Merge pull request #13 from BlockScience/jj-dev

Bug Fix: Can't use environments without proc_trigger
This commit is contained in:
Joshua E. Jodesty 2018-12-04 15:28:22 -05:00 committed by GitHub
commit e3179a6e8e
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12 changed files with 428 additions and 44 deletions

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@ -46,9 +46,9 @@ exec_mode = ExecutionMode()
print("Simulation Run 1")
print()
single_config = [configs[0]]
first_config = [configs[0]] # from config1
single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
run1 = Executor(exec_context=single_proc_ctx, configs=single_config)
run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
run1_raw_result = run1.main()
result = pd.DataFrame(run1_raw_result)
# result.to_csv('~/Projects/DiffyQ-SimCAD/results/config4csv', sep=',')

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@ -40,7 +40,6 @@ class Identity:
class Processor:
def __init__(self, id=Identity()):
self.id = id
self.b_identity = id.b_identity
@ -65,7 +64,8 @@ class Processor:
# Maybe Refactor to only use dictionary BUT I used dfs to fill NAs. Perhaps fill
def generate_config(self, state_dict, mechanisms, exo_proc):
# include False / False case
# ToDo: include False / False case
# ToDo: Use Range multiplier instead for loop iterator
def no_update_handler(bdf, sdf):
if (bdf.empty == False) and (sdf.empty == True):
bdf_values = bdf.values.tolist()
@ -88,7 +88,6 @@ class Processor:
bdf_values = [[self.b_identity] * len(sdf_values)]
return sdf_values, bdf_values
# zipped_list = []
if len(mechanisms) != 0:
bdf = self.create_matrix_field(mechanisms, 'behaviors')
sdf = self.create_matrix_field(mechanisms, 'states')

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@ -3,6 +3,7 @@ from decimal import Decimal
from fn.func import curried
import pandas as pd
class TensorFieldReport:
def __init__(self, config_proc):
self.config_proc = config_proc

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@ -28,7 +28,6 @@ class ExecutionContext:
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':

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@ -1,44 +1,45 @@
from copy import deepcopy
from fn.op import foldr, call
import pprint
pp = pprint.PrettyPrinter(indent=4)
from SimCAD.utils import rename
from SimCAD.engine.utils import engine_exception
id_exception = engine_exception(KeyError, KeyError, None)
class Executor:
def __init__(self, behavior_ops):
def __init__(self, behavior_ops, behavior_update_exception=id_exception, state_update_exception=id_exception):
self.behavior_ops = behavior_ops
self.state_update_exception = state_update_exception
self.behavior_update_exception = behavior_update_exception
# Data Type reduction
def getBehaviorInput(self, step, sL, s, funcs):
def get_behavior_input(self, step, sL, s, funcs):
ops = self.behavior_ops[::-1]
def getColResults(step, sL, s, funcs):
def get_col_results(step, sL, s, funcs):
return list(map(lambda f: f(step, sL, s), funcs))
return foldr(call, getColResults(step, sL, s, funcs))(ops)
return foldr(call, get_col_results(step, sL, s, funcs))(ops)
def apply_env_proc(self, env_processes, state_dict, step):
for state in state_dict.keys():
if state in list(env_processes.keys()):
state_dict[state] = env_processes[state](step)(state_dict[state])
env_state = env_processes[state]
if env_state.__name__ == '_curried': # might want to change
state_dict[state] = env_state(step)(state_dict[state])
else:
state_dict[state] = env_state(state_dict[state])
# remove / modify
def exception_handler(self, f, m_step, sL, last_mut_obj, _input):
try:
return f(m_step, sL, last_mut_obj, _input)
except KeyError:
print("Exception")
return f(m_step, sL, sL[-2], _input)
def mech_step(self, m_step, sL, state_funcs, behavior_funcs, env_processes, t_step, run):
last_in_obj = sL[-1]
_input = self.getBehaviorInput(m_step, sL, last_in_obj, behavior_funcs)
_input = self.state_update_exception(self.get_behavior_input(m_step, sL, last_in_obj, behavior_funcs))
# print(sL)
# *** add env_proc value here as wrapper function ***
last_in_copy = dict([self.exception_handler(f, m_step, sL, last_in_obj, _input) for f in state_funcs])
# ToDo: add env_proc generator to `last_in_copy` iterator as wrapper function
last_in_copy = dict([self.behavior_update_exception(f(m_step, sL, last_in_obj, _input)) for f in state_funcs])
for k in last_in_obj:
if k not in last_in_copy:
@ -46,7 +47,7 @@ class Executor:
del last_in_obj
# make env proc trigger field agnostic
# make env proc trigger field agnostic
self.apply_env_proc(env_processes, last_in_copy, last_in_copy['timestamp']) # mutating last_in_copy
last_in_copy["mech_step"], last_in_copy["time_step"], last_in_copy['run'] = m_step, t_step, run
@ -79,10 +80,7 @@ class Executor:
time_seq = [x + 1 for x in time_seq]
simulation_list = [states_list]
for time_step in time_seq:
# print(run)
pipe_run = self.mech_pipeline(simulation_list[-1], configs, env_processes, time_step, run)
# pp.pprint(pipe_run)
# exit()
_, *pipe_run = pipe_run
simulation_list.append(pipe_run)
@ -93,7 +91,6 @@ class Executor:
pipe_run = []
for run in range(runs):
run += 1
# print("Run: "+str(run))
states_list_copy = deepcopy(states_list) # WHY ???
head, *tail = self.block_pipeline(states_list_copy, configs, env_processes, time_seq, run)
genesis = head.pop()

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@ -1,4 +1,5 @@
from datetime import datetime
from fn.func import curried
def datetime_range(start, end, delta, dt_format='%Y-%m-%d %H:%M:%S'):
@ -20,4 +21,21 @@ def last_index(l):
def retrieve_state(l, offset):
return l[last_index(l) + offset + 1]
return l[last_index(l) + offset + 1]
@curried
def engine_exception(ErrorType, error_message, exception_function, try_function):
try:
return try_function
except ErrorType:
print(error_message)
return exception_function
# def exception_handler(f, m_step, sL, last_mut_obj, _input):
# try:
# return f(m_step, sL, last_mut_obj, _input)
# except KeyError:
# print("Exception")
# return f(m_step, sL, sL[-2], _input)

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@ -1,14 +1,32 @@
def print_fwd(x):
# from fn.func import curried
def pipe(x):
return x
def print_pipe(x):
print(x)
return x
flatten = lambda l: [item for sublist in l for item in sublist]
def flatten(l):
return [item for sublist in l for item in sublist]
def flatmap(f, items):
return list(map(f, items))
return list(map(f, items))
def key_filter(l, keyname):
return [v[keyname] for k, v in l.items()]
return [v[keyname] for k, v in l.items()]
# @curried
def rename(new_name, f):
f.__name__ = new_name
return f
#
# def rename(newname):
# def decorator(f):
# f.__name__ = newname
# return f
# return decorator

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@ -4,6 +4,7 @@ from tabulate import tabulate
# The following imports NEED to be in the exact same order
from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import config1, config2
# from simulations.validation import base_config1, base_config2
# from simulations.barlin import config4
# from simulations.zx import config_zx
# from simulations.barlin import config6atemp #config6aworks,
@ -14,18 +15,18 @@ from SimCAD import configs
exec_mode = ExecutionMode()
print("Simulation Run 1")
print("Simulation Execution 1")
print()
single_config = [configs[0]]
first_config = [configs[0]] # from config1
single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
run1 = Executor(exec_context=single_proc_ctx, configs=single_config)
run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
run1_raw_result = run1.main()
result = pd.DataFrame(run1_raw_result)
# result.to_csv('~/Projects/DiffyQ-SimCAD/results/config4.csv', sep=',')
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
print("Simulation Run 2: Pairwise Execution")
print("Simulation Execution 2: Pairwise Execution")
print()
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)

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@ -0,0 +1,171 @@
from decimal import Decimal
import numpy as np
from datetime import timedelta
from SimCAD import configs
from SimCAD.configuration import Configuration
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
ep_time_step
seed = {
'z': np.random.RandomState(1),
'a': np.random.RandomState(2),
'b': np.random.RandomState(3),
'c': np.random.RandomState(3)
}
# Behaviors per Mechanism
# Different return types per mechanism ?? *** No ***
def b1m1(step, sL, s):
return {'param1': 1}
def b2m1(step, sL, s):
return {'param1': 1}
def b1m2(step, sL, s):
return {'param1': 1, 'param2': 2}
def b2m2(step, sL, s):
return {'param1': 1, 'param2': 4}
def b1m3(step, sL, s):
return {'param1': 1, 'param2': np.array([10, 100])}
def b2m3(step, sL, s):
return {'param1': 1, 'param2': np.array([20, 200])}
# deff not more than 2
# Internal States per Mechanism
def s1m1(step, sL, s, _input):
y = 's1'
x = s['s1'] + _input['param1']
return (y, x)
def s2m1(step, sL, s, _input):
y = 's2'
x = s['s2'] + _input['param1']
return (y, x)
def s1m2(step, sL, s, _input):
y = 's1'
x = s['s1'] + _input['param1']
return (y, x)
def s2m2(step, sL, s, _input):
y = 's2'
x = s['s2'] + _input['param1']
return (y, x)
def s1m3(step, sL, s, _input):
y = 's1'
x = s['s1'] + _input['param1']
return (y, x)
def s2m3(step, sL, s, _input):
y = 's2'
x = s['s2'] + _input['param1']
return (y, x)
# Exogenous States
proc_one_coef_A = 0.7
proc_one_coef_B = 1.3
def es3p1(step, sL, s, _input):
y = 's3'
x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
return (y, x)
def es4p2(step, sL, s, _input):
y = 's4'
x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
return (y, x)
ts_format = '%Y-%m-%d %H:%M:%S'
t_delta = timedelta(days=0, minutes=0, seconds=1)
def es5p2(step, sL, s, _input):
y = 'timestamp'
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
return (y, x)
# Environment States
def env_a(x):
return 10
def env_b(x):
return 10
# def what_ever(x):
# return x + 1
# Genesis States
genesis_states = {
's1': Decimal(0.0),
's2': Decimal(0.0),
's3': Decimal(1.0),
's4': Decimal(1.0),
'timestamp': '2018-10-01 15:16:24'
}
# remove `exo_update_per_ts` to update every ts
exogenous_states = exo_update_per_ts(
{
"s3": es3p1,
"s4": es4p2,
"timestamp": es5p2
}
)
# make env proc trigger field agnostic
# ToDo: Bug - Can't use environments without proc_trigger. TypeError: 'int' object is not callable
# "/Users/jjodesty/Projects/DiffyQ-SimCAD/SimCAD/engine/simulation.py"
env_processes = {
# "s3": env_a,
# "s4": env_b
"s3": proc_trigger('2018-10-01 15:16:25', env_a),
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
}
# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
# mechanisms = {}
mechanisms = {
"m1": {
"behaviors": {
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
"b2": b2m1
},
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
"s1": s1m1,
"s2": s2m1
}
},
"m2": {
"behaviors": {
"b1": b1m2,
"b2": b2m2
},
"states": {
"s1": s1m2,
"s2": s2m2
}
},
"m3": {
"behaviors": {
"b1": b1m3,
"b2": b2m3
},
"states": {
"s1": s1m3,
"s2": s2m3
}
}
}
sim_config = {
"N": 2,
"T": range(5)
}
configs.append(
Configuration(
sim_config=sim_config,
state_dict=genesis_states,
seed=seed,
exogenous_states=exogenous_states,
env_processes=env_processes,
mechanisms=mechanisms
)
)

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@ -0,0 +1,180 @@
from decimal import Decimal
import numpy as np
from datetime import timedelta
from SimCAD import configs
from SimCAD.configuration import Configuration
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
ep_time_step
seed = {
'z': np.random.RandomState(1),
'a': np.random.RandomState(2),
'b': np.random.RandomState(3),
'c': np.random.RandomState(3)
}
# Behaviors per Mechanism
# Different return types per mechanism ?? *** No ***
def b1m1(step, sL, s):
return {'param1': 1}
def b2m1(step, sL, s):
return {'param2': 4}
def b1m2(step, sL, s):
return {'param1': 'a', 'param2': 2}
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):
return {'param1': ['d'], 'param2': np.array([20, 200])}
# Internal States per Mechanism
def s1m1(step, sL, s, _input):
y = 's1'
x = _input['param1']
return (y, x)
def s2m1(step, sL, s, _input):
y = 's2'
x = _input['param2']
return (y, x)
def s1m2(step, sL, s, _input):
y = 's1'
x = _input['param1']
return (y, x)
def s2m2(step, sL, s, _input):
y = 's2'
x = _input['param2']
return (y, x)
def s1m3(step, sL, s, _input):
y = 's1'
x = _input['param1']
return (y, x)
def s2m3(step, sL, s, _input):
y = 's2'
x = _input['param2']
return (y, x)
# Exogenous States
proc_one_coef_A = 0.7
proc_one_coef_B = 1.3
def es3p1(step, sL, s, _input):
y = 's3'
x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
return (y, x)
def es4p2(step, sL, s, _input):
y = 's4'
x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
return (y, x)
ts_format = '%Y-%m-%d %H:%M:%S'
t_delta = timedelta(days=0, minutes=0, seconds=1)
def es5p2(step, sL, s, _input):
y = 'timestamp'
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
return (y, x)
# Environment States
def env_a(x):
return 10
def env_b(x):
return 10
# def what_ever(x):
# return x + 1
# Genesis States
genesis_states = {
's1': Decimal(0.0),
's2': Decimal(0.0),
's3': Decimal(1.0),
's4': Decimal(1.0),
'timestamp': '2018-10-01 15:16:24'
}
# remove `exo_update_per_ts` to update every ts
# why `exo_update_per_ts` here instead of `env_processes`
exogenous_states = exo_update_per_ts(
{
"s3": es3p1,
"s4": es4p2,
"timestamp": es5p2
}
)
# make env proc trigger field agnostic
env_processes = {
"s3": proc_trigger('2018-10-01 15:16:25', env_a),
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
}
# lambdas
# genesis Sites should always be there
# [1, 2]
# behavior_ops = [ foldr(_ + _), lambda x: x + 0 ]
# [1, 2] = {'b1': ['a'], 'b2', [1]} =
# behavior_ops = [behavior_to_dict, print_fwd, sum_dict_values]
# behavior_ops = [foldr(dict_elemwise_sum())]
# behavior_ops = []
# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
# mechanisms = {}
mechanisms = {
"m1": {
"behaviors": {
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
# "b2": b2m1
},
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
"s1": s1m1,
# "s2": s2m1
}
},
"m2": {
"behaviors": {
"b1": b1m2,
# "b2": b2m2
},
"states": {
"s1": s1m2,
# "s2": s2m2
}
},
"m3": {
"behaviors": {
"b1": b1m3,
"b2": b2m3
},
"states": {
"s1": s1m3,
"s2": s2m3
}
}
}
sim_config = {
"N": 2,
"T": range(5)
}
configs.append(
Configuration(
sim_config=sim_config,
state_dict=genesis_states,
seed=seed,
exogenous_states=exogenous_states,
env_processes=env_processes,
mechanisms=mechanisms
)
)

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@ -91,7 +91,7 @@ def env_b(x):
# return x + 1
# Genesis States
state_dict = {
genesis_states = {
's1': Decimal(0.0),
's2': Decimal(0.0),
's3': Decimal(1.0),
@ -167,7 +167,7 @@ sim_config = {
configs.append(
Configuration(
sim_config=sim_config,
state_dict=state_dict,
state_dict=genesis_states,
seed=seed,
exogenous_states=exogenous_states,
env_processes=env_processes,

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@ -93,7 +93,7 @@ def env_b(x):
# return x + 1
# Genesis States
state_dict = {
genesis_states = {
's1': Decimal(0.0),
's2': Decimal(0.0),
's3': Decimal(1.0),
@ -171,7 +171,7 @@ sim_config = {
configs.append(
Configuration(
sim_config=sim_config,
state_dict=state_dict,
state_dict=genesis_states,
seed=seed,
exogenous_states=exogenous_states,
env_processes=env_processes,