Added beta distribution to initialization and made necessary fun conc…

…essions in config. Added sensitivity analysis related things. This version has been tested locally only.

Sensitivity_Analysis/SampleGenerator.py

@@ -16,19 +16,22 @@
 # event_alpha 4.13+/-50%
 # event_beta  0.07+/-50%
 
-problem={   "names": ["homophily","local_ratio","noise_ratio","event_alpha","event_beta"], 
+problem={   "names": ["homophily","local_ratio","noise_ratio","shock_alpha","shock_beta"], 
             "num_vars":5,
             "bounds":[[0,10],[0.2,0.3],[0.04,0.06],[4.13*0.5,4.13*1.5],[0.07*0.5,0.07*1.5]],
             "dists":["unif","unif","unif","unif","unif"]}
 
 S_sample=sobol.sample(problem,n)
 
-S_sampledf=pd.DataFrame(S_sample, columns=["homophily","local_ratio","noise_ratio","event_alpha","event_beta"])
+S_sampledf=pd.DataFrame(S_sample, columns=["homophily","local_ratio","noise_ratio","shock_alpha","shock_beta"])
 
-S_sampledf.index.name="RunID"
 # drop duplicates 
 S_sampledf=S_sampledf.drop_duplicates()
 
+# reindex
+S_sampledf.index = pd.RangeIndex(start=1, stop=len(S_sampledf) + 1, step=1)
+S_sampledf.index.name="RunID"
+
 S_sampledf.to_csv("SaltelliSampleParams-n1024.csv")
 
 

Sensitivity_Analysis/SensitivityAnalysis.py

@@ -0,0 +1,67 @@
+import sys
+sys.path.append('/Users/victoria/Documents/Scripts/Python/DGL-PTM/dgl_ptm')
+import dgl_ptm
+import os
+import torch
+import argparse
+import collections
+os.environ["DGLBACKEND"] = "pytorch"
+
+# keeping None as default to make error obvious
+parser = argparse.ArgumentParser(description='Sensitivity Analysis')
+parser.add_argument('-r', '--run_id', default=None, type=int, help='Run ID from sample file (default: None)')
+parser.add_argument('-s', '--seed', default=None, type=int, help='Seed (default: None)')
+parser.add_argument('-ho', '--homophily', default=None, type=float, help='Homophily (default: None)') 
+parser.add_argument('-l', '--local', default=None, type=float, help='Local attachment ratio (default: None)')
+parser.add_argument('-n', '--noise', default=None, type=float, help='Noise ratio (default: None)')
+parser.add_argument('-a', '--shock_a', default=None, type=float, help='Shock alpha (default: None)')
+parser.add_argument('-b', '--shock_b', default=None, type=float, help='Shock beta (default: None)')
+
+args =parser.parse_args()
+
+model = dgl_ptm.PovertyTrapModel(model_identifier=f'batchtest_{args.run_id}',)
+
+model.set_model_parameters(**{'number_agents': 100 , 
+    'seed':args.seed,
+    'gamma_vals':torch.tensor([0.3,0.45]) , #for pseudo income
+    'sigma_dist': {'type':'uniform','parameters':[0.05,1.94],'round':True,'decimals':1},
+    'cost_vals': torch.tensor([0.,0.45]), #for pseudo income
+    'a_theta_dist': {'type':'uniform','parameters':[0.1,1],'round':False,'decimals':None},
+    'sensitivity_dist':{'type':'uniform','parameters':[0.0,1],'round':False,'decimals':None},
+    'capital_dist': {'type':'uniform','parameters':[0.1,10.],'round':False,'decimals':None}, 
+    'alpha_dist': {'type':'normal','parameters':[1.08,0.074],'round':False,'decimals':None},
+    'lambda_dist': {'type':'uniform','parameters':[0.05,0.94],'round':True,'decimals':1},
+    'initial_graph_type': 'barabasi-albert',
+    'initial_graph_args': {'seed': 1, 'new_node_edges':1},
+    'device': 'cpu',
+    'step_target':20,
+    'steering_parameters':{'npath':'./agent_data.zarr',
+                            'epath':'./edge_data', 
+                            'ndata':['all_except',['a_table']],
+                            'edata':['all'],
+                            'mode':'w',
+                            'wealth_method':'weighted_transfer',
+                            'income_method':'income_generation',
+                            'tech_gamma': torch.tensor([0.3,0.35,0.45]),
+                            'tech_cost': torch.tensor([0,0.15,0.65]),
+                            'consume_method':'past_shock_bellman_consumption',
+                            'nn_path': "/nn_data/both_PudgeSixLayer_1024/0723_110813/model_best.pth",
+                            'adapt_m':torch.tensor([0,0.5,0.9]),
+                            'adapt_cost':torch.tensor([0,0.25,0.45]),
+                            'depreciation': 0.6,
+                            'discount': 0.95,
+                            'm_theta_dist': {'type':'beta','parameters':[args.shock_a,args.shock_b],'round':False,'decimals':None},
+                            'del_method':'size',
+                            'noise_ratio': args.noise,
+                            'local_ratio': args.local,
+                            'homophily_parameter': args.homophily,
+                            'characteristic_distance':3.33, 
+                            'truncation_weight':1.0e-10,
+                            'step_type':'custom'}})
+
+print(model.config.steering_parameters)                        
+
+model.initialize_model()
+print (model.steering_parameters['modelTheta'] )
+model.run()
+

Sensitivity_Analysis/SensitivityAnalysis.sh

@@ -0,0 +1,46 @@
+#!/bin/bash
+#SBATCH --job-name=SA1_seed1
+#SBATCH -p gpu
+#SBATCH --gpus=1
+
+#              d-hh:mm:ss
+#SBATCH --time=05:30:00
+
+
+module load 2023
+conda activate dgl_ptm_gpu
+
+
+
+# Sensitivity Analysis
+
+
+seed=1
+counter=0
+start=1
+earlystop=3
+sample_csv="SaltelliSampleParams-n1024.csv"
+
+while IFS=, read -r RunID homophily local_ratio noise_ratio shock_alpha shock_beta; do    
+    # Check range compliance
+    if [ "$earlystop" != "None" ] && [ "$RunID" -gt "$earlystop" ]; then
+        break
+    fi
+    # Check range compliance and print parameters
+    if [ "$RunID" -ge "$start" ]; then
+        echo Run: "$RunID"
+        echo Homophily: "$homophily" Local Ratio: "$local_ratio" Noise Ratio: "$noise_ratio" Shock Alpha: "$shock_alpha" Shock Beta: "$shock_beta"
+        python SensitivityAnalysis.py --seed "$seed" --run_id "$RunID" --homophily "$homophily" --local "$local_ratio" --noise "$noise_ratio" --shock_a "$shock_alpha" --shock_b "$shock_beta"
+
+
+    fi
+
+done < "$sample_csv"
+wait
+echo " $(date) - Runs $start through $earlystop are complete for seed $seed."
+
+
+
+
+
+