def test_InferredInfectionProbability_prior_pdf_beta_degenerated_to_uniform_several_values(proba):
value = pd.DataFrame([{"Time": 0., "Value": proba}, {"Time": 1., "Value": 0.5}])
mean = pd.DataFrame([{"Time": 0., "Value": 0.5}, {"Time": 1., "Value": 0.5}])
ip1 = inf.InferredInfectionProbability(value, mean, 1.0, 1.0, np.random.default_rng(123))
assert ip1.prior_pdf() == 1.
value = pd.DataFrame([{"Time": 0., "Value": proba}, {"Time": 1., "Value": 0.5}])
mean = pd.DataFrame([{"Time": 0., "Value": 0.5}, {"Time": 1., "Value": 0.5}])
ip1 = inf.InferredInfectionProbability(value, mean, 1.0, 1.0, np.random.default_rng(123))
assert ip1.prior_pdf() == 1.
def test_InferredInfectionProbability_validate():
value = pd.DataFrame([{"Time": 0., "Value": 0.5}])
mean = pd.DataFrame([{"Time": 0., "Value": 1.}])
ip1 = inf.InferredInfectionProbability(value, mean, 2.0, 1.0, np.random.default_rng(123))
assert ip1.validate()
value = pd.DataFrame([{"Time": 0., "Value": -0.5}])
mean = pd.DataFrame([{"Time": 0., "Value": 1.}])
ip1 = inf.InferredInfectionProbability(value, mean, 2.0, 1.0, np.random.default_rng(123))
assert ~ip1.validate()
def test_InferredInfectionProbability():
ip = inf.InferredInfectionProbability(pd.DataFrame(), pd.DataFrame(), 2.0, 1.0, np.random.default_rng(123))
assert ip.value.empty
assert ip.mean.empty
assert ip.shape == 2.
assert ip.kernel_sigma == 1.
assert ip.rng.__eq__(np.random.default_rng(123))
def test_Particle_validate():
rng = np.random.default_rng(123)
value = pd.DataFrame([{"Time": 0., "Value": 0.5}])
mean = pd.DataFrame([{"Time": 0., "Value": 1.}])
good_infection_proba = inf.InferredInfectionProbability(value, mean, 2.0, 1.0, rng)
value = pd.DataFrame([{"Time": 0., "Value": -0.5}])
mean = pd.DataFrame([{"Time": 0., "Value": 1.}])
bad_infection_proba = inf.InferredInfectionProbability(value, mean, 2.0, 1.0, rng)
value = pd.DataFrame([{"Health_Board": "hb1", "Age": "[17,70)", "Infected": 0},
{"Health_Board": "hb2", "Age": "[17,70)", "Infected": 30}])
mean = pd.DataFrame([{"Health_Board": "hb1", "Age": "[17,70)", "Infected": 10},
{"Health_Board": "hb2", "Age": "[17,70)", "Infected": 50}])
good_initial_infections = inf.InferredInitialInfections(value, mean, 0.2, 10., 1., rng)
value = pd.DataFrame([{"Health_Board": "hb1", "Age": "[17,70)", "Infected": -10},
{"Health_Board": "hb2", "Age": "[17,70)", "Infected": 10}])
mean = pd.DataFrame([{"Health_Board": "hb1", "Age": "[17,70)", "Infected": 10},
{"Health_Board": "hb2", "Age": "[17,70)", "Infected": 50}])
bad_initial_infections = inf.InferredInitialInfections(value, mean, 0.2, 10., 1., rng)
value = pd.DataFrame([{"Date": "2020-01-10", "Movement_Multiplier": 1., "Contact_Multiplier": 4.},
{"Date": "2020-02-10", "Movement_Multiplier": 2., "Contact_Multiplier": 5.}])
mean = pd.DataFrame([{"Date": "2020-01-10", "Movement_Multiplier": 1., "Contact_Multiplier": 2.},
{"Date": "2020-02-10", "Movement_Multiplier": 2., "Contact_Multiplier": 4.}])
good_cm = inf.InferredContactMultipliers(value, mean, 0.2, 1., [dt.date.min, dt.date(2020, 2, 1), dt.date.max], rng)
value = pd.DataFrame([{"Date": "2020-01-10", "Movement_Multiplier": 1., "Contact_Multiplier": 0.},
{"Date": "2020-02-10", "Movement_Multiplier": 2., "Contact_Multiplier": 5.}])
mean = pd.DataFrame([{"Date": "2020-01-10", "Movement_Multiplier": 1., "Contact_Multiplier": 2.},
{"Date": "2020-02-10", "Movement_Multiplier": 2., "Contact_Multiplier": 4.}])
bad_cm = inf.InferredContactMultipliers(value, mean, 0.2, 1., [dt.date.min, dt.date(2020, 2, 1), dt.date.max], rng)
assert inf.Particle({
"infection-probability": good_infection_proba,
"initial-infections": good_initial_infections,
"contact-multipliers": good_cm
}).validate_particle()
assert ~inf.Particle({
"infection-probability": bad_infection_proba,
"initial-infections": bad_initial_infections,
"contact-multipliers": bad_cm
}).validate_particle()
def test_InferredInfectionProbability_perturbation_pdf_multiple_values_unit_noise():
value = pd.DataFrame([{"Time": 0., "Value": 0.05}, {"Time": 1., "Value": 0.05}])
mean = pd.DataFrame()
ip1 = inf.InferredInfectionProbability(value, mean, 4.0, 1.0, np.random.default_rng(123))
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 0.05}, {"Time": 1., "Value": 0.05}])) == 1.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 0.}, {"Time": 1., "Value": 0.}])) == 1.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 1.}, {"Time": 1., "Value": 1.}])) == 1.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": -0.05}, {"Time": 1., "Value": -0.05}])) == 0.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 1.05}, {"Time": 1., "Value": 1.05}])) == 0.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 2.05}, {"Time": 1., "Value": 2.05}])) == 0.
def test_InferredInfectionProbability_generate_perturbated():
value = pd.DataFrame([{"Time": 0., "Value": 0.5}])
mean = pd.DataFrame([{"Time": 0., "Value": 1.}])
ip1 = inf.InferredInfectionProbability(value, mean, 2.0, 1.0, np.random.default_rng(123))
ip2 = ip1.generate_perturbated()
pd.testing.assert_frame_equal(ip1.value, value)
pd.testing.assert_frame_equal(ip2.value, pd.DataFrame([{"Time": 0., "Value": 0.682351863248}]))
pd.testing.assert_frame_equal(ip1.mean, ip2.mean)
assert ip2.shape == ip1.shape
assert ip2.kernel_sigma == ip1.kernel_sigma
assert ip2.rng.__eq__(ip2.rng)
def test_InferredInfectionProbability_perturbation_pdf_small_noise():
value = pd.DataFrame([{"Time": 0., "Value": 0.05}])
mean = pd.DataFrame()
ip1 = inf.InferredInfectionProbability(value, mean, 4.0, 0.01, np.random.default_rng(123))
pytest.approx(ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 0.05}])), 50., 1e-8)
pytest.approx(ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 0.06}])), 50., 1e-8)
pytest.approx(ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 0.04}])), 50., 1e-8)
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 0.07}])) == 0.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 0.03}])) == 0.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 0.}])) == 0.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 1.}])) == 0.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": -0.05}])) == 0.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 1.05}])) == 0.
assert ip1.perturbation_pdf(pd.DataFrame([{"Time": 0., "Value": 2.05}])) == 0.
def test_Particle():
rng = np.random.default_rng(123)
infection_probability = inf.InferredInfectionProbability(pd.DataFrame(), pd.DataFrame(), 2.0, 1.0, rng)
initial_infections = inf.InferredInitialInfections(pd.DataFrame(), pd.DataFrame(), 0.2, 10., 1., rng)
contact_multipliers = inf.InferredContactMultipliers(pd.DataFrame(), pd.DataFrame(), 0.2, 1., [], rng)
particle = inf.Particle({
"infection-probability": infection_probability,
"initial-infections": initial_infections,
"contact-multipliers": contact_multipliers
})
assert len(particle.inferred_variables) == 3
assert list(particle.inferred_variables.keys()) == ["infection-probability", "initial-infections",
"contact-multipliers"]
inf.Particle({})
def test_InferredInfectionProbability_prior_pdf_beta_several_values():
value = pd.DataFrame([{"Time": 0., "Value": 0.05}, {"Time": 1., "Value": 0.05}])
mean = pd.DataFrame([{"Time": 0., "Value": 0.05}, {"Time": 1., "Value": 0.05}])
ip1 = inf.InferredInfectionProbability(value, mean, 4.0, 1.0, np.random.default_rng(123))
assert pytest.approx(ip1.prior_pdf(), 16.03449072358245**2, 1e-8)