Execution Ctx pt.1

README.md

@@ -2,7 +2,7 @@
 
 **Dependencies:**
 ```bash
-pip install pathos pipenv fn tabulate
+pip install -r requirements.txt
 ```
 
 **Project:**

SimCAD/__init__.py

@@ -1,7 +1,12 @@
+from fn.op import foldr
+from SimCAD.utils.configuration import dict_elemwise_sum
+
+
 configs = []
 
-class Configuration(object):
-    def __init__(self, sim_config, state_dict, seed, exogenous_states, env_processes, behavior_ops, mechanisms):
+
+class Configuration:
+    def __init__(self, sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms, behavior_ops=[foldr(dict_elemwise_sum())]):
         self.sim_config = sim_config
         self.state_dict = state_dict
         self.seed = seed

SimCAD/engine/__init__.py

@@ -6,28 +6,42 @@ from SimCAD.utils.ui import create_tensor_field
 from SimCAD.utils.configProcessor import generate_config
 from SimCAD.engine.simulation import Executor as SimExecutor
 
+class ExecutionMode(object):
 
-class ExecutionContext:
-    def parallelize_simulations(self, fs, states_list, configs, env_processes, Ts, Ns):
-        l = list(zip(fs, states_list, configs, env_processes, Ts, Ns))
-        with Pool(len(configs)) as p:
-            results = p.map(lambda t: t[0](t[1], t[2], t[3], t[4], t[5]), l)
+    single_proc = 'single_proc'
+    multi_proc = 'multi_proc'
 
-        return results
+class ExecutionContext(object):
+
+    def __init__(self, context=ExecutionMode.multi_proc):
+        self.name = context
+        self.method = None
+        def parallelize_simulations(fs, states_list, configs, env_processes, Ts, Ns):
+            l = list(zip(fs, states_list, configs, env_processes, Ts, Ns))
+            with Pool(len(configs)) as p:
+                results = p.map(lambda t: t[0](t[1], t[2], t[3], t[4], t[5]), l)
+
+            return results
+
+        if context == 'single_proc':
+            self.method = None
+        elif context == 'multi_proc':
+            self.method = parallelize_simulations
 
 
 class Executor:
 
-    def __init__(self, ExecutionContext, configs):
+    def __init__(self, exec_context, configs):
         self.SimExecutor = SimExecutor
-        self.ExecutionContext = ExecutionContext
+        self.exec_method = exec_context.method
+        self.exec_context = exec_context.name
         self.configs = configs
         self.main = self.execute
 
+
     def execute(self):
 
-        ec = ExecutionContext()
-        print(self.configs)
+        print(self.exec_context+": "+str(self.configs))
         states_lists, Ts, Ns, eps, configs_structs, env_processes_list, mechanisms, simulation_execs = \
             [], [], [], [], [], [], [], []
         config_idx = 0
@@ -45,15 +59,21 @@ class Executor:
 
         # Dimensions: N x r x mechs
 
-        if len(self.configs) > 1:
-            simulations = ec.parallelize_simulations(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
-            results = []
-            for result, mechanism, ep in list(zip(simulations, mechanisms, eps)):
-                print(tabulate(create_tensor_field(mechanism, ep), headers='keys', tablefmt='psql'))
-                results.append(flatten(result))
-            return results
-        else:
+        def single_proc_exec(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns):
             simulation, states_list, config = simulation_execs.pop(), states_lists.pop(), configs_structs.pop()
             env_processes, T, N = env_processes_list.pop(), Ts.pop(), Ns.pop()
             result = simulation(states_list, config, env_processes, T, N)
-            return flatten(result)
+            return flatten(result)
+
+        if self.exec_context == ExecutionMode.single_proc:
+            return single_proc_exec(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
+        elif self.exec_context == ExecutionMode.multi_proc:
+            if len(self.configs) > 1:
+                simulations = self.exec_method(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
+                results = []
+                for result, mechanism, ep in list(zip(simulations, mechanisms, eps)):
+                    print(tabulate(create_tensor_field(mechanism, ep), headers='keys', tablefmt='psql'))
+                    results.append(flatten(result))
+                return results
+            else:
+                return single_proc_exec(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)

SimCAD/engine/simulation.py

@@ -2,18 +2,15 @@ from copy import deepcopy
 from fn import _
 from fn.op import foldr, call
 
-class Executor(object):
+
+class Executor:
     def __init__(self, behavior_ops):
         self.behavior_ops = behavior_ops
 
     # Data Type reduction
     def getBehaviorInput(self, step, sL, s, funcs):
 
-        if len(self.behavior_ops) == 0:
-            ops = [foldr(_ + _)]
-        else:
-            ops = self.behavior_ops[::-1]
-
+        ops = self.behavior_ops[::-1]
         def getColResults(step, sL, s, funcs):
             return list(map(lambda f: f(step, sL, s), funcs))
 

SimCAD/utils/configProcessor.py

@@ -69,7 +69,7 @@ def generate_config(state_dict, mechanisms, exo_proc):
         bdf_values = [[b_identity] * len(sdf_values)]
         return sdf_values, bdf_values
 
-    zipped_list = []
+    # zipped_list = []
     if len(mechanisms) != 0:
         bdf = create_matrix_field(mechanisms, 'behaviors')
         sdf = create_matrix_field(mechanisms, 'states')

requirements.txt

@@ -0,0 +1,4 @@
+pathos
+pipenv
+fn
+tabulate

sandboxUX/config1.py

@@ -161,4 +161,4 @@ sim_config = {
     "T": range(5)
 }
 
-configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, behavior_ops, mechanisms))
+configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms, behavior_ops))

sandboxUX/config2.py

@@ -1,9 +1,8 @@
 from decimal import Decimal
 import numpy as np
-from fn.op import foldr
 
 from SimCAD import Configuration, configs
-from SimCAD.utils.configuration import exo_update_per_ts, proc_trigger, dict_elemwise_sum, bound_norm_random, \
+from SimCAD.utils.configuration import exo_update_per_ts, proc_trigger, bound_norm_random, \
     ep_time_step
 
 
@@ -26,6 +25,7 @@ def b1m2(step, sL, s):
 def b2m2(step, sL, s):
     return {'param1': 'b', 'param2': 4}
 
+
 def b1m3(step, sL, s):
     return {'param1': ['c'], 'param2': np.array([10, 100])}
 def b2m3(step, sL, s):
@@ -120,7 +120,7 @@ env_processes = {
 
 # [1, 2] = {'b1': ['a'], 'b2', [1]} =
 # behavior_ops = [behavior_to_dict, print_fwd, sum_dict_values]
-behavior_ops = [foldr(dict_elemwise_sum())]
+# behavior_ops = [foldr(dict_elemwise_sum())]
 # behavior_ops = []
 
 # need at least 1 behaviour and 1 state function for the 1st mech with behaviors
@@ -163,4 +163,4 @@ sim_config = {
     "T": range(5)
 }
 
-configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, behavior_ops, mechanisms))
+configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))

sandboxUX/sim_test.py

@@ -1,15 +1,19 @@
 import pandas as pd
 from tabulate import tabulate
 
-from SimCAD.engine import ExecutionContext, Executor
+from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
 from sandboxUX import config1, config2
+from SimCAD import configs
 
 # ToDo: pass ExecutionContext with execution method as ExecutionContext input
 
+exec_mode = ExecutionMode()
+
 print("Simulation Run 1")
 print()
-single_config = [config1]
-run1 = Executor(ExecutionContext, single_config)
+single_config = [configs[0]]
+single_proc_ctx = ExecutionContext(exec_mode.single_proc)
+run1 = Executor(single_proc_ctx, single_config)
 run1_raw_result = run1.main()
 result = pd.DataFrame(run1_raw_result)
 print(tabulate(result, headers='keys', tablefmt='psql'))
@@ -17,8 +21,8 @@ print()
 
 print("Simulation Run 2: Pairwise Execution")
 print()
-configs = [config1, config2]
-run2 = Executor(ExecutionContext, configs)
+multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)
+run2 = Executor(multi_proc_ctx, configs)
 run2_raw_results = run2.main()
 for raw_result in run2_raw_results:
     result = pd.DataFrame(raw_result)