Reafactor Pt. 1

This commit is contained in:
Joshua E. Jodesty 2018-11-15 16:22:21 -05:00
parent 311c867000
commit 29ca7ac177
11 changed files with 96 additions and 67 deletions

11
configuration/__init__.py Normal file
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@ -0,0 +1,11 @@
configs = []
class Config(object):
def __init__(self, sim_config, state_dict, seed, exogenous_states, env_processes, behavior_ops, mechanisms):
self.sim_config = sim_config
self.state_dict = state_dict
self.seed = seed
self.exogenous_states = exogenous_states
self.env_processes = env_processes
self.behavior_ops = behavior_ops
self.mechanisms = mechanisms

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@ -0,0 +1,18 @@
# from pathos.multiprocessing import ProcessingPool as Pool
#
# class Multiproc(object):
#
# def __init__(self, fs, states_list, configs, env_processes, Ts, Ns):
# self.fs = fs
# self.states_list = states_list
# self.configs = configs
# self.env_processes = env_processes
# self.Ts = Ts
# self.Ns = Ns
#
# def parallelize_simulations(self):
# l = list(zip(self.fs, self.states_list, self.configs, self.env_processes, self.Ts, self.Ns))
# with Pool(len(self.configs)) as p:
# results = p.map(lambda t: t[0](t[1], t[2], t[3], t[4], t[5]), l)
#
# return results

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@ -1,8 +0,0 @@
from pathos.multiprocessing import ProcessingPool as Pool
def parallelize_simulations(fs, states_list, configs, env_processes, T, N):
l = list(zip(fs, states_list, configs, env_processes))
with Pool(len(configs)) as p:
results = p.map(lambda x: x[0](x[1], x[2], x[3], T, N), l)
return results

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@ -1,52 +0,0 @@
import pandas as pd
from tabulate import tabulate
from engine.configProcessor import generate_config
from engine.mechanismExecutor import Executor
from utils.engine import flatten
from utils.ui import create_tensor_field
from engine.multiproc import parallelize_simulations
# from ui.config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
import ui.config1 as conf1
import ui.config2 as conf2
def main():
states_list1 = [conf1.state_dict]
states_list2 = [conf2.state_dict]
states_lists = [states_list1,states_list2]
T = conf1.sim_config['T']
N = conf2.sim_config['N']
ep1 = list(conf1.exogenous_states.values())
ep2 = list(conf2.exogenous_states.values())
eps = [ep1, ep2]
config1 = generate_config(conf1.state_dict, conf1.mechanisms, ep1)
config2 = generate_config(conf2.state_dict, conf2.mechanisms, ep2)
configs = [config1, config2]
env_processes = [conf1.env_processes, conf2.env_processes]
# Dimensions: N x r x mechs
simulation1 = Executor(conf1.behavior_ops).simulation
simulation2 = Executor(conf2.behavior_ops).simulation
simulation_execs = [simulation1,simulation2]
if len(configs) > 1:
simulations = parallelize_simulations(simulation_execs, states_lists, configs, env_processes, T, N)
# else:
# simulations = [simulation(states_list1, configs[0], env_processes, T, N)]
# behavior_ops, states_list, configs, env_processes, time_seq, runs
# result = simulation(states_list1, config1, conf1.env_processes, T, N)
# return pd.DataFrame(flatten(result))
mechanisms = [conf1.mechanisms, conf2.mechanisms]
for result, mechanism, ep in list(zip(simulations, mechanisms, eps)):
print(tabulate(create_tensor_field(mechanism, ep), headers='keys', tablefmt='psql'))
print(tabulate(pd.DataFrame(flatten(result)), headers='keys', tablefmt='psql'))

0
runtime/__init__.py Normal file
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9
runtime/multiproc.py Normal file
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from pathos.multiprocessing import ProcessingPool as Pool
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

49
runtime/run.py Normal file
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import pandas as pd
from tabulate import tabulate
from configuration import configs
from utils.engine import flatten
from utils.ui import create_tensor_field
from engine.configProcessor import generate_config
from engine.mechanism import Executor
from runtime.multiproc import parallelize_simulations
# from ui import config1, config2
def main():
print(configs)
states_lists, Ts, Ns, eps, configs_struct, env_processes, mechanisms, simulation_execs = \
[], [], [], [], [], [], [], []
config_idx = 0
for x in configs:
states_lists.append([x.state_dict])
Ts.append(x.sim_config['T'])
Ns.append(x.sim_config['N'])
eps.append(list(x.exogenous_states.values()))
configs_struct.append(generate_config(x.state_dict, x.mechanisms, eps[config_idx]))
env_processes.append(x.env_processes)
mechanisms.append(x.mechanisms)
simulation_execs.append(Executor(x.behavior_ops).simulation)
config_idx += 1
# Dimensions: N x r x mechs
if len(configs) > 1:
simulations = parallelize_simulations(simulation_execs, states_lists, configs_struct, env_processes, Ts, Ns)
for result, mechanism, ep in list(zip(simulations, mechanisms, eps)):
print(tabulate(create_tensor_field(mechanism, ep), headers='keys', tablefmt='psql'))
print(tabulate(pd.DataFrame(flatten(result)), headers='keys', tablefmt='psql'))
else:
print('single note')
simulation, states_list, config = simulation_execs.pop(), states_lists.pop(), configs_struct.pop()
env_process = env_processes.pop()
# simulations = [simulation(states_list, config, env_processes, T, N)]
# behavior_ops, states_list, configs, env_processes, time_seq, runs
# result = simulation(states_list1, config1, conf1.env_processes, T, N)
# return pd.DataFrame(flatten(result))

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@ -1,4 +1,5 @@
from engine import run
from runtime import run
from ui import config1, config2
from tabulate import tabulate
result = run.main()
print(tabulate(result, headers='keys', tablefmt='psql'))

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@ -1,8 +1,6 @@
from configuration import Config, configs
from utils.configuration import *
from fn.op import foldr
from fn import _
from fn.func import curried
import numpy as np
from decimal import Decimal
@ -160,3 +158,5 @@ sim_config = {
"N": 2,
"T": range(5)
}
configs.append(Config(sim_config, state_dict, seed, exogenous_states, env_processes, behavior_ops, mechanisms))

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@ -1,7 +1,6 @@
from configuration import Config, configs
from utils.configuration import *
from fn.op import foldr
import numpy as np
from decimal import Decimal
@ -160,4 +159,6 @@ mechanisms = {
sim_config = {
"N": 2,
"T": range(5)
}
}
configs.append(Config(sim_config, state_dict, seed, exogenous_states, env_processes, behavior_ops, mechanisms))