Ciw Single Thread Performance

2023-12-07.py

from dataclasses import dataclass
import random

import ciw
from hciw.waitlist import create_existing_customers_from_list
from hciw.service_disciplines import lex_priority_benchmark_with_threshold_switch
import numpy as np
import pandas as pd


@dataclass(frozen=True, order=True)
class Patient:
    """Class for type of patient.

    This class is intended to be value object,
    meaning that its attributes exactly define
    its identity and behaviour.
    """

    priority: float
    benchmark: float
    internal_procedure_group: str

num_classes = 1000
customer_classes = {Patient(priority=random.randint(0,3), benchmark=i, internal_procedure_group=str(i)) for i in range(num_classes)}

num_nodes = 20

mean_arrival_rate = 100
mean_serve_rate = 10

arrival_dists = {
    customer_class: [
        ciw.dists.Exponential(rate=1 / mean_arrival_rate) for i in range(num_nodes)
    ]
    for customer_class in customer_classes
}
serve_dists = {
    customer_class: [
        ciw.dists.Exponential(rate=1 / mean_serve_rate) for i in range(num_nodes)
    ]
    for customer_class in customer_classes
}
routing_matrices = {
    customer_class: [
        np.random.dirichlet(np.ones(num_nodes + 1)).tolist()[:-1]
        for i in range(num_nodes)
    ]
    for customer_class in customer_classes
}
servers = [np.random.poisson(1) for i in range(num_nodes)]

N = ciw.create_network(
    arrival_distributions=arrival_dists,
    service_distributions=serve_dists,
    routing=routing_matrices,
    number_of_servers=servers,
    service_disciplines=[lex_priority_benchmark_with_threshold_switch] * num_nodes
)

Q = ciw.Simulation(N)

customer_classes = list(customer_classes)

backlog = [
    [
        i,
        random.choice(customer_classes),
        random.uniform(-i, 0),
        random.choice([1, 2, 3]),
    ]
    for i in range(300_000)
]

create_existing_customers_from_list(backlog, Q)

Q.simulate_until_max_time(365 * 6, progress_bar=True)

recs = pd.DataFrame(Q.get_all_records())