sim({
     'N': 1,
-    'T': range(100), #day 
+    'T': range(183), #day 
 })
 
 seeds = {

v2/model/model/conviction_helper_functions.py

@@ -95,7 +95,7 @@ def gen_new_participant(network, new_participant_holdings):
 
 
 
-def gen_new_proposal(network, funds, supply, scale_factor = 1.0/100):
+def gen_new_proposal(network, funds, supply, scale_factor = 1.0/1000):
     '''
     Definition:
     Driving processes for the arrival of proposals.
@@ -163,14 +163,16 @@ def get_nodes_by_type(g, node_type_selection):
     '''
     return [node for node in g.nodes if g.nodes[node]['type']== node_type_selection ]
 
-def get_sentimental(sentiment, force, decay=0):
+def get_sentimental(sentiment, force, decay=.1):
     '''
     '''
     mu = decay
-    sentiment = sentiment*(1-mu) + force
+    sentiment = sentiment*(1-mu) + force*mu
     
     if sentiment > 1:
         sentiment = 1
+    elif sentiment < 0:
+        sentiment = 0
         
     return sentiment
 
@@ -424,7 +426,7 @@ def quantile_plot(xkey, ykey, dataframe, dq=.1, logy=False, return_df = False):
     if return_df:
         return data
 
-def affinities_plot(df):
+def affinities_plot(df, dims = (8.5, 11) ):
     '''
     '''
     last_net= df.network.values[-1]
@@ -440,7 +442,6 @@ def affinities_plot(df):
             j = last_props[j_ind]
             affinities[i_ind][j_ind] = last_net.edges[(i,j)]['affinity']
 
-    dims = (20, 5)
     fig, ax = plt.subplots(figsize=dims)
 
     sns.heatmap(affinities.T,
@@ -448,6 +449,7 @@ def affinities_plot(df):
                 yticklabels=last_props,
                 square=True,
                 cbar=True,
+                cmap = plt.cm.RdYlGn,
                 ax=ax)
 
     plt.title('affinities between participants and proposals')
@@ -457,12 +459,14 @@ def affinities_plot(df):
 
 
 
-def trigger_sweep(field, trigger_func,beta,rho,alpha,xmax=.2):
+def trigger_sweep(field, trigger_func,beta,rho,alpha,supply=10**9):
     '''
     '''
+    xmax= beta
+
     if field == 'effective_supply':
         share_of_funds = np.arange(.001,xmax,.001)
-        total_supply = np.arange(0,10**9, 10**6) 
+        total_supply = np.arange(0,supply*10, supply/100) 
         demo_data_XY = np.outer(share_of_funds,total_supply)
 
         demo_data_Z0=np.empty(demo_data_XY.shape)
@@ -484,11 +488,12 @@ def trigger_sweep(field, trigger_func,beta,rho,alpha,xmax=.2):
                 'share_of_max_conv': demo_data_Z2,
                 'log10_share_of_max_conv':demo_data_Z3,
                 'total_supply':total_supply,
-                'share_of_funds':share_of_funds}
+                'share_of_funds':share_of_funds,
+                'alpha':alpha}
     elif field == 'alpha':
-        alpha = np.arange(0,1,.001)
+        #note if alpha >.01 then this will give weird results max alpha will be >1
+        alpha = np.arange(0,.5,.001)
         share_of_funds = np.arange(.001,xmax,.001)
-        total_supply = 10**9
         demo_data_XY = np.outer(share_of_funds,alpha)
 
         demo_data_Z4=np.empty(demo_data_XY.shape)
@@ -498,7 +503,7 @@ def trigger_sweep(field, trigger_func,beta,rho,alpha,xmax=.2):
         for sof_ind in range(len(share_of_funds)):
             sof = share_of_funds[sof_ind]
             for a_ind in range(len(alpha)):
-                ts = total_supply
+                ts = supply
                 a = alpha[a_ind]
                 tc = ts /(1-a)
                 trigger = trigger_func(sof, 1, ts, beta, rho, a)
@@ -512,7 +517,8 @@ def trigger_sweep(field, trigger_func,beta,rho,alpha,xmax=.2):
                'share_of_max_conv': demo_data_Z6,
                'log10_share_of_max_conv':demo_data_Z7,
                'alpha':alpha,
-               'share_of_funds':share_of_funds}
+               'share_of_funds':share_of_funds,
+               'supply':supply}
         
     else:
         return "invalid field"
@@ -535,53 +541,125 @@ def trigger_plotter(share_of_funds,Z, color_label,y, ylabel,cmap='jet'):
     
 def trigger_grid(supply_sweep, alpha_sweep):
 
-    fig, axs = plt.subplots(nrows=2, ncols=2,figsize=(20,20))
+    fig, axs = plt.subplots(nrows=2, ncols=1,figsize=(20,20))
     axs = axs.flatten()
 
-    share_of_funds = alpha_sweep['share_of_funds']
+    # cut out the plots that didn't require the heatmap
-    Z = alpha_sweep['log10_share_of_max_conv']
+    # and switch to (2,1) subplots
-    y = alpha_sweep['alpha']
-    ylabel = 'alpha'
 
-    axs[0].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
+    # share_of_funds = alpha_sweep['share_of_funds']
-    #axs[0].colorbar(cf)
+    # Z = alpha_sweep['log10_share_of_max_conv']
-    axs[0].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
+    # y = alpha_sweep['alpha']
-    axs[0].set_ylabel(ylabel)
+    # ylabel = 'alpha'
-    axs[0].set_xlabel('Share of Funds Requested')
-    axs[0].set_title('Trigger Function Map - Alpha sweep')
 
+    # axs[0].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
+    # #axs[0].colorbar(cf)
+    # axs[0].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
+    # axs[0].set_ylabel(ylabel)
+    # axs[0].set_xlabel('Share of Funds Requested')
+    # axs[0].set_title('Trigger Function Map - Alpha sweep')
 
     share_of_funds = alpha_sweep['share_of_funds']
     Z = alpha_sweep['log10_trigger']
     y = alpha_sweep['alpha']
     ylabel = 'alpha'
+    supply = alpha_sweep['supply']
 
-    axs[1].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
+    cp0=axs[0].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
-    axs[1].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
+    axs[0].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
-    axs[1].set_ylabel(ylabel)
+    axs[0].set_ylabel(ylabel)
-    axs[1].set_xlabel('Share of Funds Requested')
+    axs[0].set_xlabel('Share of Funds Requested')
-    axs[1].set_title('Trigger Function Map - Alpha sweep - Z: log10_trigger')
+    axs[0].set_title('Trigger Function Map - Alpha sweep; Supply ='+str(supply))
+    cb0=plt.colorbar(cp0, ax=axs[0])
+    cb0.set_label('log10 of conviction to trigger')
 
-    share_of_funds = supply_sweep['share_of_funds']
+    # share_of_funds = supply_sweep['share_of_funds']
-    Z = supply_sweep['log10_share_of_max_conv']
+    # Z = supply_sweep['log10_share_of_max_conv']
-    y = supply_sweep['total_supply']
+    # y = supply_sweep['total_supply']
-    ylabel = 'Effective Supply'
+    # ylabel = 'Effective Supply'
 
-    axs[2].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
+    # axs[2].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
-    axs[2].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
+    # axs[2].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
-    axs[2].set_ylabel(ylabel)
+    # axs[2].set_ylabel(ylabel)
-    axs[2].set_xlabel('Share of Funds Requested')
+    # axs[2].set_xlabel('Share of Funds Requested')
-    axs[2].set_title('Trigger Function Map - Supply sweep - Z: share_of_max_conv')   
+    # axs[2].set_title('Trigger Function Map - Supply sweep - Z: share_of_max_conv')   
-    
     
     share_of_funds = supply_sweep['share_of_funds']
     Z = supply_sweep['log10_trigger']
     y = supply_sweep['total_supply']
     ylabel = 'Effective Supply'
+    alpha = supply_sweep['alpha']
 
-    axs[3].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
+    max_conv = y/(1-alpha)
-    axs[3].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
+
-    axs[3].set_ylabel(ylabel)
+    cp1=axs[1].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
-    axs[3].set_xlabel('Share of Funds Requested')
+    axs[1].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
-    axs[3].set_title('Trigger Function Map - Supply sweep')
+    axs[1].set_ylabel(ylabel)
-    
+    axs[1].set_xlabel('Share of Funds Requested')
+    axs[1].set_title('Trigger Function Map - Supply sweep; alpha='+str(alpha))
+    axs[1].set_label('log10 of conviction to trigger')
+    cb1=plt.colorbar(cp1, ax=axs[1])
+    cb1.set_label('log10 of conviction to trigger')
+
+
+def initialize_network(n,m, initial_funds, supply):
+    '''
+    Definition:
+    Function to initialize network x object
+
+    Parameters:
+
+    Assumptions:
+
+    Returns:
+
+    Example:
+    '''
+    # initilize network x graph
+    network = nx.DiGraph()
+    # create participant nodes with type and token holding
+    for i in range(n):
+        network.add_node(i)
+        network.nodes[i]['type']= "participant"
+        
+        h_rv = expon.rvs(loc=0.0, scale= supply/n)
+        network.nodes[i]['holdings'] = h_rv # SOL check
+        
+        s_rv = np.random.rand() 
+        network.nodes[i]['sentiment'] = s_rv
+    
+    participants = get_nodes_by_type(network, 'participant')
+    initial_supply = np.sum([ network.nodes[i]['holdings'] for i in participants])
+       
+    
+    # Generate initial proposals
+    for ind in range(m):
+        j = n+ind
+        network.add_node(j)
+        network.nodes[j]['type']="proposal"
+        network.nodes[j]['conviction'] = 0
+        network.nodes[j]['status'] = 'candidate'
+        network.nodes[j]['age'] = 0
+        
+        r_rv = gamma.rvs(3,loc=0.001, scale=100)
+        network.nodes[j]['funds_requested'] = r_rv
+        
+        network.nodes[j]['trigger']= trigger_threshold(r_rv, initial_funds, initial_supply,beta,rho,alpha)
+        
+        for i in range(n):
+            network.add_edge(i, j)
+            
+            rv = np.random.rand()
+            a_rv = np.random.uniform(-1,1,1)[0]
+            network.edges[(i, j)]['affinity'] = a_rv
+            network.edges[(i, j)]['tokens'] = 0
+            network.edges[(i, j)]['conviction'] = 0
+            network.edges[(i, j)]['type'] = 'support'
+            
+        proposals = get_nodes_by_type(network, 'proposal')
+        total_requested = np.sum([ network.nodes[i]['funds_requested'] for i in proposals])
+        
+        network = initial_conflict_network(network, rate = .25)
+        network = initial_social_network(network, scale = 1)
+        
+    return network

v2/model/model/initialization.py

@@ -10,7 +10,8 @@ from .conviction_helper_functions import *
 beta = .2 #later we should set this to be param so we can sweep it
 # tuning param for the trigger function
 rho = .001
-alpha = 1 - 0.9999599
+#alpha = 1 - 0.9999599 #native timescale for app as in contract code
+alpha = .5**3 #timescale set in days with 3 day halflife (from comments in contract comments)
 supply = 21706 # Honey supply balance as of 7-17-2020  
 initial_sentiment = .6
 
@@ -28,65 +29,4 @@ base_failure_rate = 200
 
 initial_funds = 48000 # in xDai
 
-def initialize_network(n,m, inital_funds, supply):
+network = initialize_network(n,m,initial_funds,supply)
-    '''
-    Definition:
-    Function to initialize network x object
-
-    Parameters:
-
-    Assumptions:
-
-    Returns:
-
-    Example:
-    '''
-    # initilize network x graph
-    network = nx.DiGraph()
-    # create participant nodes with type and token holding
-    for i in range(n):
-        network.add_node(i)
-        network.nodes[i]['type']= "participant"
-        
-        h_rv = expon.rvs(loc=0.0, scale= supply/n)
-        network.nodes[i]['holdings'] = h_rv # SOL check
-        
-        s_rv = np.random.rand() 
-        network.nodes[i]['sentiment'] = s_rv
-    
-    participants = get_nodes_by_type(network, 'participant')
-    initial_supply = np.sum([ network.nodes[i]['holdings'] for i in participants])
-       
-    
-    # Generate initial proposals
-    for ind in range(m):
-        j = n+ind
-        network.add_node(j)
-        network.nodes[j]['type']="proposal"
-        network.nodes[j]['conviction'] = 0
-        network.nodes[j]['status'] = 'candidate'
-        network.nodes[j]['age'] = 0
-        
-        r_rv = gamma.rvs(3,loc=0.001, scale=100)
-        network.nodes[j]['funds_requested'] = r_rv
-        
-        network.nodes[j]['trigger']= trigger_threshold(r_rv, initial_funds, initial_supply,beta,rho,alpha)
-        
-        for i in range(n):
-            network.add_edge(i, j)
-            
-            a_rv = np.random.uniform(-1,1,1)[0]
-            network.edges[(i, j)]['affinity'] = a_rv
-            network.edges[(i, j)]['tokens'] = 0
-            network.edges[(i, j)]['conviction'] = 0
-            network.edges[(i, j)]['type'] = 'support'
-            
-        proposals = get_nodes_by_type(network, 'proposal')
-        total_requested = np.sum([ network.nodes[i]['funds_requested'] for i in proposals])
-        
-        network = initial_conflict_network(network, rate = .25)
-        network = initial_social_network(network, scale = 1)
-        
-    return network, initial_funds
-#initializers
-network, initial_funds = initialize_network(n,m,initial_funds,supply)

v2/model/model/participants.py

@@ -76,27 +76,19 @@ def complete_proposal(params, step, sL, s, _input):
     return (key, value)
 
 def update_sentiment_on_completion(params, step, sL, s, _input):
+    
     network = s['network']
-    proposals = get_nodes_by_type(network, 'proposal')
     completed = _input['completed']
-    failed = _input['failed']
+    failed  = _input['failed']
-    
-    grants_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='active'])
-    
-    grants_completed = np.sum([network.nodes[j]['funds_requested'] for j in completed])
-    grants_failed = np.sum([network.nodes[j]['funds_requested'] for j in failed])
-    
     sentiment = s['sentiment']
     
-    if grants_outstanding>0:
+    completed_count = len(completed)
-        force = (grants_completed-grants_failed)/grants_outstanding
+    failed_count = len(failed) 
-    else:
-        force=1
-    if (force >=0) and (force <=1):
-        sentiment = get_sentimental(sentiment, force, mu)
-    else:
-        sentiment = get_sentimental(sentiment, 0, mu)
     
+    if completed_count+failed_count>0:
+        sentiment = get_sentimental(sentiment,completed_count-failed_count, .25)
+    else:
+        sentiment = get_sentimental(sentiment, 0, 0)
     
     key = 'sentiment'
     value = sentiment
@@ -134,8 +126,10 @@ def participants_decisions(params, step, sL, s):
                 booster = social_affinity_booster(network, j, i)
                 affinity = network.edges[(i, j)]['affinity']+booster
                 cutoff = sensitivity*np.max([network.edges[(i,p)]['affinity'] for p in candidates])
-                if cutoff <.5:
+                # range is [-1,1], where 0 is indifference, this determines min affinity supported
-                    cutoff = .5
+                # if no proposal meets this threshold participants may support a null proposal
+                if cutoff <.3:
+                    cutoff = .3
                 
                 if affinity > cutoff:
                     support.append(j)

v2/model/model/proposals.py

@@ -76,11 +76,11 @@ def update_sentiment_on_release(params, step, sL, s, _input):
     proposals_accepted = np.sum([network.nodes[j]['funds_requested'] for j in accepted])
     
     sentiment = s['sentiment']
-    force = proposals_accepted/proposals_outstanding
+    force = len(accepted)
-    if (force >=0) and (force <=1):
+    if force>0:
-        sentiment = get_sentimental(sentiment, force, False)
+        sentiment = get_sentimental(sentiment, force, .25)
     else:
-        sentiment = get_sentimental(sentiment, 0, False)
+        sentiment = get_sentimental(sentiment, 0, 0)
     
     key = 'sentiment'
     value = sentiment

v2/model/model/system.py

@@ -16,11 +16,21 @@ def driving_process(params, step, sL, s):
     arrival_rate = 10/(1+s['sentiment'])
     rv1 = np.random.rand()
     new_participant = bool(rv1<1/arrival_rate)
-    supporters = get_edges_by_type(s['network'], 'support')
+
+    network = s['network']
     
-    len_parts = len(get_nodes_by_type(s['network'], 'participant'))
+    proposals = get_nodes_by_type(network, 'proposal')
+    participants = get_nodes_by_type(network, 'participant')
+
+    cadidate_proposals = [j for j in proposals if network.nodes[j]['status']=='candidate']
+    subgraph_nodes = cadidate_proposals+participants
+
+    candidate_subgraph = s['network'].subgraph(subgraph_nodes)
+    supporters = get_edges_by_type(candidate_subgraph, 'support')
+    
+    len_parts = len(participants)
     #supply = s['supply'] 
-    expected_holdings = .1*supply/len_parts
+    expected_holdings = .01*supply/len_parts
     if new_participant:
         h_rv = expon.rvs(loc=0.0, scale=expected_holdings)
         new_participant_holdings = h_rv
@@ -28,16 +38,15 @@ def driving_process(params, step, sL, s):
         new_participant_holdings = 0
     
     network = s['network']
-    affinities = [network.edges[e]['affinity'] for e in supporters ]
+    affinities = [network.edges[e]['affinity'] for e in supporters]
     median_affinity = np.median(affinities)
     
-    proposals = get_nodes_by_type(network, 'proposal')
+    fund_requests = [network.nodes[j]['funds_requested'] for j in cadidate_proposals]
-    fund_requests = [network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate']
     
     funds = s['funds']
     total_funds_requested = np.sum(fund_requests)
     
-    proposal_rate = 1/median_affinity * (1+total_funds_requested/funds)
+    proposal_rate = 1/(1+median_affinity) * (1+total_funds_requested/funds)
     rv2 = np.random.rand()
     new_proposal = bool(rv2<1/proposal_rate)