multithreaded mech_step, mech_pipeline in progress
This commit is contained in:
parent
980bba081a
commit
f55124fbb0
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@ -2,6 +2,7 @@
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.DS_Store
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.DS_Store
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.idea
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.idea
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notebooks/.ipynb_checkpoints
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notebooks/.ipynb_checkpoints
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notebooks/multithreading.ipynb
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SimCAD.egg-info
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SimCAD.egg-info
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__pycache__
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__pycache__
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Pipfile
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Pipfile
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@ -2,7 +2,9 @@ from datetime import datetime, timedelta
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from decimal import Decimal
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from decimal import Decimal
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from fn.func import curried
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from fn.func import curried
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import pandas as pd
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import pandas as pd
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from pathos.threading import ThreadPool
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from SimCAD.utils import groupByKey
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class TensorFieldReport:
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class TensorFieldReport:
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def __init__(self, config_proc):
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def __init__(self, config_proc):
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@ -18,6 +20,14 @@ class TensorFieldReport:
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return df
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return df
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# def s_update(y, x):
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# return lambda step, sL, s, _input: (y, x)
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#
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#
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def state_update(y, x):
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return lambda step, sL, s, _input: (y, x)
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def bound_norm_random(rng, low, high):
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def bound_norm_random(rng, low, high):
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# Add RNG Seed
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# Add RNG Seed
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res = rng.normal((high+low)/2,(high-low)/6)
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res = rng.normal((high+low)/2,(high-low)/6)
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@ -53,9 +63,15 @@ def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', _timedelta = t_delta
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def exo_update_per_ts(ep):
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def exo_update_per_ts(ep):
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@curried
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@curried
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def ep_decorator(f, y, step, sL, s, _input):
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def ep_decorator(fs, y, step, sL, s, _input):
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# print(s)
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if s['mech_step'] + 1 == 1: # inside f body to reduce performance costs
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if s['mech_step'] + 1 == 1: # inside f body to reduce performance costs
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return f(step, sL, s, _input)
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if isinstance(fs, list):
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pool = ThreadPool(nodes=len(fs))
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fx = pool.map(lambda f: f(step, sL, s, _input), fs)
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return groupByKey(fx)
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else:
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return fs(step, sL, s, _input)
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else:
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else:
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return (y, s[y])
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return (y, s[y])
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return {es: ep_decorator(f, es) for es, f in ep.items()}
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return {es: ep_decorator(f, es) for es, f in ep.items()}
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@ -2,14 +2,15 @@ from fn.op import foldr
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from fn.func import curried
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from fn.func import curried
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def get_base_value(datatype):
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def get_base_value(x):
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if datatype is str:
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if isinstance(x, str):
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return ''
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return ''
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elif datatype is int:
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elif isinstance(x, int):
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return 0
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return 0
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elif datatype is list:
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elif isinstance(x, list):
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return []
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return []
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return 0
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else:
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return 0
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def behavior_to_dict(v):
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def behavior_to_dict(v):
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@ -33,7 +34,7 @@ def sum_dict_values():
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def dict_op(f, d1, d2):
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def dict_op(f, d1, d2):
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def set_base_value(target_dict, source_dict, key):
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def set_base_value(target_dict, source_dict, key):
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if key not in target_dict:
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if key not in target_dict:
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return get_base_value(type(source_dict[key]))
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return get_base_value(source_dict[key])
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else:
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else:
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return target_dict[key]
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return target_dict[key]
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@ -1,7 +1,11 @@
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from pathos.threading import ThreadPool
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from copy import deepcopy
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from copy import deepcopy
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from fn.op import foldr, call
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from fn.op import foldr, call
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import pprint
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from SimCAD.utils import rename
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pp = pprint.PrettyPrinter(indent=4)
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from SimCAD.utils import groupByKey, flatten, drop_right
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from SimCAD.engine.utils import engine_exception
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from SimCAD.engine.utils import engine_exception
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@ -23,23 +27,42 @@ class Executor:
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return foldr(call, get_col_results(step, sL, s, funcs))(ops)
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return foldr(call, get_col_results(step, sL, s, funcs))(ops)
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def xthreaded_env_proc(self, f, s_valx):
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if isinstance(s_valx, list):
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pool = ThreadPool(nodes=len(s_valx)) # ToDo: Optimize
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return pool.map(lambda f: f(s_valx), s_valx)
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else:
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return f(s_valx)
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def apply_env_proc(self, env_processes, state_dict, step):
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def apply_env_proc(self, env_processes, state_dict, step):
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for state in state_dict.keys():
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for state in state_dict.keys():
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if state in list(env_processes.keys()):
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if state in list(env_processes.keys()):
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env_state = env_processes[state]
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env_state = env_processes[state]
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if (env_state.__name__ == '_curried') or (env_state.__name__ == 'proc_trigger'): # might want to change
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if (env_state.__name__ == '_curried') or (env_state.__name__ == 'proc_trigger'): # might want to change
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state_dict[state] = env_state(step)(state_dict[state])
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state_dict[state] = self.xthreaded_env_proc(env_state(step), state_dict[state])
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else:
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else:
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state_dict[state] = env_state(state_dict[state])
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state_dict[state] = self.xthreaded_env_proc(env_state, state_dict[state])
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def xthreaded_state_update(self, fs, m_step, sL, last_in_obj, _input):
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if isinstance(fs, list):
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pool = ThreadPool(nodes=len(fs)) # ToDo: Optimize
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fx = pool.map(lambda f: f(m_step, sL, last_in_obj, _input), fs)
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return groupByKey(fx)
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else:
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return fs(m_step, sL, last_in_obj, _input)
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def mech_step(self, m_step, sL, state_funcs, behavior_funcs, env_processes, t_step, run):
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def mech_step(self, m_step, sL, state_funcs, behavior_funcs, env_processes, t_step, run):
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last_in_obj = sL[-1]
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last_in_obj = sL[-1]
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_input = self.state_update_exception(self.get_behavior_input(m_step, sL, last_in_obj, behavior_funcs))
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_input = self.state_update_exception(self.get_behavior_input(m_step, sL, last_in_obj, behavior_funcs))
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# ToDo: add env_proc generator to `last_in_copy` iterator as wrapper function
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# ToDo: add env_proc generator to `last_in_copy` iterator as wrapper function
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last_in_copy = dict([self.behavior_update_exception(f(m_step, sL, last_in_obj, _input)) for f in state_funcs])
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last_in_copy = dict([
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self.behavior_update_exception(
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self.xthreaded_state_update(f, m_step, sL, last_in_obj, _input)
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) for f in state_funcs
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])
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for k in last_in_obj:
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for k in last_in_obj:
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if k not in last_in_copy:
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if k not in last_in_copy:
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# make env proc trigger field agnostic
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# make env proc trigger field agnostic
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self.apply_env_proc(env_processes, last_in_copy, last_in_copy['timestamp']) # mutating last_in_copy
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self.apply_env_proc(env_processes, last_in_copy, last_in_copy['timestamp']) # mutating last_in_copy
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# print()
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# pp.pprint(last_in_copy)
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# exit()
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def set_sys_metrics(m_step, t_step, run):
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last_in_copy["mech_step"], last_in_copy["time_step"], last_in_copy['run'] = m_step, t_step, run
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if any(isinstance(x, list) for x in last_in_copy.values()):
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last_in_copies = flatten(last_in_copy)
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for last_in_copy in last_in_copies:
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set_sys_metrics(m_step, t_step, run)
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sL.append(last_in_copies)
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else:
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set_sys_metrics(m_step, t_step, run)
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sL.append(last_in_copy)
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last_in_copy["mech_step"], last_in_copy["time_step"], last_in_copy['run'] = m_step, t_step, run
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sL.append(last_in_copy)
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del last_in_copy
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del last_in_copy
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# print()
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# pp.pprint(sL)
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# exit()
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return sL
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return sL
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def mech_pipeline(self, states_list, configs, env_processes, t_step, run):
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def mech_pipeline(self, states_list, configs, env_processes, t_step, run):
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genesis_states = states_list_copy[-1]
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genesis_states = states_list_copy[-1]
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genesis_states['mech_step'], genesis_states['time_step'] = m_step, t_step
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genesis_states['mech_step'], genesis_states['time_step'] = m_step, t_step
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states_list = [genesis_states]
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states_list = [genesis_states]
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# print(genesis_states)
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m_step += 1
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m_step += 1
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for config in configs:
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for config in configs:
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s_conf, b_conf = config[0], config[1]
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s_conf, b_conf = config[0], config[1]
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last_states = states_list[-1]
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dropped_right_sL = drop_right(states_list, 1)
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print()
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# print(states_list)
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# if isinstance(last_states, list):
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# x = list(map(lambda last_state_dict: states_list.pop().append(last_state_dict), last_states))
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# pp.pprint(states_list)
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states_list = self.mech_step(m_step, states_list, s_conf, b_conf, env_processes, t_step, run)
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states_list = self.mech_step(m_step, states_list, s_conf, b_conf, env_processes, t_step, run)
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m_step += 1
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m_step += 1
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t_step += 1
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t_step += 1
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print()
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# print(states_list)
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exit()
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return states_list
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return states_list
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# rename pipe
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# rename pipe
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@ -1,5 +1,7 @@
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from datetime import datetime
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from datetime import datetime
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from fn.func import curried
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from fn.func import curried
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from SimCAD.utils import rename
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# from SimCAD.configuration.utils import s_update
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def datetime_range(start, end, delta, dt_format='%Y-%m-%d %H:%M:%S'):
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def datetime_range(start, end, delta, dt_format='%Y-%m-%d %H:%M:%S'):
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return l[last_index(l) + offset + 1]
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return l[last_index(l) + offset + 1]
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# exception_function = f(m_step, sL, sL[-2], _input)
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# try_function = f(m_step, sL, last_mut_obj, _input)
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@curried
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@curried
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def engine_exception(ErrorType, error_message, exception_function, try_function):
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def engine_exception(ErrorType, error_message, exception_function, try_function):
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try:
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try:
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@ -33,9 +37,11 @@ def engine_exception(ErrorType, error_message, exception_function, try_function)
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return exception_function
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return exception_function
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# def exception_handler(f, m_step, sL, last_mut_obj, _input):
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@curried
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# try:
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def fit_param(param, x):
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# return f(m_step, sL, last_mut_obj, _input)
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return x + param
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# except KeyError:
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# print("Exception")
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# fit_param = lambda param: lambda x: x + param
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# return f(m_step, sL, sL[-2], _input)
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def sweep(params, sweep_f):
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return [rename('sweep', sweep_f(param)) for param in params]
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from collections import defaultdict
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from itertools import product
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# from fn.func import curried
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# from fn.func import curried
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def pipe(x):
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def pipe(x):
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return x
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return x
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def flattenDict(l):
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def tupalize(k, vs):
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l = []
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if isinstance(vs, list):
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for v in vs:
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l.append((k, v))
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else:
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l.append((k, vs))
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return l
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flat_list = [tupalize(k, vs) for k, vs in l.items()]
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flat_dict = [dict(items) for items in product(*flat_list)]
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return flat_dict
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def flatten(l):
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def flatten(l):
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return [item for sublist in l for item in sublist]
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if isinstance(l, list):
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return [item for sublist in l for item in sublist]
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elif isinstance(l, dict):
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return flattenDict(l)
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def flatmap(f, items):
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def drop_right(l, n=1):
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return list(map(f, items))
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return l[:len(l)-n]
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# def flatmap(f, items):
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# return list(map(f, items))
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def key_filter(l, keyname):
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def key_filter(l, keyname):
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return [v[keyname] for k, v in l.items()]
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return [v[keyname] for k, v in l.items()]
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def groupByKey(l):
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d = defaultdict(list)
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for key, value in l:
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d[key].append(value)
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return list(dict(d).items()).pop()
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# @curried
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# @curried
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def rename(new_name, f):
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def rename(new_name, f):
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f.__name__ = new_name
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f.__name__ = new_name
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@ -29,16 +29,16 @@ print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
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print("Output:")
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print("Output:")
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print(tabulate(result, headers='keys', tablefmt='psql'))
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print(tabulate(result, headers='keys', tablefmt='psql'))
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print()
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print()
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#
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print("Simulation Execution 2: Pairwise Execution")
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# print("Simulation Execution 2: Pairwise Execution")
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print()
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# print()
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multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
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# multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
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run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
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# run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
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for raw_result, tensor_field in run2.main():
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# for raw_result, tensor_field in run2.main():
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result = pd.DataFrame(raw_result)
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# result = pd.DataFrame(raw_result)
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print()
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# print()
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print("Tensor Field:")
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# print("Tensor Field:")
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print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
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# print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
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print("Output:")
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# print("Output:")
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print(tabulate(result, headers='keys', tablefmt='psql'))
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# print(tabulate(result, headers='keys', tablefmt='psql'))
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print()
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# print()
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@ -4,8 +4,9 @@ from datetime import timedelta
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from SimCAD import configs
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from SimCAD import configs
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from SimCAD.configuration import Configuration
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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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from SimCAD.configuration.utils import state_update, exo_update_per_ts, proc_trigger, bound_norm_random, \
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ep_time_step
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ep_time_step
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from SimCAD.engine.utils import sweep
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seed = {
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seed = {
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'z': np.random.RandomState(1),
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'z': np.random.RandomState(1),
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@ -42,6 +43,14 @@ def s2m1(step, sL, s, _input):
|
||||||
x = _input['param2'] #+ [Coef2 x 5]
|
x = _input['param2'] #+ [Coef2 x 5]
|
||||||
return (y, x)
|
return (y, x)
|
||||||
|
|
||||||
|
s2m1 = sweep(
|
||||||
|
params = [Decimal(11.0), Decimal(22.0)],
|
||||||
|
sweep_f = lambda param: lambda step, sL, s, _input: (
|
||||||
|
's2',
|
||||||
|
s['s2'] + param
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
def s1m2(step, sL, s, _input):
|
def s1m2(step, sL, s, _input):
|
||||||
y = 's1'
|
y = 's1'
|
||||||
x = _input['param1']
|
x = _input['param1']
|
||||||
|
|
@ -64,10 +73,20 @@ def s2m3(step, sL, s, _input):
|
||||||
proc_one_coef_A = 0.7
|
proc_one_coef_A = 0.7
|
||||||
proc_one_coef_B = 1.3
|
proc_one_coef_B = 1.3
|
||||||
|
|
||||||
def es3p1(step, sL, s, _input):
|
# def es3p1(step, sL, s, _input):
|
||||||
y = 's3'
|
# y = 's3'
|
||||||
x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
|
# x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
|
||||||
return (y, x)
|
# return (y, x)
|
||||||
|
|
||||||
|
|
||||||
|
es3p1 = sweep(
|
||||||
|
params = [Decimal(11.0), Decimal(22.0)],
|
||||||
|
sweep_f = lambda param: lambda step, sL, s, _input: (
|
||||||
|
's3',
|
||||||
|
s['s3'] + param
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
def es4p2(step, sL, s, _input):
|
def es4p2(step, sL, s, _input):
|
||||||
y = 's4'
|
y = 's4'
|
||||||
|
|
@ -111,7 +130,7 @@ exogenous_states = exo_update_per_ts(
|
||||||
# ToDo: make env proc trigger field agnostic
|
# ToDo: make env proc trigger field agnostic
|
||||||
# ToDo: input json into function renaming __name__
|
# ToDo: input json into function renaming __name__
|
||||||
env_processes = {
|
env_processes = {
|
||||||
"s3": env_a,
|
# "s3": env_a,
|
||||||
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
|
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue