266 lines
6.2 KiB
Python
266 lines
6.2 KiB
Python
from decimal import Decimal
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import numpy as np
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from datetime import timedelta
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import pprint
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from SimCAD import configs
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from SimCAD.configuration import Configuration
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from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
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ep_time_step, parameterize_mechanism, parameterize_states, sweep
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pp = pprint.PrettyPrinter(indent=4)
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# ToDo: handle single param sweep
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beta = [Decimal(1), Decimal(2)]
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seed = {
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'z': np.random.RandomState(1),
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'a': np.random.RandomState(2),
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'b': np.random.RandomState(3),
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'c': np.random.RandomState(3)
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}
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# Behaviors per Mechanism
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def b1m1(step, sL, s):
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return {'param1': 1}
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def b2m1(step, sL, s):
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return {'param2': 4}
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# @curried
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def b1m2(param, step, sL, s):
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return {'param1': 'a', 'param2': param}
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def b2m2(step, sL, s):
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return {'param1': 'b', 'param2': 0}
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def b1m3(step, sL, s):
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return {'param1': np.array([10, 100])}
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def b2m3(step, sL, s):
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return {'param1': np.array([20, 200])}
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# Internal States per Mechanism
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def s1m1(step, sL, s, _input):
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y = 's1'
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x = 0
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return (y, x)
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# @curried
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def s2m1(param, step, sL, s, _input):
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y = 's2'
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x = param
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return (y, x)
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def s1m2(step, sL, s, _input):
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y = 's1'
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x = _input['param2']
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return (y, x)
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def s2m2(step, sL, s, _input):
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y = 's2'
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x = _input['param2']
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return (y, x)
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def s1m3(step, sL, s, _input):
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y = 's1'
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x = 0
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return (y, x)
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def s2m3(step, sL, s, _input):
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y = 's2'
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x = 0
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return (y, x)
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# Exogenous States
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proc_one_coef_A = 0.7
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proc_one_coef_B = 1.3
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# @curried
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def es3p1(param, step, sL, s, _input):
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y = 's3'
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x = s['s3'] + param
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return (y, x)
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def es4p2(param, step, sL, s, _input):
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y = 's4'
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x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B) + param
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return (y, x)
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ts_format = '%Y-%m-%d %H:%M:%S'
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t_delta = timedelta(days=0, minutes=0, seconds=1)
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def es5p2(step, sL, s, _input):
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y = 'timestamp'
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x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
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return (y, x)
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# Environment States
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# @curried
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# def env_a(param, x):
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# return x + param
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def env_a(x):
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return x
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def env_b(x):
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return 10
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# Genesis States
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genesis_states = {
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's1': Decimal(0.0),
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's2': Decimal(0.0),
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's3': Decimal(1.0),
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's4': Decimal(1.0),
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'timestamp': '2018-10-01 15:16:24'
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}
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# remove `exo_update_per_ts` to update every ts
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raw_exogenous_states = {
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"s3": sweep(beta, es3p1), #es3p1, #sweep(beta, es3p1),
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"s4": sweep(beta, es4p2),
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"timestamp": es5p2
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}
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exogenous_states_list = list(map(exo_update_per_ts, parameterize_states(raw_exogenous_states)))
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# ToDo: make env proc trigger field agnostic
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# ToDo: input json into function renaming __name__
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triggered_env_b = proc_trigger('2018-10-01 15:16:25', env_b)
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env_processes = {
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"s3": env_a, #sweep(beta, env_a),
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"s4": triggered_env_b #rename('parameterized', triggered_env_b) #sweep(beta, triggered_env_b)
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}
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# parameterized_env_processes = parameterize_states(env_processes)
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#
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# pp.pprint(parameterized_env_processes)
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# exit()
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# ToDo: The number of values enteren in sweep should be the # of config objs created,
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# not dependent on the # of times the sweep is applied
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# sweep exo_state func and point to exo-state in every other funtion
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# param sweep on genesis states
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# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
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# mechanisms = {}
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#middleware(beta, [(m1, states, s2, s2m1), (m2, behaviors, b1, b1m2)], mechanisms)
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mechanisms_test = {
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"m1": {
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"behaviors": {
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"b1": b1m1,
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"b2": b2m1
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},
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"states": {
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"s1": s1m1,
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"s2": "sweep"
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}
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},
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"m2": {
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"behaviors": {
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"b1": "sweep",
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"b2": b2m2
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},
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"states": {
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"s1": s1m2,
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"s2": s2m2
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}
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},
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"m3": {
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"behaviors": {
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"b1": b1m3,
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"b2": b2m3
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},
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"states": {
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"s1": s1m3,
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"s2": s2m3
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}
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}
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}
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from copy import deepcopy
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from funcy import curry
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def sweep_identifier(sweep_list, sweep_id_list, mechanisms):
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new_mechanisms = deepcopy(mechanisms)
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for x in sweep_id_list:
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mech, update_type, update, f = x[0], x[1], x[2], x[3]
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current_f = new_mechanisms[x[0]][x[1]][x[2]]
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if current_f is 'sweep':
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new_mechanisms[x[0]][x[1]][x[2]] = sweep(sweep_list, x[3])
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# for mech, update_types in new_mechanisms.items():
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# for update_type, fkv in update_types.items():
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# for sk, current_f in fkv.items():
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# if current_f != 'sweep' and isinstance(current_f, list) == False:
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# new_mechanisms[mech][update_type][sk] = curry(f)(0)
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del mechanisms
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return new_mechanisms
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sweep_id_list = [('m1', 'states', 's2', s2m1), ('m2', 'behaviors', 'b1', b1m2)]
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# pp.pprint(sweep_identifier(beta, sweep_id_list, mechanisms_test))
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mechanisms = sweep_identifier(beta, sweep_id_list, mechanisms_test)
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# mechanisms = {
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# "m1": {
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# "behaviors": {
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# "b1": b1m1,
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# "b2": b2m1
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# },
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# "states": {
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# "s1": s1m1,
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# "s2": sweep(beta, s2m1) #s2m1(1) #sweep(beta, s2m1)
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# }
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# },
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# "m2": {
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# "behaviors": {
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# "b1": sweep(beta, b1m2), #b1m2(1) #sweep(beta, b1m2),
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# "b2": b2m2
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# },
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# "states": {
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# "s1": s1m2,
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# "s2": s2m2
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# }
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# },
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# "m3": {
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# "behaviors": {
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# "b1": b1m3,
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# "b2": b2m3
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# },
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# "states": {
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# "s1": s1m3,
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# "s2": s2m3
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# }
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# }
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# }
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parameterized_mechanism = parameterize_mechanism(mechanisms)
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pp.pprint(parameterized_mechanism)
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# exit()
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sim_config = {
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"N": 2,
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"T": range(5)
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}
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for mechanisms, exogenous_states in zip(parameterized_mechanism, exogenous_states_list):
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configs.append(
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Configuration(
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sim_config=sim_config,
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state_dict=genesis_states,
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seed=seed,
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exogenous_states=exogenous_states, #parameterize_states(raw_exogenous_states)[1],
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env_processes=env_processes,
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mechanisms=mechanisms #parameterize_mechanism(mechanisms)[1]
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)
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)
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