def test_plot_output_comparisons_waiting_class_1():
"""
Test that the values to be plotted by the function for the mean waiting time
of class 1 individuals are the expected when using:
- Markov formula and simulation state probabilities
- Markov formula and Markov state probabilities
- Simulation
"""
(
range_space,
simulation_times_using_markov_formula,
markov_times,
simulation_times,
) = plot_output_comparisons(
lambda_1=3,
lambda_2=4,
mu=1,
num_of_servers=3,
threshold=6,
system_capacity=15,
buffer_capacity=7,
seed_num=0,
num_of_trials=1,
runtime=100,
measure_to_compare="waiting",
class_type=0,
plot_over="mu",
max_parameter_value=5,
accuracy=5,
)
expected_range_space = [1, 2, 3, 4, 5]
expected_sim_times_using_formula = [
2.377120739790196,
0.7785480327193071,
0.21825612502962743,
0.0633853178321979,
0.02219807426322811,
]
expected_markov_times = [
2.666380625245361,
0.7505484517766888,
0.201787897652177,
0.06072282228882266,
0.024434222615639434,
]
expected_sim_times = [
[2.100498503091243],
[0.8060558886538617],
[0.24673859227916475],
[0.06673599211050996],
[0.026042424326131127],
]
assert np.all(range_space == expected_range_space)
assert np.allclose(
simulation_times_using_markov_formula, expected_sim_times_using_formula
)
assert np.allclose(markov_times, expected_markov_times)
assert np.allclose(simulation_times, expected_sim_times)
def test_plot_output_comparisons_invalid_measure():
"""
Test that an error is raised when an invalid measure is passed to the
function.
"""
with pytest.raises(ValueError):
plot_output_comparisons(
lambda_1=None,
lambda_2=0.1,
mu=None,
num_of_servers=None,
threshold=None,
system_capacity=None,
buffer_capacity=None,
seed_num=None,
num_of_trials=None,
runtime=None,
measure_to_compare="invalid_measure",
)
def test_plot_output_comparisons_blocking_class_1():
"""
Test to ensure an error comes up when trying to get the blocking times of
class 1 individuals
"""
with pytest.raises(Exception):
plot_output_comparisons(
lambda_1=1,
lambda_2=1,
mu=1,
num_of_servers=3,
threshold=5,
system_capacity=10,
buffer_capacity=7,
seed_num=0,
num_of_trials=1,
runtime=100,
measure_to_compare="blocking",
class_type=0,
plot_over="lambda_1",
max_parameter_value=3,
accuracy=5,
)
def test_plot_of_proportion_within_target_both_classes():
"""
Test the values to be plotted by the function for the mean proportion of
individuals for both classes are as expected for all methods used:
- Markov formula and simulation state probabilities
- Markov formula and Markov state probabilities
- Simulation
"""
(
range_space,
simulation_props_using_markov_formula,
markov_props,
simulation_props,
) = plot_output_comparisons(
lambda_1=1,
lambda_2=1,
mu=1,
num_of_servers=3,
threshold=2,
system_capacity=20,
buffer_capacity=10,
seed_num=1,
num_of_trials=2,
runtime=100,
target=4,
class_type=None,
measure_to_compare="proportion",
accuracy=5,
plot_over="threshold",
max_parameter_value=10,
)
expected_range_space = [
2,
4,
6,
8,
10,
]
expected_sim_props_using_formula = [
0.9803420072819845,
0.973534925815833,
0.965115375542696,
0.955970978810005,
0.947016739750006,
]
expected_markov_props = [
0.9793015428995077,
0.9737157940379565,
0.966029525931023,
0.959257362821785,
0.9542079250880933,
]
expected_sim_props = [
[0.9786096256684492, 0.9938650306748467],
[0.9622641509433962, 0.9777777777777777],
[0.9481132075471698, 0.9777777777777777],
[0.9292452830188679, 0.9777777777777777],
[0.910377358490566, 0.9777777777777777],
]
assert np.all(range_space == expected_range_space)
assert np.allclose(
simulation_props_using_markov_formula, expected_sim_props_using_formula
)
assert np.allclose(markov_props, expected_markov_props)
assert np.allclose(simulation_props, expected_sim_props)
def test_plot_of_proportion_within_target_class_2():
"""
Test the values to be plotted by the function for the mean proportion of
individuals for class 2 are as expected for all methods used:
- Markov formula and simulation state probabilities
- Markov formula and Markov state probabilities
- Simulation
"""
(
range_space,
simulation_props_using_markov_formula,
markov_props,
simulation_props,
) = plot_output_comparisons(
lambda_1=1,
lambda_2=1,
mu=1,
num_of_servers=3,
threshold=2,
system_capacity=20,
buffer_capacity=10,
seed_num=1,
num_of_trials=2,
runtime=100,
target=4,
class_type=1,
measure_to_compare="proportion",
accuracy=5,
plot_over="threshold",
max_parameter_value=10,
)
expected_range_space = [
2,
4,
6,
8,
10,
]
expected_sim_props_using_formula = [
0.9816843611112658,
0.9776764633348265,
0.9695135798097695,
0.9607212930115949,
0.9505136921747348,
]
expected_markov_props = [
0.9816843611112656,
0.9776309516976318,
0.9695851706481967,
0.96203774630283,
0.9559521606811459,
]
expected_sim_props = [
[1.0, 0.9880952380952381],
[0.978021978021978, 0.9770114942528736],
[0.967032967032967, 0.9655172413793104],
[0.9560439560439561, 0.9655172413793104],
[0.9230769230769231, 0.9655172413793104],
]
assert np.all(range_space == expected_range_space)
assert np.allclose(
simulation_props_using_markov_formula, expected_sim_props_using_formula
)
assert np.allclose(markov_props, expected_markov_props)
assert np.allclose(simulation_props, expected_sim_props)
def test_plot_of_proportion_within_target_class_1():
"""
Test the values to be plotted by the function for the mean proportion of
individuals for class 1 are as expected for all methods used:
- Markov formula and simulation state probabilities
- Markov formula and Markov state probabilities
- Simulation
"""
(
range_space,
simulation_props_using_markov_formula,
markov_props,
simulation_props,
) = plot_output_comparisons(
lambda_1=1,
lambda_2=1,
mu=1,
num_of_servers=3,
threshold=2,
system_capacity=20,
buffer_capacity=10,
seed_num=1,
num_of_trials=2,
runtime=100,
target=4,
class_type=0,
measure_to_compare="proportion",
accuracy=5,
plot_over="threshold",
max_parameter_value=10,
)
expected_range_space = [
2,
4,
6,
8,
10,
]
expected_sim_props_using_formula = [
0.9790136369646812,
0.9694014142792851,
0.9607171712756224,
0.9512206646084153,
0.9435197873252772,
]
expected_markov_props = [
0.9769758299950714,
0.9698065422230608,
0.9624762629273674,
0.9564778065163335,
0.9524639194726416,
]
expected_sim_props = [
[0.9615384615384616, 1.0],
[0.9504132231404959, 0.978494623655914],
[0.9338842975206612, 0.989247311827957],
[0.9090909090909091, 0.989247311827957],
[0.9008264462809917, 0.989247311827957],
]
assert np.all(range_space == expected_range_space)
assert np.allclose(
simulation_props_using_markov_formula, expected_sim_props_using_formula
)
assert np.allclose(markov_props, expected_markov_props)
assert np.allclose(simulation_props, expected_sim_props)
def test_plot_output_comparisons_blocking_property(
lambda_1, lambda_2, mu, num_of_servers, threshold
):
"""
Test that the values to be plotted by the function for the mean blocking time
of either CLASS 2 INDIVIDUALS or ALL INDIVIDUALS are the same for all methods
used:
- Markov formula and simulation state probabilities
- Markov formula and Markov state probabilities
- Simulation
These values are expected to be the same because class 1 individuals do not
have any blocking time, and thus the overall blocking time is calculated just
from class 2 individuals.
"""
(
range_space_1,
simulation_times_using_markov_formula_1,
markov_times_1,
simulation_times_1,
) = plot_output_comparisons(
lambda_1=lambda_1,
lambda_2=lambda_2,
mu=mu,
num_of_servers=num_of_servers,
threshold=threshold,
system_capacity=10,
buffer_capacity=5,
seed_num=0,
num_of_trials=1,
runtime=500,
measure_to_compare="blocking",
class_type=1,
plot_over="buffer_capacity",
max_parameter_value=5,
accuracy=None,
)
(
range_space_2,
simulation_times_using_markov_formula_2,
markov_times_2,
simulation_times_2,
) = plot_output_comparisons(
lambda_1=lambda_1,
lambda_2=lambda_2,
mu=mu,
num_of_servers=num_of_servers,
threshold=threshold,
system_capacity=10,
buffer_capacity=5,
seed_num=0,
num_of_trials=1,
runtime=500,
measure_to_compare="blocking",
class_type=None,
plot_over="buffer_capacity",
max_parameter_value=5,
accuracy=None,
)
assert np.all(range_space_1 == range_space_2)
assert np.all(
simulation_times_using_markov_formula_1
== simulation_times_using_markov_formula_2
)
assert np.all(markov_times_1 == markov_times_2)
assert np.all(simulation_times_1 == simulation_times_2)
def test_plot_output_comparisons_blocking_both_classes():
"""
Test that the values to be plotted by the function for the mean waiting time
of all individuals are the expected when using:
- Markov formula and simulation state probabilities
- Markov formula and Markov state probabilities
- Simulation
"""
(
range_space,
simulation_times_using_markov_formula,
markov_times,
simulation_times,
) = plot_output_comparisons(
lambda_1=1,
lambda_2=1,
mu=1,
num_of_servers=1,
threshold=5,
system_capacity=10,
buffer_capacity=7,
seed_num=0,
num_of_trials=1,
runtime=100,
measure_to_compare="blocking",
class_type=None,
plot_over="num_of_servers",
max_parameter_value=5,
accuracy=None,
)
expected_range_space = [
1,
2,
3,
4,
5,
]
expected_sim_times_using_formula = [
30.454703888754974,
0.8000539978455747,
0.09939633736936365,
0.08297030340373893,
0.06341488800287158,
]
expected_markov_times = [
40.065612220723104,
2.820781651110878,
0.25749828422874693,
0.05700263606859959,
0.024799827726554754,
]
expected_sim_times = [
[10.427934396602263],
[0.25420006034794723],
[0.05675700649642476],
[0.08092456927729426],
[0.08979883878110877],
]
assert np.all(range_space == expected_range_space)
assert np.allclose(
simulation_times_using_markov_formula, expected_sim_times_using_formula
)
assert np.allclose(markov_times, expected_markov_times)
assert np.allclose(simulation_times, expected_sim_times)
def test_plot_output_comparisons_blocking_class_2():
"""
Test that the values to be plotted by the function for the mean blocking time
of class 2 individuals are the expected when using:
- Markov formula and simulation state probabilities
- Markov formula and Markov state probabilities
- Simulation
"""
(
range_space,
simulation_times_using_markov_formula,
markov_times,
simulation_times,
) = plot_output_comparisons(
lambda_1=1,
lambda_2=1,
mu=1,
num_of_servers=3,
threshold=5,
system_capacity=10,
buffer_capacity=7,
seed_num=0,
num_of_trials=1,
runtime=100,
measure_to_compare="blocking",
class_type=1,
plot_over="lambda_2",
max_parameter_value=3,
accuracy=None,
)
expected_range_space = [
1,
1.5,
2,
2.5,
3,
]
expected_sim_times_using_formula = [
0.09939633736936365,
0.3428086786668058,
1.258688113496703,
1.5510437610794483,
2.4480598024079474,
]
expected_markov_times = [
0.25749828422874693,
0.7336269690016299,
1.4059020459868858,
2.0166211860863115,
2.446138025813656,
]
expected_sim_times = [
[0.05675700649642476],
[0.2035750550633296],
[1.0204972927807057],
[1.4297836865197424],
[2.276273474404749],
]
assert np.all(range_space == expected_range_space)
assert np.allclose(
simulation_times_using_markov_formula, expected_sim_times_using_formula
)
assert np.allclose(markov_times, expected_markov_times)
assert np.allclose(simulation_times, expected_sim_times)
def test_plot_output_comparisons_waiting_both_classes():
"""
Test that the values to be plotted by the function for the mean waiting time
of all individuals are the expected when using:
- Markov formula and simulation state probabilities
- Markov formula and Markov state probabilities
- Simulation
"""
(
range_space,
simulation_times_using_markov_formula,
markov_times,
simulation_times,
) = plot_output_comparisons(
lambda_1=3,
lambda_2=4,
mu=1,
num_of_servers=3,
threshold=5,
system_capacity=10,
buffer_capacity=7,
seed_num=0,
num_of_trials=1,
runtime=100,
measure_to_compare="waiting",
class_type=None,
plot_over="threshold",
max_parameter_value=9,
accuracy=5,
)
expected_range_space = [
5,
6,
7,
8,
9,
]
expected_sim_times_using_formula = (
[
1.4383683274990688,
1.6172139699602939,
1.7871674638990405,
1.902953640568348,
2.079546013987979,
],
)
expected_markov_times = [
1.4997317350805834,
1.6663508613218276,
1.8329697824825426,
1.999548467136932,
2.165791830248812,
]
expected_sim_times = [
[1.4595100304540891],
[1.5414680277219233],
[1.8463653589649593],
[1.9638358136060718],
[2.1872623359765617],
]
assert np.all(range_space == expected_range_space)
assert np.allclose(
simulation_times_using_markov_formula, expected_sim_times_using_formula
)
assert np.allclose(markov_times, expected_markov_times)
assert np.allclose(simulation_times, expected_sim_times)
def test_plot_output_comparisons_waiting_class_2():
"""
Test that the values to be plotted by the function for the mean waiting time
of class 2 individuals are the expected when using:
- Markov formula and simulation state probabilities
- Markov formula and Markov state probabilities
- Simulation
"""
(
range_space,
simulation_times_using_markov_formula,
markov_times,
simulation_times,
) = plot_output_comparisons(
lambda_1=3,
lambda_2=4,
mu=1,
num_of_servers=3,
threshold=6,
system_capacity=10,
buffer_capacity=7,
seed_num=0,
num_of_trials=1,
runtime=100,
measure_to_compare="waiting",
class_type=1,
plot_over="system_capacity",
max_parameter_value=18,
accuracy=5,
)
expected_range_space = [
10,
12,
14,
16,
18,
]
expected_sim_times_using_formula = [
0.9518119232230957,
0.9314674163209273,
0.8815151220881429,
0.9520317760341209,
0.9522967196743792,
]
expected_markov_times = [
0.9996062485853283,
0.9996071004169865,
0.9996071216135696,
0.9996071221161823,
0.9996071221275438,
]
expected_sim_times = [
[0.8587675978623437],
[0.9410302653948986],
[0.6712503805879015],
[0.7596612894701423],
[0.7466921877207321],
]
assert np.all(range_space == expected_range_space)
assert np.allclose(
simulation_times_using_markov_formula, expected_sim_times_using_formula
)
assert np.allclose(markov_times, expected_markov_times)
assert np.allclose(simulation_times, expected_sim_times)