181 lines
3.8 KiB
Python
181 lines
3.8 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 cadCAD.configuration import append_configs
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from cadCAD.configuration.utils import env_proc_trigger, ep_time_step, config_sim
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from typing import Dict, List
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pp = pprint.PrettyPrinter(indent=4)
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seeds = {
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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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# Optional
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g: Dict[str, List[int]] = {
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'alpha': [1],
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'beta': [2, 5],
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'gamma': [3, 4],
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'omega': [7]
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}
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# Policies per Mechanism
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def p1m1(_g, step, sL, s):
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return {'param1': 1}
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def p2m1(_g, step, sL, s):
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return {'param2': 4}
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def p1m2(_g, step, sL, s):
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return {'param1': 'a', 'param2': _g['beta']}
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def p2m2(_g, step, sL, s):
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return {'param1': 'b', 'param2': 0}
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def p1m3(_g, step, sL, s):
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return {'param1': np.array([10, 100])}
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def p2m3(_g, 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(_g, step, sL, s, _input):
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return 's1', 0
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def s2m1(_g, step, sL, s, _input):
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return 's2', _g['beta']
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def s1m2(_g, step, sL, s, _input):
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return 's1', _input['param2']
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def s2m2(_g, step, sL, s, _input):
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return 's2', _input['param2']
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def s1m3(_g, step, sL, s, _input):
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return 's1', 0
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def s2m3(_g, step, sL, s, _input):
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return 's2', 0
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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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def es3p1(_g, step, sL, s, _input):
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return 's3', _g['gamma']
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# @curried
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def es4p2(_g, step, sL, s, _input):
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return 's4', _g['gamma']
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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(_g, step, sL, s, _input):
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y = 'timestep'
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x = ep_time_step(s, dt_str=s['timestep'], 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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# 'timestep': '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": es3p1,
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"s4": es4p2,
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# "timestep": es5p2
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}
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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 = env_proc_trigger(1, 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 entered 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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partial_state_update_block = {
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"m1": {
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"policies": {
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"b1": p1m1,
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"b2": p2m1
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},
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"variables": {
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"s1": s1m1,
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"s2": s2m1
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}
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},
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"m2": {
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"policies": {
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"b1": p1m2,
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"b2": p2m2,
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},
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"variables": {
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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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"policies": {
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"b1": p1m3,
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"b2": p2m3
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},
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"variables": {
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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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# config_sim Necessary
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sim_config = config_sim(
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{
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"N": 2,
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"T": range(5),
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"M": g # Optional
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}
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)
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# New Convention
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append_configs(
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sim_configs=sim_config,
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initial_state=genesis_states,
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seeds=seeds,
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raw_exogenous_states=raw_exogenous_states,
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env_processes=env_processes,
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partial_state_update_blocks=partial_state_update_block
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) |