cleanup pt. 2: deleted more complicated messaging simulation
This commit is contained in:
Joshua E. Jodesty
parent
964c0b9123
commit
239cf94cae
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@ -1,165 +0,0 @@
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import random
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from copy import deepcopy
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from datetime import timedelta, datetime
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from cadCAD.configuration import append_configs
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from cadCAD.configuration.utils import bound_norm_random, config_sim, time_step, env_trigger
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from cadCAD.utils.sys_config import update_timestamp
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def choose_rnd(x: list, choices):
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def choose(x, choices):
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for n in list(range(choices)):
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elem = random.choice(x)
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x.remove(elem)
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yield elem
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copied_list = deepcopy(x)
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results = list(choose(copied_list, choices))
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del copied_list
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return results
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def message(sender, receiver, _input):
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return {'sender': sender, 'receiver': receiver, 'input': _input, 'sent_time': datetime.now()}
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def enter_room_msgs(room, users):
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return [message(user, room, f"{user} enters chat-room") for user in users]
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def exit_room_msgs(room, users):
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return [message(user, room, f"{user} exited chat-room") for user in users]
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rooms = ['room_1', 'room_2']
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user_group_1 = ['A', 'B', 'C', 'D']
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user_group_2 = ['E', 'F', 'G', 'H']
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room_1_messages = enter_room_msgs('room_1', random.shuffle(user_group_1))
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room_2_messages = enter_room_msgs('room_2', random.shuffle(user_group_2))
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def intitialize_conditions():
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users = user_group_1 + user_group_2
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messages = sorted(room_1_messages + room_2_messages, key=lambda i: i['time'])
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room_1_session = {'room': 'room_1', 'users': user_group_1, 'messages': room_1_messages}
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return {
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'client_a': room_1_session,
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'client_b': room_1_session,
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'server': {'rooms': rooms, 'users': users, 'messages': messages},
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'record_creation': datetime.now()
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}
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def send_message(room, sender, receiver, _input):
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return lambda _g, step, sL, s: {
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'types': ['send'],
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'events': [
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{
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'type': 'send',
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'room': room,
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'user': sender,
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'sent': [message(sender, receiver, _input)]
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}
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]
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}
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def exit_room(room, sender):
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return lambda _g, step, sL, s: {
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'types': ['exit'],
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'events': [
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{
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'type': 'exit',
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'room': room,
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'user': sender,
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'sent': exit_room_msgs(sender, room)
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}
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]
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}
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# Policies per Mechanism
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# ToDo Randomize client choices in runtime
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[alpha, omega] = choose_rnd(user_group_1, 2)
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a_msg1 = send_message('room_1', alpha, omega, f'Hello {omega}')
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b_msg1 = send_message('room_1', omega, alpha, f'Hello {alpha}')
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a_msg2 = send_message('room_1', alpha, omega, f'Bye {omega}')
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b_msg2 = send_message('room_1', omega, alpha, f'Bye {alpha}')
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a_msg3 = exit_room('room_1', alpha)
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b_msg3 = exit_room('room_1', omega)
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def remove_exited_users(users, actions):
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users = deepcopy(users)
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if 'exit' in actions['types']:
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for event in actions['events']:
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if event['type'] == 'exit':
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for user in event['user']:
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users.remove(user)
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return users
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# State Updates
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# {'room': 'room_1', 'users': user_group_1, 'messages': room_1_messages}
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def process_messages(_g, step, sL, s, actions):
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return 'client', {'room': s['room'], 'users': users, 'messages': actions['sent']}
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def process_exits(_g, step, sL, s, actions):
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users = remove_exited_users(s['users'], actions)
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return 'server', {'rooms': s['room'], 'users': users, 'messages': actions['sent']}
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update_record_creation = update_timestamp(
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'record_creation',
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timedelta(days=0, minutes=0, seconds=30),
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'%Y-%m-%d %H:%M:%S'
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)
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# partial_state_update_block = [
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# {
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# "policies": {
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# "b1": a_msg1,
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# "b2": b_msg1
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# },
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# "variables": {
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# "client_a": client_a_m1,
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# "client_b": client_b_m1,
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# "received": update_timestamp
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# }
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# },
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# {
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# "policies": {
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# "b1": a_msg2,
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# "b2": b_msg2
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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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# {
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# "policies": {
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# "b1": a_msg3,
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# "b2": b_msg3
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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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sim_config = config_sim(
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{
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"N": 1,
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"T": range(5),
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}
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)
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# append_configs(
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# user_id='user_a',
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# sim_configs=sim_config,
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# initial_state=genesis_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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# policy_ops=[lambda a, b: a + b]
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# )
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@ -1,31 +0,0 @@
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from pprint import pprint
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import pandas as pd
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from tabulate import tabulate
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from simulations.distributed.spark.session import spark_context as sc
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from simulations.regression_tests import config1, config2
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from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
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from cadCAD.utils import arrange_cols
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from cadCAD import configs
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exec_mode = ExecutionMode()
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print("Simulation Execution: Distributed Execution")
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dist_proc_ctx = ExecutionContext(context=exec_mode.dist_proc)
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run = Executor(exec_context=dist_proc_ctx, configs=configs, spark_context=sc)
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# pprint(dist_proc_ctx)
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# print(configs)
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i = 0
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config_names = ['config1', 'config2']
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for raw_result, tensor_field in run.execute():
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result = arrange_cols(pd.DataFrame(raw_result), False)
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print()
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# print(f"Tensor Field: {config_names[i]}")
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print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
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print("Output:")
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print(tabulate(result, headers='keys', tablefmt='psql'))
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print()
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i += 1
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@ -1,132 +0,0 @@
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from copy import deepcopy
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from datetime import timedelta, datetime
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import time
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from cadCAD.configuration import append_configs
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from cadCAD.configuration.utils import config_sim
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# session = enters('room_1', ['A', 'B'])
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intitial_conditions = {
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'record_creation': datetime.now(),
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'client_a': {'users': [], 'messages': [], 'avg_send_time': 1},
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'client_b': {'users': [], 'messages': [], 'avg_send_time': 1}
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}
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# Actions
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def message(client_id, room, action, _input, sender, receiver=None):
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# start_time = datetime.now()
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result = {
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'types': [action],
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'messages': [
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{
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'client': client_id, 'room': room, 'action': action,
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'sender': sender, 'receiver': receiver,
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'input': _input,
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'creatred': datetime.now()
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}
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]
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}
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# datetime.now() - start_time
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return result
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def enter_action(state, room, user):
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return lambda _g, step, sL, s: message(state, room, 'enter', f"{user} enters {room}", user)
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def message_action(state, room, _input, sender, receiver):
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return lambda _g, step, sL, s: message(state, room, 'send', _input, sender, receiver)
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def exit_action(state, room, user):
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return lambda _g, step, sL, s: message(state, room, 'exit', f"{user} exited {room}", user)
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# State Updates
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def update_users(users, actions, action_types=['send','enter','exit']):
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users = deepcopy(users)
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for action_type in action_types:
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if action_type in actions['types']:
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for msg in actions['messages']:
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if msg['action'] == 'send' and action_type == 'send':
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continue
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elif msg['action'] == 'enter' and action_type == 'enter':
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for user in msg['sender']:
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users.append(user) # register_entered
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elif msg['action'] == 'exit' and action_type == 'exit':
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for user in msg['sender']:
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users.remove(user) # remove_exited
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return users
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def send_message(state):
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return lambda _g, step, sL, s, actions: (
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state,
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{
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'users': update_users(s[state]['users'], actions),
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'messages': actions['messages'], 'avg_send_time': 1
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}
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)
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def current_time(state):
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return lambda _g, step, sL, s, actions: (state, datetime.now())
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sim_composition = [
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{
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"policies": {
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"b1": enter_action('server', 'room_1', 'A'),
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"b2": enter_action('server', 'room_1', 'B')
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},
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"variables": {
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'client_a': send_message('client_a'),
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'client_b': send_message('client_b'),
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'record_creation': current_time('record_creation')
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}
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},
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{
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"policies": {
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"b1": message_action('client_A', 'room_1', "Hi B", 'A', 'B'),
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"b2": message_action('client_B', 'room_1', "Hi A", 'B', 'A')
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},
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"variables": {
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'client_a': send_message('client_a'),
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'client_b': send_message('client_b'),
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'record_creation': current_time('record_creation')
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}
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},
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{
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"policies": {
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"b1": message_action('client_A', 'room_1', "Bye B", 'A', 'B'),
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"b2": message_action('client_B', 'room_1', "Bye A", 'B', 'A')
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},
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"variables": {
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'client_a': send_message('client_a'),
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'client_b': send_message('client_b'),
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'record_creation': current_time('record_creation')
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}
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},
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{
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"policies": {
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"b1": exit_action('server', 'room_1', 'A'),
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"b2": exit_action('server', 'room_1', 'B')
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},
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"variables": {
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'client_a': send_message('client_a'),
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'client_b': send_message('client_b'),
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'record_creation': current_time('record_creation')
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}
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}
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]
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# 5 = 10000 / (500 x 4)
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sim_config = config_sim(
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{
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"N": 5,
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"T": range(500),
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}
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)
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append_configs(
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user_id='user_a',
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sim_configs=sim_config,
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initial_state=intitial_conditions,
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partial_state_update_blocks=sim_composition,
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policy_ops=[lambda a, b: a + b]
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)
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from kafka import KafkaProducer
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from kafka import KafkaProducer
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from simulations.distributed.executor.spark.jobs import distributed_simulations
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from simulations.distributed.executor.spark.jobs import distributed_simulations
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from simulations.distributed.policies import enter_action, message_actions, exit_action
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from simulations.distributed.spark.session import sc
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from simulations.distributed.spark.session import sc
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from simulations.distributed.state_updates import send_message, count_messages, add_send_time, current_time
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def count(start, step):
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def count(start, step):
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@ -36,91 +38,7 @@ intitial_conditions = {
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'total_send_time': 0.000000
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'total_send_time': 0.000000
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}
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}
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# Actions
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def messages(client_id, room, action, _input, sender, receiver=None):
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return {
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'types': [action],
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'messages': [
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{
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'client': client_id, 'room': room, 'action': action,
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'sender': sender, 'receiver': receiver,
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'input': _input,
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'creatred': datetime.now()
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}
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]
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}
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def enter_action(state, room, user):
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def f(_g, step, sL, s, kafkaConfig):
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msgs = messages(state, room, 'enter', f"{user} enters {room}", user)
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msgs['send_times'] = [0.000000]
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msgs['msg_counts'] = [len(msgs['messages'])]
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return msgs
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return f
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def message_actions(state, room, _input, sender, receiver):
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msgs = messages(state, room, 'send', _input, sender, receiver)
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msgs_list = msgs['messages']
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def send_action(_g, step, sL, s, kafkaConfig):
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start_time = datetime.now()
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for msg in msgs_list:
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producer: KafkaProducer = kafkaConfig['producer']
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topic: str = kafkaConfig['send_topic']
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encoded_msg = str(msg).encode('utf-8')
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producer.send(topic, encoded_msg)
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msgs['send_times'] = [(datetime.now() - start_time).total_seconds()]
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msgs['msg_counts'] = [len(msgs_list)]
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return msgs
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return send_action
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def exit_action(state, room, user):
|
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||||||
def f(_g, step, sL, s, kafkaConfig):
|
|
||||||
msgs = messages(state, room, 'exit', f"{user} exited {room}", user)
|
|
||||||
msgs_list = msgs['messages']
|
|
||||||
msgs['send_times'] = [0.000000]
|
|
||||||
msgs['msg_counts'] = [len(msgs_list)]
|
|
||||||
return msgs
|
|
||||||
return f
|
|
||||||
|
|
||||||
# State Updates
|
# State Updates
|
||||||
def update_users(users, actions, action_types=['send','enter','exit']):
|
|
||||||
users = deepcopy(users)
|
|
||||||
for action_type in action_types:
|
|
||||||
if action_type in actions['types']:
|
|
||||||
for msg in actions['messages']:
|
|
||||||
if msg['action'] == 'send' and action_type == 'send':
|
|
||||||
continue
|
|
||||||
elif msg['action'] == 'enter' and action_type == 'enter':
|
|
||||||
for user in msg['sender']:
|
|
||||||
users.append(user) # register_entered
|
|
||||||
elif msg['action'] == 'exit' and action_type == 'exit':
|
|
||||||
for user in msg['sender']:
|
|
||||||
users.remove(user) # remove_exited
|
|
||||||
return users
|
|
||||||
|
|
||||||
add = lambda a, b: a + b
|
|
||||||
def count_messages(_g, step, sL, s, actions, kafkaConfig):
|
|
||||||
return 'total_msg_count', s['total_msg_count'] + reduce(add, actions['msg_counts'])
|
|
||||||
|
|
||||||
def add_send_time(_g, step, sL, s, actions, kafkaConfig):
|
|
||||||
return 'total_send_time', s['total_send_time'] + reduce(add, actions['send_times'])
|
|
||||||
|
|
||||||
def send_message(state):
|
|
||||||
return lambda _g, step, sL, s, actions, kafkaConfig: (
|
|
||||||
state,
|
|
||||||
{
|
|
||||||
'users': update_users(s[state]['users'], actions),
|
|
||||||
'messages': actions['messages'],
|
|
||||||
'msg_counts': reduce(add, actions['msg_counts']),
|
|
||||||
'send_times': reduce(add, actions['send_times'])
|
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
def current_time(state):
|
|
||||||
return lambda _g, step, sL, s, actions, kafkaConfig: (state, datetime.now())
|
|
||||||
|
|
||||||
sim_composition = [
|
sim_composition = [
|
||||||
{
|
{
|
||||||
|
|
|
||||||
|
|
@ -1,28 +0,0 @@
|
||||||
import pandas as pd
|
|
||||||
from tabulate import tabulate
|
|
||||||
|
|
||||||
from simulations.distributed.spark.session import spark_context as sc
|
|
||||||
from simulations.distributed import messaging
|
|
||||||
|
|
||||||
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
|
|
||||||
from cadCAD.utils import arrange_cols
|
|
||||||
from cadCAD import configs
|
|
||||||
|
|
||||||
exec_mode = ExecutionMode()
|
|
||||||
|
|
||||||
print("Simulation Execution: Distributed Execution")
|
|
||||||
dist_proc_ctx = ExecutionContext(context=exec_mode.dist_proc)
|
|
||||||
run = Executor(exec_context=dist_proc_ctx, configs=configs, spark_context=sc)
|
|
||||||
# pprint(dist_proc_ctx)
|
|
||||||
|
|
||||||
# print(configs)
|
|
||||||
i = 0
|
|
||||||
for raw_result, tensor_field in run.execute():
|
|
||||||
result = arrange_cols(pd.DataFrame(raw_result), False)
|
|
||||||
print()
|
|
||||||
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
|
|
||||||
print("Output:")
|
|
||||||
print(tabulate(result.head(1), headers='keys', tablefmt='psql'))
|
|
||||||
print(tabulate(result.tail(1), headers='keys', tablefmt='psql'))
|
|
||||||
print()
|
|
||||||
i += 1
|
|
||||||
|
|
@ -1,36 +0,0 @@
|
||||||
import pandas as pd
|
|
||||||
from tabulate import tabulate
|
|
||||||
# The following imports NEED to be in the exact order
|
|
||||||
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
|
|
||||||
from cadCAD.utils import arrange_cols
|
|
||||||
from simulations.regression_tests import config1, config2
|
|
||||||
from cadCAD import configs
|
|
||||||
|
|
||||||
from simulations.distributed.spark.session import spark
|
|
||||||
|
|
||||||
exec_mode = ExecutionMode()
|
|
||||||
|
|
||||||
print("Simulation Execution: Concurrent Execution")
|
|
||||||
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
|
|
||||||
run = Executor(exec_context=multi_proc_ctx, configs=configs)
|
|
||||||
|
|
||||||
# print(configs)
|
|
||||||
tf = None
|
|
||||||
i = 0
|
|
||||||
config_names = ['config1', 'config2']
|
|
||||||
for raw_result, tensor_field in run.execute():
|
|
||||||
result = arrange_cols(pd.DataFrame(raw_result), False)
|
|
||||||
print()
|
|
||||||
# print(f"Tensor Field: {config_names[i]}")
|
|
||||||
tf = tensor_field
|
|
||||||
# print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
|
|
||||||
print("Output:")
|
|
||||||
# print(tabulate(result, headers='keys', tablefmt='psql'))
|
|
||||||
print()
|
|
||||||
i += 1
|
|
||||||
|
|
||||||
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
|
|
||||||
|
|
||||||
df = spark.createDataFrame(tf)
|
|
||||||
|
|
||||||
df.show()
|
|
||||||
|
|
@ -0,0 +1,50 @@
|
||||||
|
from datetime import datetime
|
||||||
|
from kafka import KafkaProducer
|
||||||
|
|
||||||
|
# Actions
|
||||||
|
def messages(client_id, room, action, _input, sender, receiver=None):
|
||||||
|
return {
|
||||||
|
'types': [action],
|
||||||
|
'messages': [
|
||||||
|
{
|
||||||
|
'client': client_id, 'room': room, 'action': action,
|
||||||
|
'sender': sender, 'receiver': receiver,
|
||||||
|
'input': _input,
|
||||||
|
'creatred': datetime.now()
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def enter_action(state, room, user):
|
||||||
|
def f(_g, step, sL, s, kafkaConfig):
|
||||||
|
msgs = messages(state, room, 'enter', f"{user} enters {room}", user)
|
||||||
|
msgs['send_times'] = [0.000000]
|
||||||
|
msgs['msg_counts'] = [len(msgs['messages'])]
|
||||||
|
return msgs
|
||||||
|
return f
|
||||||
|
|
||||||
|
def message_actions(state, room, _input, sender, receiver):
|
||||||
|
msgs = messages(state, room, 'send', _input, sender, receiver)
|
||||||
|
msgs_list = msgs['messages']
|
||||||
|
def send_action(_g, step, sL, s, kafkaConfig):
|
||||||
|
start_time = datetime.now()
|
||||||
|
for msg in msgs_list:
|
||||||
|
producer: KafkaProducer = kafkaConfig['producer']
|
||||||
|
topic: str = kafkaConfig['send_topic']
|
||||||
|
encoded_msg = str(msg).encode('utf-8')
|
||||||
|
producer.send(topic, encoded_msg)
|
||||||
|
msgs['send_times'] = [(datetime.now() - start_time).total_seconds()]
|
||||||
|
msgs['msg_counts'] = [len(msgs_list)]
|
||||||
|
return msgs
|
||||||
|
|
||||||
|
return send_action
|
||||||
|
|
||||||
|
def exit_action(state, room, user):
|
||||||
|
def f(_g, step, sL, s, kafkaConfig):
|
||||||
|
msgs = messages(state, room, 'exit', f"{user} exited {room}", user)
|
||||||
|
msgs_list = msgs['messages']
|
||||||
|
msgs['send_times'] = [0.000000]
|
||||||
|
msgs['msg_counts'] = [len(msgs_list)]
|
||||||
|
return msgs
|
||||||
|
return f
|
||||||
|
|
@ -0,0 +1,40 @@
|
||||||
|
from copy import deepcopy
|
||||||
|
from datetime import datetime
|
||||||
|
from functools import reduce
|
||||||
|
|
||||||
|
# State Updates
|
||||||
|
def update_users(users, actions, action_types=['send','enter','exit']):
|
||||||
|
users = deepcopy(users)
|
||||||
|
for action_type in action_types:
|
||||||
|
if action_type in actions['types']:
|
||||||
|
for msg in actions['messages']:
|
||||||
|
if msg['action'] == 'send' and action_type == 'send':
|
||||||
|
continue
|
||||||
|
elif msg['action'] == 'enter' and action_type == 'enter':
|
||||||
|
for user in msg['sender']:
|
||||||
|
users.append(user) # register_entered
|
||||||
|
elif msg['action'] == 'exit' and action_type == 'exit':
|
||||||
|
for user in msg['sender']:
|
||||||
|
users.remove(user) # remove_exited
|
||||||
|
return users
|
||||||
|
|
||||||
|
add = lambda a, b: a + b
|
||||||
|
def count_messages(_g, step, sL, s, actions, kafkaConfig):
|
||||||
|
return 'total_msg_count', s['total_msg_count'] + reduce(add, actions['msg_counts'])
|
||||||
|
|
||||||
|
def add_send_time(_g, step, sL, s, actions, kafkaConfig):
|
||||||
|
return 'total_send_time', s['total_send_time'] + reduce(add, actions['send_times'])
|
||||||
|
|
||||||
|
def send_message(state):
|
||||||
|
return lambda _g, step, sL, s, actions, kafkaConfig: (
|
||||||
|
state,
|
||||||
|
{
|
||||||
|
'users': update_users(s[state]['users'], actions),
|
||||||
|
'messages': actions['messages'],
|
||||||
|
'msg_counts': reduce(add, actions['msg_counts']),
|
||||||
|
'send_times': reduce(add, actions['send_times'])
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
def current_time(state):
|
||||||
|
return lambda _g, step, sL, s, actions, kafkaConfig: (state, datetime.now())
|
||||||
Loading…
Reference in New Issue